Adjusting a trajectory of a mobile network node

Abstract

A network node (120a, 140a-c, 520, 540a-c, 1800) can determine (1510) information associated with a wireless network provided to one or more communication devices (110, 1700) by one or more mobile network nodes (140a-c, 540a-c). The network node can further determine (1520) a trajectory of a mobile network node of the one or more mobile network nodes. The network node can further determine (1530) adjustments to the trajectory of the mobile network node based on the information. The network node can further adjust (1540) the trajectory of the mobile network node.

Description

ADJUSTING A TRAJECTORY OF A MOBILE NETWORK NODETECHNICAL FIELD

[0001] The present disclosure is related to wireless communication systems and more particularly to adjusting a trajectory of a mobile network node.BACKGROUND

[0002] FIG. 1 illustrates an example of a new radio (“NR”) network (e.g., a 5th Generation (“5G”) network) including a 5G core (“5GC”) network 130, network nodes 120a- b (e.g., 5G base station (“gNB”)), multiple communication devices 110 (also referred to as user equipment (“UE”)).

[0003] Historically, edge computing has relied on the principle of (potentially) movable UEs accessing a network (e.g., the internet) via spatially fixed edge nodes (e.g., a base station). However, in some examples (e.g., a private 5G network), edge nodes can be mobile. In one example, edge nodes (sometimes referred to herein as network nodes) are included within unmanned aerial vehicles (“UAVs”). The UAVs can be moving around an environment while the edge nodes (moving along with the UAVs) are providing wireless access points (as well as other network functions) to UEs in the environment. In this example, two entities in the 5G network would be moving (e.g., both the UEs as well as the edge nodes are mobile).SUMMARY

[0004] According to some embodiments, a method of operating a network node (1800) is provided. The method includes determining information associated with a wireless network provided to one or more communication devices by one or more mobile network nodes. The method further includes determining a trajectory of a mobile network node of the one or more mobile network nodes. The method further includes determining adjustments to the trajectory of the mobile network node based on the information. The method further includes adjusting the trajectory of the mobile network node.

[0005] According to other embodiments, a network node, a mobile network node, a mobile network node manager, a host, a system, a computer program, a computer program product, or a non-transitory computer readable medium is provided to perform the above method.

[0006] The embodiments described herein can provide technical improvements. In some embodiments, controlling the trajectory of one or more mobile edge nodes (e.g., mobile network nodes) can allow for load balancing between different mobile edge nodes. In additional or alternative embodiments, controlling the trajectory of one or more mobile edgenodes can allow for adjustments to signal quality (e.g., reduction of noise or pathloss) based on a position of the mobile edge node relative to a UE.

[0007] In additional or alternative embodiments, the mobile edge nodes can be incorporated in a moving entity (e.g., a UAV). In some examples, this can be beneficial for private 5G networks which, unlike public 5G networks, are closed entities and can hence allow for such UAV-based mobility which can be controlled by a single organization such as a business enterprise. Moreover, this can be implemented for future UAV undisruptive communications. In some examples, simultaneous localization and mapping (“SLAM”)- based spiking neural networks (“SNN”) can be an efficient and reliable UE-UAV allocation.BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate certain non-limiting embodiments of inventive concepts. In the drawings:

[0009] FIG. 1 is a schematic diagram illustrating an example of a 5thgeneration (“5G”) network;

[0010] FIG. 2 is a schematic diagram illustrating an example of a 5G network with unmanned aerial vehicles (“UAVs”) that include mobile edge nodes in accordance with some embodiments;

[0011] FIG. 3 is a schematic diagram illustrating an example of the 5G network of FIG.2 with a load imbalance between UAVs in accordance with some embodiments;

[0012] FIG. 4 is a schematic diagram illustrating an example of an obstacle being between a mobile edge node and a UE in accordance with some embodiments;

[0013] FIG. 5 is a block diagram illustrating an example of an edge network that includes a mobile network node manager with a trajectory manager in accordance with some embodiments;

[0014] FIG. 6 is a block diagram illustrating an example of an edge network that includes a mobile network node with a trajectory manager in accordance with some embodiments;

[0015] FIGS. 7-12 are table illustrates examples of a simulation of a edge network that includes trajectory adjustments in accordance with some embodiments;

[0016] FIG. 13 is a flow chart illustrating an example of operations for adjusting a trajectory of a UAV-based edge node in accordance with some embodiments;

[0017] FIG. 14 is a signal flow chart illustrating an example of signals transmitted for adjusting a trajectory of a UAV-based edge node in accordance with some embodiments;

[0018] FIG. 15 is a flow chart illustrating an example of operations performed by a network node in accordance with some embodiments;

[0019] FIG. 16 is a block diagram of a communication system in accordance with some embodiments;

[0020] FIG. 17 is a block diagram of a user equipment in accordance with some embodiments;

[0021] FIG. 18 is a block diagram of a network node in accordance with some embodiments;

[0022] FIG. 19 is a block diagram of a host, which may be an embodiment of the host of FIG. 16, in accordance with some embodiments;

[0023] FIG. 20 is a block diagram of a virtualization environment in accordance with some embodiments; and

[0024] FIG. 21 shows a communication diagram of a host communicating via a network node with a user equipment over a partially wireless connection in accordance with some embodiments.DETAILED DESCRIPTION

[0025] Some of the embodiments contemplated herein will now be described more fully with reference to the accompanying drawings. Embodiments are provided by way of example to convey the scope of the subject matter to those skilled in the art, in which examples of embodiments of inventive concepts are shown. Inventive concepts may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of present inventive concepts to those skilled in the art. It should also be noted that these embodiments are not mutually exclusive. Components from one embodiment may be tacitly assumed to be present / used in another embodiment.

[0026] In a situation in which both UEs and edge nodes are mobile (e.g., moving or capable of moving), imbalances among the UEs and edge nodes can arise as they move around. In some examples, the imbalances refer to situations where a load (and / or potential load) on one edge node is different from a load on another edge node. In an extreme example, a single edge node may end up connected to all the UEs, while multiple edge nodes have no UEs connected to them.

[0027] Various embodiments herein describe adjusting a trajectory of one or more mobile edge nodes to address imbalance issues. In some examples, the edge nodes are includedwithin drones (e.g., unmanned aerial vehicles (“UAVs”), unmanned surface vehicles (“UAVs”), and / or unmanned ground vehicles (“UGVs”)) that are controlled by network operators.

[0028] FIGS. 2-3 illustrate examples of a 5G network that includes a set of UAVs 140a- c that serve as mobile edge nodes (e.g., each UAV 140a-c includes a network node). In this example, each of the UAVs 140a-c can wireless communicate with UEs 110 within their corresponding coverage areas. The UAVs 140a-c can also wireless communicate with a static network node 120a (e.g., a base station). Accordingly, the UAVs 140a-c can serve as access points for the UEs 110 to communicate with the network node 120a and a corresponding network (e.g., a private 5G network). In some examples, the UAVs 140a-c can directly communicate between each other.

[0029] The network in FIG. 2 may be considered balanced because each of the UAVs 140a-c include two UEs 110 in their coverage areas. The network in FIG. 3 may be considered imbalanced because each of the UAVs 140a-c include a different number of UEs 110 in their coverage areas.

[0030] In some examples, the network may be considered balanced / imbalanced based on an inequal amount of data being transmitted by the different UEs 110 and / or based on the different UEs 110 having different priorities.

[0031] In some embodiments, a trajectory of one or more mobile edge nodes is adjusted to address signal quality issues. FIG. 4 illustrates an example in which an obstacle 450 is located between a UAV 140a and a UE 110 and the obstacle 450. In some examples, the obstacle 450 causes noise and / or signal degradation in communication between the UAV 140a and the UE 110. In this example, the trajectory of the UAV 140a can be adjusted to improve signal quality between the UAV 140a and the UE 110 by causing the UAV 140a to move to a location in which the obstacle 450 will not interfere with the communications.

[0032] In additional or alternative embodiments, a trajectory of a mobile edge node is dynamically controlled based on a mobility of the UEs as the UEs move around. In some examples, the control includes limiting the trajectory of each mobile edge node based on the movement of other UAVs and the collective movement of the UEs. This can prevent any single edge node from being overburdened with more UEs than it can handle, without violating quality of service (“QoS”) requirements from the UEs.

[0033] In additional or alternative examples, dynamically controlling the mobile edge node includes increasing the number of mobile edge nodes that are needed in a particular edge zone in case the number of UEs that have assembled in the edge zone are too many for themobile edge nodes assigned to that area to manage. This can be performed without violating the UEs’ QoS requirements. However, this may require taking into account the QoS requirements of the UEs that are not in the edge zone.

