Multimodal transformer-based integrated sensing and communication (ISAC) data enhanced beam selection

Abstract

A computer-implemented method to utilize ISAC data to enhance beam prediction The method comprises obtaining, for each UE connected to an access point, ISAC data and UE position data and passing the ISAC data and the UE position data through a multimodal transformer encoder. The method comprises translating the output of the multimodal transformer encoder into an output of dimension M corresponding to the number of beamforming vectors by passing the output of the multimodal transformer encoder through a linear neural network, where the output is a predicted reward for each of the M possible beamforming vectors. The method comprises using the predicted reward, train multi-agent contextual bandits to select beamforming vectors for the UEs, where the state-space for the multi-agent contextual bandits comprises the ISAC data and UE location data, and the action space comprises the M beamforming vectors.

Description

[0001] MULTIMODAL TRANSFORMER-BASED INTEGRATED SENSING AND COMMUNICATION (ISAC) DATA ENHANCED BEAM SELECTION

[0002] TECHNCIAL FIELD

[0003] The application relates generally to beamforming in a communication network, and more specifically to using sensing data and transformer models for enhanced beamforming.

[0004] BACKGROUND

[0005] Artificial Intelligence (Al) is a rapidly advancing field that includes a variety of techniques and methods designed to enable machines to replicate human intelligence and cognitive functions. Fundamentally, Al focuses on creating algorithms and models that allow machines to process data, learn from it, and make decisions or predictions based on that learning. Recently, Al algorithms have been extensively applied to optimize 5G and 6G wireless communication systems.

[0006] Transformers are a widely used deep learning architecture applied in various fields, such as natural language processing and computer vision. The transformer architecture is based on self-attention mechanisms, which allow the model to assess the significance of different words in a sequence. This self-attention mechanism enables transformers to capture long-range dependencies in the input data. Specifically, multi-modal transformers (MMTs) are adept at handling multiple modalities, as they can effectively scale to accommodate various modalities and tasks.

[0007] Multi-agent contextual bandit is a reinforcement learning algorithm. It can be regarded as an extension of the traditional contextual bandit framework, designed to address decision-making problems involving multiple agents operating in a shared environment. In this setting, each agent must learn to make optimal decisions based on contextual information while considering the actions and potential interactions with other agents. This framework is particularly useful in scenarios where agents must cooperate or compete to maximize their cumulative rewards, such as in resource allocation, and network optimization. The multi-agent aspect introduces additional complexity, as agents must not only learn from their own experiences but also adapt to the strategies of others. Integrated Sensing and Communication (ISAC) is an emerging paradigm that seeks to unify the traditionally separate functions of wireless communication and radar sensing into a single framework. This integration aims to optimize the use of spectral and hardware resources, enabling devices to simultaneously transmit data and sense the environment. ISAC is particularly relevant in the context of next-generation wireless networks, such as 5G and 6G, where the demand for efficient spectrum utilization and advanced sensing capabilities is increasing. By leveraging shared infrastructure and spectrum, ISAC systems can enhance situational awareness, improve communication reliability, and support new applications like autonomous driving and smart cities.

[0008] Beam selection is the process of choosing the optimal directional beam to establish a strong and efficient communication link between a base station and user equipment in wireless networks. It is crucial for maximizing signal quality and network capacity, especially in environments with high mobility or dense obstructions. Research has demonstrated that using sensing data, like LiDAR, RADAR, or cameras, can enhance the beam prediction performance. While future base stations are expected to have ISAC sensing data available, its effectiveness for beam prediction has yet to be evaluated. Some related works such as Choi, Jinseok, Jeonghun Park, Namyoon Lee, and Ahmed Alkhateeb. "Joint and Robust Beamforming Framework for Integrated Sensing and Communication Systems." arXiv preprint arXiv:2402.09155 (2024) and Hua, Haocheng, Jie Xu, and Tony Xiao Han. "Optimal transmit beamforming for integrated sensing and communication." IEEE Transactions on Vehicular Technology (2023) attempt to jointly optimize the design of sensing and communication capabilities, but they do not address how availability of ISAC sensing data could enhance communication performance.

[0009] There currently exist certain challenge(s).

[0010] Some current studies only focus on beamforming for jointly optimizing sensing and communication capabilities. They use a unified framework with the objective of maximizing communication performance given sensing performance constraints. However, these works do not consider how presence of ISAC sensing information can improve the communication performance. With base stations equipped with ISAC sensing, it's important to evaluate how this capability can improve communication performance.

[0011] There are some existing works using other modalities of beam prediction, including camera, LiDAR, and RADAR. However, the installation of these sensors can impose an extra cost, and they suffer from instability. For instance, cameras are less reliable in adverse weather conditions such as snow and fog, RADAR provides a sparser environmental representation, and LiDAR sensors can be expensive.

[0012] Although ISAC data is now in general use for many purposes, research on utilizing ISAC data to improve wireless communication performance is rare. While some research has demonstrated that using sensing modalities like cameras can enhance beam prediction, it is of interest to establish a framework for how ISAC data can be usefully included in beamforming.