[0034] In additional or alternative embodiments, trajectories of a set of mobile edge nodes cis optimized using a spiking neural network reinforced learning (“SNN-RL”) methodology by suitably awarding the reward mechanism.

[0035] FIGS. 5-6 illustrate two optional configurations for a mobile edge node network. In both FIG. 5 and FIG. 6, a mobile network node manager 520 communicates with each of the mobile network nodes 540a-c and each of the mobile network node 540a-c are communicatively coupled with some of the UEs 110. In FIG. 5, a trajectory manager 522 is included in the mobile network node manager 520. In FIG. 6, a trajectory manager 642 is included in one of the mobile network nodes 540a. The trajectory manager 522, 642 can receive information about the UEs 110 and / or the mobile network nodes 540a-c and determine adjustments to trajectories of the mobile network nodes 540a-c based on the information. In some examples, information about the UEs 110 includes a location of a UE 110, a mobility of a UE 110, a data rate of a UE 110, a measurement (e.g., reference signal received power (“RSRP”)) made by the UE 110, or a priority of the UE 110. In additional or alternative examples, information about the network nodes 540a-c includes a location of a mobile network node 540a-c, a mobility of a mobile network node 540a-c, a data rate of a mobile network node 540a-c, a measurement (e.g., reference signal received power (“RSRP”)) made by the mobile network node 540a-c, or a priority of the mobile network node 540a-c.

[0036] The mobile network nodes 540a-c can each be within a UAV. The mobile network node manger 520 can be a UAV manager that controls each of the UAVs. The trajectory manager 522 (within the UAV manager or within one or more of the UAVs) can request the UAV manager adjust trajectories of the UAVs (so as to adjust movement and location of the mobile network nodes 540a-c) and / or directly instruct the UAVs to adjust their trajectories.

[0037] In some embodiments, each edge node is modeled as a UAV, which contains an agent. The UAV can be connected to zero or more UEs. The agent can be connected to a trajectory manager in the UAV Manager. The task of the trajectory manager can be to determine the optimal trajectories of each UAV and, using the agent, to communicate them to each UAV. The agent can then be responsible for ensuring that the UAV moves only as per that trajectory. The agent can also be responsible for sending location information to the trajectory manager, so that the latter can track the positions and movements of all UAVs.

[0038] In additional or alternative embodiments, each UAV will have a global positioning system (“GPS”) module to detect and report its exact location. It will also have all the appropriate sensors needed for its operation (e.g., a gyroscope, a barometer, an accelerometer, and a rangefinder). Data from these sensors, transmitted via the UAV’s agent to the trajectory manager, can help the latter manage the UAV’ s movement as per the assigned trajectory.

[0039] One of the challenges in using UAVs to provide wireless communication is determining how to optimally allocate UEs to the UAVs to avoid the imbalance mentioned above. In some examples, spiking neural networks (“SNNs”) can be used to optimize this allocation process. Specifically, SNN-based procedures can be used to achieve efficient and reliable UE-UAV association. One example of an SNN-based approach is a simultaneous localization and mapping (“SLAM”)-based SNN, which combines SLAM techniques with SNNs to enable UAVs to localize themselves and allocate UEs in real-time. One of the primary advantages of this procedure is its ability to achieve real-time localization and mapping of the UAVs environment, enabling efficient allocation of UE to UAVs. SLAM- based SNN also use SNNs to optimize UE-UAV association, resulting in lower latency and higher throughput, leading to better performance overall. Additionally, this approach offers adaptability to changing environments, allowing for efficient allocation of UE and better utilization of the UAVs' resources. Overall, SLAM-based SNN can provide efficient and reliable UE-UAV allocation, making it a valuable tool for various applications such as disaster response and surveillance.

[0040] In addition to optimizing UE-UAV allocation, SNNs can also be used in combination with reinforcement learning (“RL”) to maximize data rate. This approach, known as SNN-RL, allows UAVs to learn and adapt their behavior over time based on feedback from their environment. By using SNN-RL to continuously optimize the UAVs' actions, higher data rates can be achieved while minimizing energy consumption and maximizing the UAVs' lifespan.

[0041] Cluster imbalance is a common problem in clustering algorithms, where some clusters may contain significantly more data points than others. Q-means constrained clustering and SNN-RL are two techniques that can effectively solve this issue. Q-means constrained clustering addresses cluster imbalance by constraining the maximum number of points in each cluster, ensuring a balanced allocation of data points across all clusters. On the other hand, SNN-RL uses reinforcement learning to optimize the clustering process, allowing for the creation of well-balanced and well-defined clusters. By using these techniques it ispossible to effectively solve the cluster imbalance problem and achieve more accurate clustering results in various applications, including data mining and pattern recognition.

[0042] SNNs can have several advantages over traditional Machine Learning (“ML”) and Deep Learning (“DL”) algorithms. One of the primary advantages of SNNs is their ability to operate in an event-driven manner, leading to significant time savings and reduced power consumption. Unlike traditional ML and DL algorithms that require large amounts of data to be processed in batches, SNNs process information only when there is a significant change in the data. This can allow for much faster processing and reduced power consumption, making SNNs an attractive option for various applications, such as real-time control and sensor networks. Furthermore, SNNs can also process data with higher accuracy and efficiency, making them an increasingly popular choice for implementing intelligent systems in various fields, including robotics and autonomous vehicles.

[0043] Conventional and constrained Q-means clustering techniques can be used to generate the initial clustering configuration for the SNN-RL model, providing a good starting point for the reinforcement learning algorithm. SNN-RL models can then use reinforcement learning to optimize the clustering process, leading to more accurate and efficient clustering results. We propose an SNN RL framework that allows the UAV to efficiently position in a 3D plane while efficiently serving the different users and helps in solving the optimization problem.

[0044] In some embodiments, a state S defines the UAV's position in relation to each user, their power coefficient, and the channel gain of the UAV-UEi.ST S^ SL . sJUE,T,hT]Here SjTincludes the step distance between the UAV and UEi in the x-axis and y-axis, power allocation coefficient, and channel gain. hTrefers to the current UAV height.

[0045] In additional or alternative embodiments, an action aTis defined aswhere 8ais defined asdj ranges from -1 to 1 and 8j is the magnitude of change in the power allocation coefficient of UEi. aTdetermines the adjustments of the UAV 3D placement and the power allocated to all users.

[0046] The reward at time step T is defined as,where gotdefines the total users channel gain, and I is the indicator function. wr, wf, wg, ws, and wuwith values greater than or equal to zero, are the weights corresponding to total rate, fairness, total channel gain, and satisfied and unsatisfied minimum rate requirements rewards respectively. The net rate reward term aims to increase the total sum rate after all users meet the minimum rate constraint. To reinforce the UAV to satisfy the minimum rate requirement, at any time step T, a reward of ws, which takes a relatively value, gets added to the total reward for every user achieves a rate that exceeds Rmjn.

[0047] The policy network weights and biases 9 are initialized randomly, and the target network parameters 9’ are cloned with 9. Both these networks have a first order LIF (leaky integrate- and-fire) layer that uses the snntorch leaky function. Input is assumed to be a current injection and membrane potential decays exponentially with rate beta. For,U[T] > Uthr-> S[T + 1] = 1If reset mechanism = “subtract”, then U[T + 1] will have threshold subtracted from it whenever the neuron emits a spike:U[t + 1] = pU[t] + Iin[t + 1] - RUthrIf reset mechanism = “zero”, then U[T + 1] will be set to 0 whenever the neuron emits a spike:U[t + 1] = pU[t] + lsyn[t + 1] - R(pU[t] + lin[t + 1])Here, ljnis the input current, U is the membrane potential, U^,. is the membrane threshold, R is the reset mechanism and p is the membrane potential decay rate.

[0048] The policy and target SNNs aid the RL framework to make reward-based decisions to maximize the data rate for every UE. The RL parameters are reset after a specific time to re-cluster the UEs by Q Means. The SNN RL model along with initial Q means is tested in different scenarios in the next section.

[0049] An example embodiment of UAV-based Mobility Management within a Private 5G Network is described below. In this example, there are six UEs and two UAVs. FIG. 7 illustrates six UE locations in an x-y plane at ground level within a restricted area of 100 x100. FIG. 8 illustrates the allocated cluster for each UE through conventional Q means which results in an imbalanced split of clusters.

[0050] FIG. 9A illustrates that the highest data rate achieved by a single UE is 4.5525 at episode 400. This UAV has four UEs allocated to it. FIG. 9B illustrates that the highest data rate achieved by a single UE is 9.045 at episode 400. This UAV has two UEs allocated to it. FIGS. 9A-B indicate the data rate imbalance across every UE under UAV-1 and UAV-2.

[0051] FIG. 10 illustrates that the allocated cluster for each UE through constrained Q means which results in a balanced cluster split.

[0052] FIG. 11 that the updated individual UE data rate which are obtained after passing the updated UE locations to the SNN - RL model.

[0053] FIG. 12 illustrates an example of how the SNN-RL model outperforms the conventional DQN-RL model. Hence such an algorithm can be used by the trajectory manager of FIGS. 5-6 for UAV trajectory optimization.

[0054] FIG. 13 illustrates an example of operations for adjusting a trajectory of a mobile network node.

[0055] Q-means is a clustering algorithm commonly used in wireless communication systems to allocate user equipment (UE) to unmanned aerial vehicle base stations (UAV BS). After the allocation, a spiking neural network (SNN) based reinforcement learning (RL) approach can be used to maximize the data rate of the system.

[0056] However, the initial allocation obtained by Q-means may result in cluster imbalance, which can negatively impact the performance of the SNN-RL approach. To address this issue, constrained Q-means can be used to ensure that each cluster has a balanced number of UEs.

[0057] The constrained Q-means algorithm can be used to allocate UEs to the UAV BSs while maintaining a balanced distribution of UEs among the clusters. This balanced allocation can improve the performance of the SNN-RL approach as each cluster will have a similar number of UEs to optimize.

[0058] The SNN-RL approach can then be used to dynamically adjust the transmission power and the UAV BS location to maximize the data rate of the system. The SNN-RL approach learns from the past experiences of the system to find the optimal action for a given state. The combined approach of Q-means, SNN-RL, and constrained Q-means can provide better results in terms of system efficiency and performance.

[0059] Overall, this approach can be a promising solution for future wireless communication systems, especially for UAV-based networks.

[0060] In the context of resource allocation for user equipment (UE) using unmanned aerial vehicles (UAVs), spiking neural networks (SNNs) have several advantages over deep Q-networks (DQNs) in reinforcement learning (RL) environments. SNNs can better model the spatiotemporal dynamics of the wireless channel, making them more suitable for wireless communication systems that involve fast changing and complex network conditions. Furthermore, SNNs can incorporate the timing information of spike -based communication, leading to more efficient and accurate decision-making in resource allocation. SNNs can also deal with high-dimensional inputs and outputs, which is common in wireless communication systems, without sacrificing performance. Overall, the use of SNNs in RL environments for U AV-based resource allocation can improve system performance, reduce latency, and increase the efficiency of the system.

[0061] The use of constrained Q-means clustering followed by a spiking neural networkbased reinforcement learning (SNN-RL) approach for allocating resources from unmanned aerial vehicles (UAVs) to user equipment (UE) can face several challenges. One such challenge is the increased computational complexity due to the use of constrained Q-means, which can lead to longer processing times and reduced efficiency. Additionally, the use of SNN-RL requires a large amount of training data, which can be difficult to obtain in dynamic and highly variable wireless communication environments. Another challenge is the potential for non-ideal clustering caused by the constrained Q-means, which can lead to a suboptimal allocation of resources to UEs. Finally, the mobility of UAVs and the unpredictability of the wireless channel can lead to rapid changes in network conditions, which may require frequent retraining of the SNN-RL model to maintain optimal performance. Addressing these challenges will be critical in the development of robust and efficient resource allocation systems for UAV-based wireless communication networks.

[0062] Hence it can be seen from the above description that approaches such as SNN can indeed be used to optimize the trajectory of UAVs so as to achieve balanced allocation of UEs to UAVs.

[0063] FIG. 14 illustrates an example of signals communicated within a network for adjusting a trajectory of a mobile network node. At block 1410, UE 110 transmits UE information to a Q-means module 1402. At block 1420, the Q-means module 1402 allocates the UE 110 (and / or other UEs) to the mobile network node 140a. At block 1430, the mobile network node 140a shares the UE allocation information with a SNN-RL module 1404. At block 1440, the SNN-RL module 1404 transmits an indication to the mobile network node 140a of an adjustment to the transmission power and location of the mobile network node140a. At block 1450, mobile network node 140a transmits broadcast configuration for allocated UEs to a balancer 1406. At block 1460, the balancer 1406 transmits UE information for constrained Q-means to the Q-means module 1402. At block 1470, the Q-means module 1402 transmits to the mobile network node 140a an indication of an adjusted UE allocation for balanced distribution. At block 1480, the mobile network node 140a transmits updated UE allocation information to the SNN-RL module 1404. At block 1490, the SNN-RL module 1404 transmits to the mobile network node 140a an indication of an adjustment to the transmission power and location of the of the mobile network node 140a to have a balanced allocation. At block 1495, the mobile network node 140a broadcasts a final optimized configuration to the UE 110.

[0064] In some embodiments, a process for adjusting the trajectory of mobile network nodes can be implemented and used within any distributed or centralized cloud system. Indeed, each of the components displayed in FIGS. 5-6 (e.g., Trajectory Manager 522, 642 and UAV Manager 520) can be modeled and deployed as microservices across a distributed cloud system.

[0065] The UAV Manager itself can be deployed on either a centralized cloud server, or a special stationary edge node. Also, the various components of the UAV Manager can be modelled as microservices themselves and can be deployed on the special stationery edge node or the centralized cloud server. This applies to the Trajectory Manager as well. Depending on the need, i.e., whether to minimize latency, network bandwidth, etc., the Trajectory Manager can be co-located with the UAV Manager, or as a separate microservice on the cloud.

[0066] Operations of the RAN node 1800 (implemented using the structure of FIG. 18) will now be discussed with reference to the flow chart of FIG. 15 according to some embodiments of inventive concepts. For example, modules may be stored in memory 1804 of FIG. 18, and these modules may provide instructions so that when the instructions of a module are executed by respective RAN node processing circuitry 1802, RAN node 1800 performs respective operations of the flow chart.

[0067] FIG. 15 illustrates an example of operations performed by a network node in accordance with some embodiments.

[0068] At block 1510, processing circuitry 1802 determines information associated with a wireless network provided to one or more communication devices by one more mobile network nodes. In some examples, the information associated with the wireless network includes an indication of a load on each of the one or more mobile network nodes. Inadditional or alternative examples, the information includes an indication of signal quality between the one or more communication devices and the one or more mobile network nodes.

[0069] In additional or alternative examples, the information associated with the wireless network includes an indication of at least one of: a location of the one or more mobile network nodes; a mobility of the one or more mobile network nodes; and a capacity of the one or more mobile network nodes.

[0070] In additional or alternative examples, the information includes an indication of at least one of: a location of the one or more communication devices; a mobility of the one or more communication devices; a data rate of the one or more communication devices; and a priority of the one or more communication devices.

[0071] At block 1520, processing circuitry 1802 determines a trajectory of a mobile network node. In some embodiments, the mobile network node is part of at least one of: an unmanned aerial vehicle (“UAV”); an unmanned surface vehicle (“USV”); and an unmanned ground vehicle (“UGV”).

[0072] At block 1530, processing circuitry 1802 determines adjustments to the trajectory of the mobile network node based on the information. In some examples, the adjustments to the trajectory of the mobile network node include an indication of a limitation of a movement of the mobile network node. In additional or alternative examples, the adjustments to the trajectory of the mobile network node include an indication of an explicit movement of the mobile network node.

[0073] In some embodiments, determining the adjustments to the trajectory of the mobile network node includes determining the adjustments to the trajectory of the mobile network node using a spiking neural network reinforced learning (“SNN-RL”) procedure.

[0074] In additional or alternative embodiments, determining the trajectory of the first mobile network node includes determining a trajectory of each of the one or more mobile network nodes. Determining the adjustments to the trajectory of the mobile network node includes determining adjustments to each trajectory of the one or more mobile network nodes to balance load between the one or more mobile network nodes.

[0075] At block 1540, processing circuitry 1802 adjusts the trajectory of the mobile network node. In some embodiments, the network node includes a mobile network node manager (e.g., mobile network manager 520 of FIG. 5). Adjusting the trajectory of the mobile network node can include transmitting, via communication interface 1806, an indication of the adjustments to the trajectory of the mobile network node to the mobile network node.

[0076] In additional or alternative embodiments, the mobile network node includes a first mobile network node (e.g., one of mobile network nodes 540b-c of FIG. 5). The network node comprises a second mobile network node (e.g., mobile network node 540a of FIG. 5) of the one or more mobile network nodes. Adjusting the trajectory of the mobile network node can include transmitting, via communication interface 1806, an indication of the adjustments to the trajectory of the mobile network node to the mobile network node or a mobile network node manager.