[0013] SUMMARY

[0014] Certain aspects of the disclosure and their embodiments may provide solutions to these or other challenges.

[0015] According to a first aspect, there is a computer-implemented method to utilize integrated sensing and communication, ISAC, data to enhance beam prediction in a communication network. The method comprises obtaining, for each user equipment, UE, connected to an access point, ISAC data and UE position data. The method comprises passing the ISAC data and the UE position data through a multimodal transformer encoder. The method comprises translating the output of the multimodal transformer encoder into an output of dimension M corresponding to the number of beamforming vectors by passing the output of the multimodal transformer through a linear neural network, where the output of the linear neural network is a predicted reward for each of the M possible beamforming vectors. The method comprises using the predicted reward to train multi-agent contextual bandits to select beamforming vectors for the UEs, where the state space for the multi-agent contextual bandits comprises the ISAC data and the UE location data, and the action space comprises the M beamforming vectors.

[0016] According to a second aspect, there is an access point in a communication network comprising a processor and a memory. The access point is configured to obtain, for each user equipment, UE, connected to the access point, ISAC data and UE position data. The access point is configured to pass the ISAC data and the UE position data through a multimodal transformer encoder. The access point is configured to translate the output of the multimodal transformer encoder into an output of dimension M corresponding to the number of beamforming vectors by passing the output of the multimodal transformer through a linear neural network, where the output of the linear neural network is a predicted reward for each of the M possible beamforming vectors. The access point is configured to use the predicted reward to train multi-agent contextual bandits to select beamforming vectors for the UEs, where the state space for the multi-agent contextual bandits comprises the ISAC data and the UE location data, and the action space comprises the M beamforming vectors.

[0017] Certain embodiments may provide one or more of the following technical advantage(s).

[0018] With certain aspects, the ISAC sensing data can be utilized to improve the accuracy of beam prediction. Moreover, by improving the beam prediction performance, the spectral efficiency can also be enhanced.

[0019] By utilizing ISAC sensing data to enhance the beam selection process, additional sensing equipment such as cameras, RADAR, or LiDAR will not be necessary at the base station.

[0020] Multimodal transformers help in learning the relationship between different input modalities, facilitating the learning process.

[0021] Certain aspects of the method set out in this document are capable of generalizing across various scenarios, allowing for the application of transfer learning to decrease training time.

[0022] BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Fig. 1 is a schematic overview of a simple communication system in which method according to the disclosure may be implemented.

[0024] Fig. 2 is a flowchart of a method according to the disclosure. Fig. 3 is an output of an experiment comparing the method according to the disclosure with other methods.

[0025] Fig. 4 is an output of an experiment comparing the method according to the disclosure with other methods.

[0026] Fig. 5 is an output of an experiment comparing the method according to the disclosure with other methods.

[0027] Fig. 6 is a schematic overview of a cloud implementation of a method according to the disclosure.

[0028] Fig. 7 is a detailed schematic of a first communication network in which methods according to the disclosure may be implemented.

[0029] Fig. 8 is a detailed schematic of a second communication network in which methods according to the disclosure may be implemented.

[0030] Fig. 9 is a schematic overview of a user equipment or wireless device according to the disclosure.

[0031] Fig. 10 is a schematic overview of a network node or access point according to the disclosure.

[0032] Fig. 11 is a schematic overview of a virtualization environment according to the disclosure.

[0033] DETAILED DESCRIPTION OF THE DRAWINGS

[0034] 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 full scope of the subject matter covered by the claims to those skilled in the art.

[0035] Dataset:

[0036] In an illustrative example running through the detailed description, the DeepSense 6G dataset is used to evaluate the effectiveness of proposed method by utilizing Scenarios 42 and 44 of the DeepSense 6G dataset, where the data for each scenario was collected in an indoor environment. In Scenario 42, two people are moving across a room, while in Scenario 44, one person is moving across a room. The dataset consists of different modality sensors placed at the Access Point (AP), including camera, LiDAR, RADAR, and ISAC sensors. In certain aspects, the ISAC sensing and the location data for beam prediction is used for different users in the indoor environment. The camera and LiDAR sensors are used to calculate the position of the user. This dataset is used solely for illustrative purposes and as a nonlimiting example of the situations where embodiments of the invention may be implemented.

[0037] System Overview:

[0038] Figure 1 illustrates an embodiment of an overview of the system that is utilized. In some examples, the system may comprise an indoor environment. In the illustrative example, there is one AP with a mmWave phased array, and one or more users are moving around the room. The AP needs to provide connectivity to the plurality of users.

[0039] Let K denote the number of users, and % the set of all users. Furthermore, let there be N antennas in the phased array and M beamforming vectors fme (CWxlpresent in the beamforming codebook. For a user k e % being served using vector fm, the received signal ykcan be expressed as: where xkis the signal intended for the user k, with IE [|xfc|2] = 1 . Furthermore, hke CNis the complex channel vector between the AP and the user k, and zkis the received additive white Gaussian noise (AWGN) at UE k. Each user k will receive the signal component hkfmikxk, and the interference component k' k hTkfm,k'xk' + zk. The received signal will furthermore be impaired by the choice of beamforming vectors for other users.