[0077] In additional or alternative embodiments, the mobile network node includes the mobile network node (e.g., mobile network node 540a of FIG. 5). Adjusting the trajectory of the mobile network node can include moving based on the adjustments to the trajectory.

[0078] Various operations from the flow chart of FIG. 15 may be optional with respect to some embodiments of communication devices and related methods.

[0079] FIG. 16 shows an example of a communication system 1600 in accordance with some embodiments.

[0080] In the example, the communication system 1600 includes a telecommunication network 1602 that includes an access network 1604, such as a radio access network (RAN), and a core network 1606, which includes one or more core network nodes 1608. The access network 1604 includes one or more access network nodes, such as network nodes 1610a and 1610b (one or more of which may be generally referred to as network nodes 1610), or any other similar 3rdGeneration Partnership Project (3GPP) access node or non-3GPP access point. The network nodes 1610 facilitate direct or indirect connection of user equipment (UE), such as by connecting UEs 1612a, 1612b, 1612c, and 1612d (one or more of which may be generally referred to as UEs 1612) to the core network 1606 over one or more wireless connections.

[0081] Example wireless communications over a wireless connection include transmitting and / or receiving wireless signals using electromagnetic waves, radio waves, infrared waves, and / or other types of signals suitable for conveying information without the use of wires, cables, or other material conductors. Moreover, in different embodiments, the communication system 1600 may include any number of wired or wireless networks, network nodes, UEs, and / or any other components or systems that may facilitate or participate in the communication of data and / or signals whether via wired or wireless connections. The communication system 1600 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0082] The UEs 1612 may be any of a wide variety of communication devices, including wireless devices arranged, configured, and / or operable to communicate wirelessly with the network nodes 1610 and other communication devices. Similarly, the network nodes 1610 are arranged, capable, configured, and / or operable to communicate directly or indirectly with the UEs 1612 and / or with other network nodes or equipment in the telecommunication network 1602 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunication network 1602.

[0083] In the depicted example, the core network 1606 connects the network nodes 1610 to one or more hosts, such as host 1616. These connections may be direct or indirect via one or more intermediary networks or devices. In other examples, network nodes may be directly coupled to hosts. The core network 1606 includes one more core network nodes (e.g., core network node 1608) that are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 1608. Example core network nodes include functions of one or more of a Mobile Switching Center (MSC), Mobility Management Entity (MME), Home Subscriber Server (HSS), Access and Mobility Management Function (AMF), Session Management Function (SMF), Authentication Server Function (AUSF), Subscription Identifier De-concealing function (SIDE), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0084] The host 1616 may be under the ownership or control of a service provider other than an operator or provider of the access network 1604 and / or the telecommunication network 1602, and may be operated by the service provider or on behalf of the service provider. The host 1616 may host a variety of applications to provide one or more service. Examples of such applications include live and pre-recorded audio / video content, data collection services such as retrieving and compiling data on various ambient conditions detected by a plurality of UEs, analytics functionality, social media, functions for controlling or otherwise interacting with remote devices, functions for an alarm and surveillance center, or any other such function performed by a server.

[0085] As a whole, the communication system 1600 of FIG. 16 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system may be configured to operate according to predefined rules or procedures, such as specific standards that include, but are not limited to: Global System for Mobile Communications (GSM);Universal Mobile Telecommunications System (UMTS); Long Term Evolution (LTE), and / or other suitable 2G, 3G, 4G, 5G standards, or any applicable future generation standard (e.g., 6G); wireless local area network (WLAN) standards, such as the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards (WiFi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (WiMax), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, LiFi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox.

[0086] In some examples, the telecommunication network 1602 is a cellular network that implements 3GPP standardized features. Accordingly, the telecommunications network 1602 may support network slicing to provide different logical networks to different devices that are connected to the telecommunication network 1602. For example, the telecommunications network 1602 may provide Ultra Reliable Low Latency Communication (URLLC) services to some UEs, while providing Enhanced Mobile Broadband (eMBB) services to other UEs, and / or Massive Machine Type Communication (mMTC) / Massive loT services to yet further UEs.

[0087] In some examples, the UEs 1612 are configured to transmit and / or receive information without direct human interaction. For instance, a UE may be designed to transmit information to the access network 1604 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 1604. Additionally, a UE may be configured for operating in single- or multi-RAT or multistandard mode. For example, a UE may operate with any one or combination of Wi-Fi, NR (New Radio) and LTE, i.e. being configured for multi-radio dual connectivity (MR-DC), such as E-UTRAN (Evolved-UMTS Terrestrial Radio Access Network) New Radio - Dual Connectivity (EN-DC).

[0088] In the example, the hub 1614 communicates with the access network 1604 to facilitate indirect communication between one or more UEs (e.g., UE 1612c and / or 1612d) and network nodes (e.g., network node 1610b). In some examples, the hub 1614 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 1614 may be a broadband router enabling access to the core network 1606 for the UEs. As another example, the hub 1614 may be a controller that sends commands or instructions to one or more actuators in the UEs. Commands or instructions may be received from the UEs, network nodes 1610, or by executable code, script, process, or other instructions in the hub 1614. As another example, the hub 1614 may be a data collector that acts as temporary storage for UE data and, in someembodiments, may perform analysis or other processing of the data. As another example, the hub 1614 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 1614 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 1614 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 1614 acts as a proxy server or orchestrator for the UEs, in particular in if one or more of the UEs are low energy loT devices.

[0089] The hub 1614 may have a constant / persistent or intermittent connection to the network node 1610b. The hub 1614 may also allow for a different communication scheme and / or schedule between the hub 1614 and UEs (e.g., UE 1612c and / or 1612d), and between the hub 1614 and the core network 1606. In other examples, the hub 1614 is connected to the core network 1606 and / or one or more UEs via a wired connection. Moreover, the hub 1614 may be configured to connect to an M2M service provider over the access network 1604 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 1610 while still connected via the hub 1614 via a wired or wireless connection. In some embodiments, the hub 1614 may be a dedicated hub - that is, a hub whose primary function is to route communications to / from the UEs from / to the network node 1610b. In other embodiments, the hub 1614 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 1610b, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0090] FIG. 17 shows a UE 1700 in accordance with some embodiments. As used herein, a UE refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other UEs. Examples of a UE include, but are not limited to, a smart phone, mobile phone, cell phone, voice over IP (VoIP) phone, wireless local loop phone, desktop computer, personal digital assistant (PDA), wireless cameras, gaming console or device, music storage device, playback appliance, wearable terminal device, wireless endpoint, mobile station, tablet, laptop, laptop -embedded equipment (LEE), laptop-mounted equipment (LME), smart device, wireless customer-premise equipment (CPE), vehicle-mounted or vehicle embedded / integrated wireless device, etc. Other examples include any UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-IoT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0091] A UE may support device-to-device (D2D) communication, for example by implementing a 3 GPP standard for sidelink communication, Dedicated Short-Range Communication (DSRC), vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), or vehicle-to-everything (V2X). In other examples, a UE may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, a UE may represent a device that is intended for sale to, or operation by, a human user but which may not, or which may not initially, be associated with a specific human user (e.g., a smart sprinkler controller). Alternatively, a UE may represent a device that is not intended for sale to, or operation by, an end user but which may be associated with or operated for the benefit of a user (e.g., a smart power meter).

[0092] The UE 1700 includes processing circuitry 1702 that is operatively coupled via a bus 1704 to an input / output interface 1706, a power source 1708, a memory 1710, a communication interface 1712, and / or any other component, or any combination thereof. Certain UEs may utilize all or a subset of the components shown in FIG. 17. The level of integration between the components may vary from one UE to another UE. Further, certain UEs may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0093] The processing circuitry 1702 is configured to process instructions and data and may be configured to implement any sequential state machine operative to execute instructions stored as machine-readable computer programs in the memory 1710. The processing circuitry 1702 may be implemented as one or more hardware-implemented state machines (e.g., in discrete logic, field-programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), etc.); programmable logic together with appropriate firmware; one or more stored computer programs, general-purpose processors, such as a microprocessor or digital signal processor (DSP), together with appropriate software; or any combination of the above. For example, the processing circuitry 1702 may include multiple central processing units (CPUs).

[0094] In the example, the input / output interface 1706 may be configured to provide an interface or interfaces to an input device, output device, or one or more input and / or output devices. Examples of an output device include a speaker, a sound card, a video card, a display, a monitor, a printer, an actuator, an emitter, a smartcard, another output device, or any combination thereof. An input device may allow a user to capture information into the UE 1700. Examples of an input device include a touch-sensitive or presence-sensitive display, a camera (e.g., a digital camera, a digital video camera, a web camera, etc.), a microphone, asensor, a mouse, a trackball, a directional pad, a trackpad, a scroll wheel, a smartcard, and the like. The presence-sensitive display may include a capacitive or resistive touch sensor to sense input from a user. A sensor may be, for instance, an accelerometer, a gyroscope, a tilt sensor, a force sensor, a magnetometer, an optical sensor, a proximity sensor, a biometric sensor, etc., or any combination thereof. An output device may use the same type of interface port as an input device. For example, a Universal Serial Bus (USB) port may be used to provide an input device and an output device.

[0095] In some embodiments, the power source 1708 is structured as a battery or battery pack. Other types of power sources, such as an external power source (e.g., an electricity outlet), photovoltaic device, or power cell, may be used. The power source 1708 may further include power circuitry for delivering power from the power source 1708 itself, and / or an external power source, to the various parts of the UE 1700 via input circuitry or an interface such as an electrical power cable. Delivering power may be, for example, for charging of the power source 1708. Power circuitry may perform any formatting, converting, or other modification to the power from the power source 1708 to make the power suitable for the respective components of the UE 1700 to which power is supplied.

[0096] The memory 1710 may be or be configured to include memory such as random access memory (RAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, hard disks, removable cartridges, flash drives, and so forth. In one example, the memory 1710 includes one or more application programs 1714, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 1716. The memory 1710 may store, for use by the UE 1700, any of a variety of various operating systems or combinations of operating systems.

[0097] The memory 1710 may be configured to include a number of physical drive units, such as redundant array of independent disks (RAID), flash memory, USB flash drive, external hard disk drive, thumb drive, pen drive, key drive, high-density digital versatile disc (HD-DVD) optical disc drive, internal hard disk drive, Blu-Ray optical disc drive, holographic digital data storage (HDDS) optical disc drive, external mini-dual in-line memory module (DIMM), synchronous dynamic random access memory (SDRAM), external micro-DIMM SDRAM, smartcard memory such as tamper resistant module in the form of a universal integrated circuit card (UICC) including one or more subscriber identity modules (SIMs), such as a USIM and / or ISIM, other memory, or any combination thereof. The UICC may forexample be an embedded UICC (eUICC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 1710 may allow the UE 1700 to access instructions, application programs and the like, stored on transitory or non-transitory memory media, to off-load data, or to upload data. An article of manufacture, such as one utilizing a communication system may be tangibly embodied as or in the memory 1710, which may be or comprise a device-readable storage medium.

[0098] The processing circuitry 1702 may be configured to communicate with an access network or other network using the communication interface 1712. The communication interface 1712 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 1722. The communication interface 1712 may include one or more transceivers used to communicate, such as by communicating with one or more remote transceivers of another device capable of wireless communication (e.g., another UE or a network node in an access network). Each transceiver may include a transmitter 1718 and / or a receiver 1720 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 1718 and receiver 1720 may be coupled to one or more antennas (e.g., antenna 1722) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0099] In the illustrated embodiment, communication functions of the communication interface 1712 may include cellular communication, Wi-Fi communication, LPWAN communication, data communication, voice communication, multimedia communication, short-range communications such as Bluetooth, near-field communication, location-based communication such as the use of the global positioning system (GPS) to determine a location, another like communication function, or any combination thereof. Communications may be implemented in according to one or more communication protocols and / or standards, such as IEEE 802.11, Code Division Multiplexing Access (CDMA), Wideband Code Division Multiple Access (WCDMA), GSM, LTE, New Radio (NR), UMTS, WiMax, Ethernet, transmission control protocol / intemet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0100] Regardless of the type of sensor, a UE may provide an output of data captured by its sensors, through its communication interface 1712, via a wireless connection to a network node. Data captured by sensors of a UE can be communicated through a wireless connection to a network node via another UE. The output may be periodic (e.g., once every 15 minutes if it reports the sensed temperature), random (e.g., to even out the load from reporting fromseveral sensors), in response to a triggering event (e.g., when moisture is detected an alert is sent), in response to a request (e.g., a user initiated request), or a continuous stream (e.g., a live video feed of a patient).

[0101] As another example, a UE comprises an actuator, a motor, or a switch, related to a communication interface configured to receive wireless input from a network node via a wireless connection. In response to the received wireless input the states of the actuator, the motor, or the switch may change. For example, the UE may comprise a motor that adjusts the control surfaces or rotors of a drone in flight according to the received input or to a robotic arm performing a medical procedure according to the received input.

[0102] A UE, when in the form of an Internet of Things (loT) device, may be a device for use in one or more application domains, these domains comprising, but not limited to, city wearable technology, extended industrial application and healthcare. Non-limiting examples of such an loT device are a device which is or which is embedded in: a connected refrigerator or freezer, a TV, a connected lighting device, an electricity meter, a robot vacuum cleaner, a voice controlled smart speaker, a home security camera, a motion detector, a thermostat, a smoke detector, a door / window sensor, a flood / moisture sensor, an electrical door lock, a connected doorbell, an air conditioning system like a heat pump, an autonomous vehicle, a surveillance system, a weather monitoring device, a vehicle parking monitoring device, an electric vehicle charging station, a smart watch, a fitness tracker, a head-mounted display for Augmented Reality (AR) or Virtual Reality (VR), a wearable for tactile augmentation or sensory enhancement, a water sprinkler, an animal- or item-tracking device, a sensor for monitoring a plant or animal, an industrial robot, an Unmanned Aerial Vehicle (UAV), and any kind of medical device, like a heart rate monitor or a remote controlled surgical robot. A UE in the form of an loT device comprises circuitry and / or software in dependence of the intended application of the loT device in addition to other components as described in relation to the UE 1700 shown in FIG. 17.

[0103] As yet another specific example, in an loT scenario, a UE may represent a machine or other device that performs monitoring and / or measurements, and transmits the results of such monitoring and / or measurements to another UE and / or a network node. The UE may in this case be an M2M device, which may in a 3 GPP context be referred to as an MTC device. As one particular example, the UE may implement the 3GPP NB-IoT standard. In other scenarios, a UE may represent a vehicle, such as a car, a bus, a truck, a ship and an airplane, or other equipment that is capable of monitoring and / or reporting on its operational status or other functions associated with its operation.

[0104] In practice, any number of UEs may be used together with respect to a single use case. For example, a first UE might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second UE that is a remote controller operating the drone. When the user makes changes from the remote controller, the first UE may adjust the throttle on the drone (e.g. by controlling an actuator) to increase or decrease the drone’s speed. The first and / or the second UE can also include more than one of the functionalities described above. For example, a UE might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0105] FIG. 18 shows a network node 1800 in accordance with some embodiments. As used herein, network node refers to equipment capable, configured, arranged and / or operable to communicate directly or indirectly with a UE and / or with other network nodes or equipment, in a telecommunication network. Examples of network nodes include, but are not limited to, access points (APs) (e.g., radio access points), base stations (BSs) (e.g., radio base stations, Node Bs, evolved Node Bs (eNBs) and NR NodeBs (gNBs)).

[0106] Base stations may be categorized based on the amount of coverage they provide (or, stated differently, their transmit power level) and so, depending on the provided amount of coverage, may be referred to as femto base stations, pico base stations, micro base stations, or macro base stations. A base station may be a relay node or a relay donor node controlling a relay. A network node may also include one or more (or all) parts of a distributed radio base station such as centralized digital units and / or remote radio units (RRUs), sometimes referred to as Remote Radio Heads (RRHs). Such remote radio units may or may not be integrated with an antenna as an antenna integrated radio. Parts of a distributed radio base station may also be referred to as nodes in a distributed antenna system (DAS).

[0107] Other examples of network nodes include multiple transmission point (multi- TRP) 5G access nodes, multi-standard radio (MSR) equipment such as MSR BSs, network controllers such as radio network controllers (RNCs) or base station controllers (BSCs), base transceiver stations (BTSs), transmission points, transmission nodes, multi-cell / multicast coordination entities (MCEs), Operation and Maintenance (O&M) nodes, Operations Support System (OSS) nodes, Self-Organizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0108] The network node 1800 includes a processing circuitry 1802, a memory 1804, a communication interface 1806, and a power source 1808. The network node 1800 may be composed of multiple physically separate components (e.g., a NodeB component and a RNCcomponent, or a BTS component and a BSC component, etc.), which may each have their own respective components. In certain scenarios in which the network node 1800 comprises multiple separate components (e.g., BTS and BSC components), one or more of the separate components may be shared among several network nodes. For example, a single RNC may control multiple NodeBs. In such a scenario, each unique NodeB and RNC pair, may in some instances be considered a single separate network node. In some embodiments, the network node 1800 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memory 1804 for different RATs) and some components may be reused (e.g., a same antenna 1810 may be shared by different RATs). The network node 1800 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1800, for example GSM, WCDMA, LTE, NR, WiFi, Zigbee, Z-wave, LoRaWAN, Radio Frequency Identification (RFID) or Bluetooth wireless technologies. These wireless technologies may be integrated into the same or different chip or set of chips and other components within network node 1800.

[0109] The processing circuitry 1802 may comprise a combination of one or more of a microprocessor, controller, microcontroller, central processing unit, digital signal processor, application- specific integrated circuit, field programmable gate array, or any other suitable computing device, resource, or combination of hardware, software and / or encoded logic operable to provide, either alone or in conjunction with other network node 1800 components, such as the memory 1804, to provide network node 1800 functionality.

[0110] In some embodiments, the processing circuitry 1802 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1802 includes one or more of radio frequency (RF) transceiver circuitry 1812 and baseband processing circuitry 1814. In some embodiments, the radio frequency (RF) transceiver circuitry 1812 and the baseband processing circuitry 1814 may be on separate chips (or sets of chips), boards, or units, such as radio units and digital units. In alternative embodiments, part or all of RF transceiver circuitry 1812 and baseband processing circuitry 1814 may be on the same chip or set of chips, boards, or units.

[0111] The memory 1804 may comprise any form of volatile or non-volatile computer- readable memory including, without limitation, persistent storage, solid-state memory, remotely mounted memory, magnetic media, optical media, random access memory (RAM), read-only memory (ROM), mass storage media (for example, a hard disk), removable storage media (for example, a flash drive, a Compact Disk (CD) or a Digital Video Disk (DVD)),and / or any other volatile or non-volatile, non-transitory device-readable and / or computerexecutable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1802. The memory 1804 may store any suitable instructions, data, or information, including a computer program, software, an application including one or more of logic, rules, code, tables, and / or other instructions capable of being executed by the processing circuitry 1802 and utilized by the network node 1800. The memory 1804 may be used to store any calculations made by the processing circuitry 1802 and / or any data received via the communication interface 1806. In some embodiments, the processing circuitry 1802 and memory 1804 is integrated.

[0112] The communication interface 1806 is used in wired or wireless communication of signaling and / or data between a network node, access network, and / or UE. As illustrated, the communication interface 1806 comprises port(s) / terminal(s) 1816 to send and receive data, for example to and from a network over a wired connection. The communication interface 1806 also includes radio front-end circuitry 1818 that may be coupled to, or in certain embodiments a part of, the antenna 1810. Radio front-end circuitry 1818 comprises filters 1820 and amplifiers 1822. The radio front-end circuitry 1818 may be connected to an antenna 1810 and processing circuitry 1802. The radio front-end circuitry may be configured to condition signals communicated between antenna 1810 and processing circuitry 1802. The radio front-end circuitry 1818 may receive digital data that is to be sent out to other network nodes or UEs via a wireless connection. The radio front-end circuitry 1818 may convert the digital data into a radio signal having the appropriate channel and bandwidth parameters using a combination of filters 1820 and / or amplifiers 1822. The radio signal may then be transmitted via the antenna 1810. Similarly, when receiving data, the antenna 1810 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1818. The digital data may be passed to the processing circuitry 1802. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0113] In certain alternative embodiments, the network node 1800 does not include separate radio front-end circuitry 1818, instead, the processing circuitry 1802 includes radio front-end circuitry and is connected to the antenna 1810. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1812 is part of the communication interface 1806. In still other embodiments, the communication interface 1806 includes one or more ports or terminals 1816, the radio front-end circuitry 1818, and the RF transceiver circuitry 1812, aspart of a radio unit (not shown), and the communication interface 1806 communicates with the baseband processing circuitry 1814, which is part of a digital unit (not shown).

[0114] The antenna 1810 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1810 may be coupled to the radio front-end circuitry 1818 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1810 is separate from the network node 1800 and connectable to the network node 1800 through an interface or port.

[0115] The antenna 1810, communication interface 1806, and / or the processing circuitry1802 may be configured to perform any receiving operations and / or certain obtaining operations described herein as being performed by the network node. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 1810, the communication interface 1806, and / or the processing circuitry 1802 may be configured to perform any transmitting operations described herein as being performed by the network node. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0116] The power source 1808 provides power to the various components of network node 1800 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1808 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1800 with power for performing the functionality described herein. For example, the network node 1800 may be connectable to an external power source (e.g., the power grid, an electricity outlet) via an input circuitry or interface such as an electrical cable, whereby the external power source supplies power to power circuitry of the power source 1808. As a further example, the power source 1808 may comprise a source of power in the form of a battery or battery pack which is connected to, or integrated in, power circuitry. The battery may provide backup power should the external power source fail.

[0117] Embodiments of the network node 1800 may include additional components beyond those shown in FIG. 18 for providing certain aspects of the network node’s functionality, including any of the functionality described herein and / or any functionality necessary to support the subject matter described herein. For example, the network node 1800 may include user interface equipment to allow input of information into the network node 1800 and to allow output of information from the network node 1800. This may allow a userto perform diagnostic, maintenance, repair, and other administrative functions for the network node 1800.

[0118] FIG. 19 is a block diagram of a host 1900, which may be an embodiment of the host 1616 of FIG. 16, in accordance with various aspects described herein. As used herein, the host 1900 may be or comprise various combinations hardware and / or software, including a standalone server, a blade server, a cloud-implemented server, a distributed server, a virtual machine, container, or processing resources in a server farm. The host 1900 may provide one or more services to one or more UEs.

[0119] The host 1900 includes processing circuitry 1902 that is operatively coupled via a bus 1904 to an input / output interface 1906, a network interface 1908, a power source 1910, and a memory 1912. Other components may be included in other embodiments. Features of these components may be substantially similar to those described with respect to the devices of previous figures, such as FIGS. 17 and 18, such that the descriptions thereof are generally applicable to the corresponding components of host 1900.

[0120] The memory 1912 may include one or more computer programs including one or more host application programs 1914 and data 1916, which may include user data, e.g., data generated by a UE for the host 1900 or data generated by the host 1900 for a UE. Embodiments of the host 1900 may utilize only a subset or all of the components shown. The host application programs 1914 may be implemented in a container-based architecture and may provide support for video codecs (e.g., Versatile Video Coding (VVC), High Efficiency Video Coding (HEVC), Advanced Video Coding (AVC), MPEG, VP9) and audio codecs (e.g., FLAC, Advanced Audio Coding (AAC), MPEG, G.711), including transcoding for multiple different classes, types, or implementations of UEs (e.g., handsets, desktop computers, wearable display systems, heads-up display systems). The host application programs 1914 may also provide for user authentication and licensing checks and may periodically report health, routes, and content availability to a central node, such as a device in or on the edge of a core network. Accordingly, the host 1900 may select and / or indicate a different host for over-the- top (OTT) services for a UE. The host application programs 1914 may support various protocols, such as the HTTP Live Streaming (HLS) protocol, Real-Time Messaging Protocol (RTMP), Real-Time Streaming Protocol (RTSP), Dynamic Adaptive Streaming over HTTP (MPEG-DASH), etc.

[0121] FIG. 20 is a block diagram illustrating a virtualization environment 2000 in which functions implemented by some embodiments may be virtualized. In the present context, virtualizing means creating virtual versions of apparatuses or devices which may includevirtualizing hardware platforms, storage devices and networking resources. As used herein, virtualization can be applied to any device described herein, or components thereof, and relates to an implementation in which at least a portion of the functionality is implemented as one or more virtual components. Some or all of the functions described herein may be implemented as virtual components executed by one or more virtual machines (VMs) implemented in one or more virtual environments 2000 hosted by one or more of hardware nodes, such as a hardware computing device that operates as a network node, UE, core network node, or host. Further, in embodiments in which the virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized.

[0122] Applications 2002 (which may alternatively be called software instances, virtual appliances, network functions, virtual nodes, virtual network functions, etc.) are run in the virtualization environment Q400 to implement some of the features, functions, and / or benefits of some of the embodiments disclosed herein.

[0123] Hardware 2004 includes processing circuitry, memory that stores software and / or instructions executable by hardware processing circuitry, and / or other hardware devices as described herein, such as a network interface, input / output interface, and so forth. Software may be executed by the processing circuitry to instantiate one or more virtualization layers 2006 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VMs 2008a and 2008b (one or more of which may be generally referred to as VMs 2008), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 2006 may present a virtual operating platform that appears like networking hardware to the VMs 2008.

[0124] The VMs 2008 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by a corresponding virtualization layer 2006. Different embodiments of the instance of a virtual appliance 2002 may be implemented on one or more of VMs 2008, and the implementations may be made in different ways. Virtualization of the hardware is in some contexts referred to as network function virtualization (NFV). NFV may be used to consolidate many network equipment types onto industry standard high volume server hardware, physical switches, and physical storage, which can be located in data centers, and customer premise equipment.

[0125] In the context of NFV, a VM 2008 may be a software implementation of aphysical machine that runs programs as if they were executing on a physical, non-virtualized machine. Each of the VMs 2008, and that part of hardware 2004 that executes that VM, be it hardwarededicated to that VM and / or hardware shared by that VM with others of the VMs, forms separate virtual network elements. Still in the context of NFV, a virtual network function is responsible for handling specific network functions that run in one or more VMs 2008 on top of the hardware 2004 and corresponds to the application 2002.

[0126] Hardware 2004 may be implemented in a standalone network node with generic or specific components. Hardware 2004 may implement some functions via virtualization. Alternatively, hardware 2004 may be part of a larger cluster of hardware (e.g. such as in a data center or CPE) where many hardware nodes work together and are managed via management and orchestration 2010, which, among others, oversees lifecycle management of applications 2002. In some embodiments, hardware 2004 is coupled to one or more radio units that each include one or more transmitters and one or more receivers that may be coupled to one or more antennas. Radio units may communicate directly with other hardware nodes via one or more appropriate network interfaces and may be used in combination with the virtual components to provide a virtual node with radio capabilities, such as a radio access node or a base station. In some embodiments, some signaling can be provided with the use of a control system 2012 which may alternatively be used for communication between hardware nodes and radio units.

[0127] FIG. 21 shows a communication diagram of a host 2102 communicating via a network node 2104 with a UE 2106 over a partially wireless connection in accordance with some embodiments. Example implementations, in accordance with various embodiments, of the UE (such as a UE 1612a of FIG. 16 and / or UE 1700 of FIG. 17), network node (such as network node 1610a of FIG. 16 and / or network node 1800 of FIG. 18), and host (such as host 1616 of FIG. 16 and / or host 1900 of FIG. 19) discussed in the preceding paragraphs will now be described with reference to FIG. 21.

[0128] Eike host 1900, embodiments of host 2102 include hardware, such as a communication interface, processing circuitry, and memory. The host 2102 also includes software, which is stored in or accessible by the host 2102 and executable by the processing circuitry. The software includes a host application that may be operable to provide a service to a remote user, such as the UE 2106 connecting via an over-the-top (OTT) connection 2150 extending between the UE 2106 and host 2102. In providing the service to the remote user, a host application may provide user data which is transmitted using the OTT connection 2150.

[0129] The network node 2104 includes hardware enabling it to communicate with the host 2102 and UE 2106. The connection 2160 may be direct or pass through a core network (like core network 1606 of FIG. 16) and / or one or more other intermediate networks, such asone or more public, private, or hosted networks. For example, an intermediate network may be a backbone network or the Internet.

[0130] The UE 2106 includes hardware and software, which is stored in or accessible by UE 2106 and executable by the UE’s processing circuitry. The software includes a client application, such as a web browser or operator-specific “app” that may be operable to provide a service to a human or non-human user via UE 2106 with the support of the host 2102. In the host 2102, an executing host application may communicate with the executing client application via the OTT connection 2150 terminating at the UE 2106 and host 2102. In providing the service to the user, the UE's client application may receive request data from the host's host application and provide user data in response to the request data. The OTT connection 2150 may transfer both the request data and the user data. The UE's client application may interact with the user to generate the user data that it provides to the host application through the OTT connection 2150.

[0131] The OTT connection 2150 may extend via a connection 2160 between the host 2102 and the network node 2104 and via a wireless connection 2170 between the network node 2104 and the UE 2106 to provide the connection between the host 2102 and the UE 2106. The connection 2160 and wireless connection 2170, over which the OTT connection 2150 may be provided, have been drawn abstractly to illustrate the communication between the host 2102 and the UE 2106 via the network node 2104, without explicit reference to any intermediary devices and the precise routing of messages via these devices.

[0132] As an example of transmitting data via the OTT connection 2150, in step 2108, the host 2102 provides user data, which may be performed by executing a host application. In some embodiments, the user data is associated with a particular human user interacting with the UE 2106. In other embodiments, the user data is associated with a UE 2106 that shares data with the host 2102 without explicit human interaction. In step 2110, the host 2102 initiates a transmission carrying the user data towards the UE 2106. The host 2102 may initiate the transmission responsive to a request transmitted by the UE 2106. The request may be caused by human interaction with the UE 2106 or by operation of the client application executing on the UE 2106. The transmission may pass via the network node 2104, in accordance with the teachings of the embodiments described throughout this disclosure. Accordingly, in step 2112, the network node 2104 transmits to the UE 2106 the user data that was carried in the transmission that the host 2102 initiated, in accordance with the teachings of the embodiments described throughout this disclosure. In step 2114, the UE 2106 receivesthe user data carried in the transmission, which may be performed by a client application executed on the UE 2106 associated with the host application executed by the host 2102.

[0133] In some examples, the UE 2106 executes a client application which provides user data to the host 2102. The user data may be provided in reaction or response to the data received from the host 2102. Accordingly, in step 2116, the UE 2106 may provide user data, which may be performed by executing the client application. In providing the user data, the client application may further consider user input received from the user via an input / output interface of the UE 2106. Regardless of the specific manner in which the user data was provided, the UE 2106 initiates, in step 2118, transmission of the user data towards the host 2102 via the network node 2104. In step 2120, in accordance with the teachings of the embodiments described throughout this disclosure, the network node 2104 receives user data from the UE 2106 and initiates transmission of the received user data towards the host 2102. In step 2122, the host 2102 receives the user data carried in the transmission initiated by the UE 2106.

[0134] One or more of the various embodiments improve the performance of OTT services provided to the UE 2106 using the OTT connection 2150, in which the wireless connection 2170 forms the last segment. More precisely, the teachings of these embodiments may provide a way to control the trajectory of one or more mobile edge nodes (e.g., mobile network nodes), which can allow for load balancing between different mobile edge nodes. In additional or alternative embodiments, controlling the trajectory of one or more mobile edge nodes can allow for adjustments to signal quality (e.g., reduction of noise or pathloss) based on a position of the mobile edge node relative to a UE. In additional or alternative embodiments, the mobile edge nodes can be incorporated in a moving entity (e.g., a UAV). In some examples, this can be beneficial for private 5G networks which, unlike public 5G networks, are closed entities and can hence allow for such UAV-based mobility which can be controlled by a single organization such as a business enterprise. Moreover, this can be implemented for future UAV undisruptive communications. In some examples, simultaneous localization and mapping (“SLAM”)-based spiking neural networks (“SNN”) can be an efficient and reliable UE-UAV allocation.

[0135] In an example scenario, factory status information may be collected and analyzed by the host 2102. As another example, the host 2102 may process audio and video data which may have been retrieved from a UE for use in creating maps. As another example, the host 2102 may collect and analyze real-time data to assist in controlling vehicle congestion (e.g., controlling traffic lights). As another example, the host 2102 may store surveillance videouploaded by a UE. As another example, the host 2102 may store or control access to media content such as video, audio, VR or AR which it can broadcast, multicast or unicast to UEs. As other examples, the host 2102 may be used for energy pricing, remote control of non-time critical electrical load to balance power generation needs, location services, presentation services (such as compiling diagrams etc. from data collected from remote devices), or any other function of collecting, retrieving, storing, analyzing and / or transmitting data.

[0136] In some examples, a measurement procedure may be provided for the purpose of monitoring data rate, latency and other factors on which the one or more embodiments improve. There may further be an optional network functionality for reconfiguring the OTT connection 2150 between the host 2102 and UE 2106, in response to variations in the measurement results. The measurement procedure and / or the network functionality for reconfiguring the OTT connection may be implemented in software and hardware of the host 2102 and / or UE 2106. In some embodiments, sensors (not shown) may be deployed in or in association with other devices through which the OTT connection 2150 passes; the sensors may participate in the measurement procedure by supplying values of the monitored quantities exemplified above, or supplying values of other physical quantities from which software may compute or estimate the monitored quantities. The reconfiguring of the OTT connection 2150 may include message format, retransmission settings, preferred routing etc.; the reconfiguring need not directly alter the operation of the network node 2104. Such procedures and functionalities may be known and practiced in the art. In certain embodiments, measurements may involve proprietary UE signaling that facilitates measurements of throughput, propagation times, latency and the like, by the host 2102. The measurements may be implemented in that software causes messages to be transmitted, in particular empty or ‘dummy’ messages, using the OTT connection 2150 while monitoring propagation times, errors, etc.

[0137] Although the computing devices described herein (e.g., UEs, network nodes, hosts) may include the illustrated combination of hardware components, other embodiments may comprise computing devices with different combinations of components. It is to be understood that these computing devices may comprise any suitable combination of hardware and / or software needed to perform the tasks, features, functions and methods disclosed herein. Determining, calculating, obtaining or similar operations described herein may be performed by processing circuitry, which may process information by, for example, converting the obtained information into other information, comparing the obtained information or converted information to information stored in the network node, and / or performing one or moreoperations based on the obtained information or converted information, and as a result of said processing making a determination. Moreover, while components are depicted as single boxes located within a larger box, or nested within multiple boxes, in practice, computing devices may comprise multiple different physical components that make up a single illustrated component, and functionality may be partitioned between separate components. For example, a communication interface may be configured to include any of the components described herein, and / or the functionality of the components may be partitioned between the processing circuitry and the communication interface. In another example, non-computationally intensive functions of any of such components may be implemented in software or firmware and computationally intensive functions may be implemented in hardware.

[0138] In certain embodiments, some or all of the functionality described herein may be provided by processing circuitry executing instructions stored on in memory, which in certain embodiments may be a computer program product in the form of a non-transitory computer- readable storage medium. In alternative embodiments, some or all of the functionality may be provided by the processing circuitry without executing instructions stored on a separate or discrete device-readable storage medium, such as in a hard-wired manner. In any of those particular embodiments, whether executing instructions stored on a non-transitory computer- readable storage medium or not, the processing circuitry can be configured to perform the described functionality. The benefits provided by such functionality are not limited to the processing circuitry alone or to other components of the computing device, but are enjoyed by the computing device as a whole, and / or by end users and a wireless network generally.

Claims

Claims:

1. A method of operating a network node (1800), the method comprises: determining (1510) information associated with a wireless network provided to one or more communication devices (110) by one or more mobile network nodes (140a-c); determining (1520) a trajectory of a mobile network node of the one or more mobile network nodes; determining (1530) adjustments to the trajectory of the mobile network node based on the information; and adjusting (1540) the trajectory of the mobile network node.

2. The method of Claim 1, wherein the network node comprises a mobile network node manager (520), and wherein adjusting the trajectory of the mobile network node comprises transmitting an indication of the adjustments to the trajectory of the mobile network node to the mobile network node.

3. The method of Claim 1, wherein the mobile network node comprises a first mobile network node (540b-c), wherein the network node comprises a second mobile network node (540a) of the one or more mobile network nodes, and wherein adjusting the trajectory of the mobile network node comprises transmitting an indication of the adjustments to the trajectory of the mobile network node to the mobile network node or a mobile network node manager.

4. The method of Claim 1, wherein the mobile network node comprises the mobile network node (540a), and wherein adjusting the trajectory of the mobile network node comprises moving based on the adjustments to the trajectory.

5. The method of any of Claims 1-4, wherein the mobile network node is part of at least one of: an unmanned aerial vehicle, UAV; an unmanned surface vehicle, USV; and an unmanned ground vehicle, UGV.

6. The method of any of Claims 1-5, wherein the information associated with the wireless network comprises an indication of a load on each of the one or more mobile network nodes.

7. The method of any of Claims 1-6, wherein the information comprises an indication of signal quality between the one or more communication devices and the one or more mobilenetwork nodes.

8. The method of any of Claims 1-7, wherein the information associated with the wireless network comprises an indication of at least one of: a location of the one or more mobile network nodes; a mobility of the one or more mobile network nodes; and a capacity of the one or more mobile network nodes.

9. The method of any of Claims 1-8, wherein the information comprises an indication of at least one of: a location of the one or more communication devices; a mobility of the one or more communication devices; a data rate of the one or more communication devices; and a priority of the one or more communication devices.

10. The method of any of Claims 1-9, wherein the adjustments to the trajectory of the mobile network node comprises an indication of a limitation of a movement of the mobile network node.

11. The method of any of Claims 1-10, wherein the adjustments to the trajectory of the mobile network node comprises an indication of an explicit movement of the mobile network node.

12. The method of any of Claims 1-11, wherein determining the adjustments to the trajectory of the mobile network node comprises determining the adjustments to the trajectory of the mobile network node using a spiking neural network reinforced learning, SNN-RL, procedure.

13. The method of any of Claims 1-12, wherein determining the trajectory of the first mobile network node comprises determining a trajectory of each of the one or more mobile network nodes, and wherein determining the adjustments to the trajectory of the mobile network node comprises determining adjustments to each trajectory of the one or more mobile network nodes to balance load between the one or more mobile network nodes.

14. A network node (1800) operating in a communications network, the network node comprising: processing circuitry (1802); and memory (1804) coupled to the processing circuitry and having instructions stored therein that are executable by the processing circuitry to cause the network node to perform operations comprising any of the operations of Claims 1-13.

15. A computer program comprising program code to be executed by processing circuitry (1802) of a network node (1800) operating in a communications network, whereby execution of the program code causes the network node to perform operations comprising any operations of Claims 1-13.

16. A computer program product comprising a non-transitory storage medium (1804) including program code to be executed by processing circuitry (1802) of a network node (1800) operating in a communications network, whereby execution of the program code causes the network node to perform operations comprising any operations of Claims 1-13.

17. A non-transitory computer-readable medium having instructions stored therein that are executable by processing circuitry (1802) of a network node (1800) operating in a communications network to cause the network node to perform operations comprising any of the operations of Claims 1-13.

18. A network node for adjusting trajectory of a mobile network node, the network node comprising: processing circuitry configured to perform any of the steps of any of Claims 1-13; power supply circuitry configured to supply power to the processing circuitry.

19. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to provide user data; and a network interface configured to initiate transmission of the user data to a network node in a cellular network for transmission to a user equipment (UE), the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of Claims 1-13 to transmit the user data from the host to the UE.

20. The host of Claim 19, wherein: the processing circuitry of the host is configured to execute a host application that provides the user data; and the UE comprises processing circuitry configured to execute a client application associated with the host application to receive the transmission of user data from the host.

21. A method implemented in a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: providing user data for the UE; andinitiating a transmission carrying the user data to the UE via a cellular network comprising the network node, wherein the network node performs any of the operations of any of Claims 1-13 to transmit the user data from the host to the UE.

22. The method of Claim 21, further comprising, at the network node, transmitting the user data provided by the host for the UE.

23. The method of any of Claims 21-22, wherein the user data is provided at the host by executing a host application that interacts with a client application executing on the UE, the client application being associated with the host application.

24. A communication system configured to provide an over-the-top (OTT) service, the communication system comprising: a host comprising: processing circuitry configured to provide user data for a user equipment (UE), the user data being associated with the over-the-top service; and a network interface configured to initiate transmission of the user data toward a cellular network node for transmission to the UE, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of Claims 1-13 to transmit the user data from the host to the UE.

25. The communication system of Claim 24, further comprising: the network node; and / or the UE.

26. A host configured to operate in a communication system to provide an over-the-top (OTT) service, the host comprising: processing circuitry configured to initiate receipt of user data; and a network interface configured to receive the user data from a network node in a cellular network, the network node having a communication interface and processing circuitry, the processing circuitry of the network node configured to perform any of the operations of Claims 1-13 to receive the user data from a user equipment (UE) for the host.

27. The host of any of Claims 25-26, wherein: the processing circuitry of the host is configured to execute a host application that receives the user data; and the host application is configured to interact with a client application executing on the UE, the client application being associated with the host application.

28. The host of any of Claims 26-27, wherein the initiating receipt of the user data comprises requesting the user data.

29. A method implemented by a host configured to operate in a communication system that further includes a network node and a user equipment (UE), the method comprising: at the host, initiating receipt of user data from the UE, the user data originating from a transmission which the network node has received from the UE, wherein the network node performs any of the steps of any of Claims 1-13 to receive the user data from the UE for the host.

30. The method of Claim 29, further comprising at the network node, transmitting the received user data to the host.

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