[0040] For each user k, the Signal to Interference plus noise ratio (SINR), can be mathematically expressed as:

[0041] SINRkfm-) = where <J2is the variance of the AWGN noise.

[0042] Next, using the Shannon capacity formula, a spectral efficiency can be expressed for each user k as:

[0043] Problem Formulation:

[0044] The objective of the methods presented herein is to maximize the sum spectral efficiency of all the users moving in the environment. This objective can be mathematically expressed as:

[0045] The objective can be achieved by finding beamforming vectors for all users which cumulatively maximize the sum spectral efficiency of all users. Finding such beamforming vectors is a cooperative problem, where the AP will need to allocate beamforming vector to all users, such that the cumulative performance is maximized.

[0046] Multimodal transformer based contextual bandits

[0047] The objective is modelled as a multi-agent contextual bandits’ problem. Each user of a device / UE connected to the AP will act as a separate agent, where the agent is a machine learning agent. Each agent takes the input of ISAC image / data, and the UE position data. The output for each agent will be the q-value or predicted reward for each of the M possible beamforming vectors. The detail for each component is summarized below.

[0048] Contextual Bandits

[0049] Contextual bandits are a type of reinforcement learning algorithm that extends the multi-armed bandit problem by incorporating context or additional information to make more informed decisions. In the traditional multi-armed bandit problem, an agent must choose between different options (or "arms") to maximize rewards, learning over time which options yield the best results. Contextual bandits enhance this framework by considering the context in which decisions are made, such as user data or environmental conditions, allowing the algorithm to tailor its choices based on the specific situation. Unlike other reinforcement learning (RL) approaches, which often involve learning a policy over multiple states and actions to maximize cumulative rewards over time, contextual bandits focus on optimizing immediate rewards without considering future states. Contextual bandits are relevant for aspects of the framework, as each time-step is independent, and the agent needs to select the best action for each state.

[0050] • State space: The state space or the contexts are the I SAC data and user location data.

[0051] • Action space: The action space corresponds to the M beamforming vectors.

[0052] • Reward Design: For the reward design, the straightforward definition would be the sum spectral efficiency of all K users ^k=1Rk. In simulations, relying on sum spectral efficiency leads to biased agent behavior. This occurs because a user's spectral efficiency increases when they are near the AP and decreases as they move away. Consequently, the agent becomes biased towards actions that yield higher rewards, which in this case, are due to the user's proximity to the AP rather than the chosen action itself. As a result, the agent begins to favor these actions in all situations, leading to sub-optimal decisionmaking. To address this problem, a distance-based factor is introduced. Mathematically, the reward at time t is formulated as: o where dkis the distance of UE k from the AP, and a is a hyperparameter.

[0053] Data Preprocessing (agents):

[0054] • User location data: The dataset does not include the locations of the UEs. To determine the users' locations, the camera images are aligned with LiDAR data to accurately pinpoint the user’s positions. Initially, for example the pretrained "You Only Look Once (Y0L0v5)" model may be employed to identify the bounding boxes of users moving within the environment. This is the method that was used in the illustrative example which will be used for increased comprehension throughout the detailed description. These bounding boxes are used to calculate the azimuth angle and radial angle a for the location of each user. These angles are applied to the LiDAR image to derive the users' exact coordinates. Since the LiDAR point cloud provides multiple points across a range of angles, these coordinates are averaged to determine the users' positions. In a real-world scenario, if the exact locations of users are available, they can be used directly.

[0055] • ISAC data: In the example, the ISAC data was collected by a phased array consisting of 16-element uniform linear array. The correlation between the received waveform at the phased array and the transmitted waveform is calculated, which provides a 2D image or representation of the environment. In our example, we treat the ISAC sensor data as equivalent to this 2D image representation of the ISAC data.

[0056] Once the data is collected and in a suitable form, the method may comprise analyzing the data using machine learning, such as by passing the data though a convolutional neural network. The analyzing step comprises extracting features and patterns from the data set to improve decision making. The extracted features and patterns may further be compressed to reduce dimensionality for further efficiency gains.

[0057] In the example, the ISAC data / image is forwarded through a series of convolutional layers to learn the hierarchical features and patterns that are essential for accurate decision making. A Convolutional Neural Network (CNN) processes input data by applying a series of convolutional layers, where each layer consists of filters that detect specific features such as edges, textures, or shapes. These features are then passed through pooling layers to reduce dimensionality and fully connected layers to take decision based on the learned features.

[0058] In the example, the ISAC image is passed through 8 convolutional layers. The final layer is then flattened and forwarded to the next stage.

[0059] The method further comprises passing the ISAC data to a transformer model, such as a multimodal transformer encoder.

[0060] Multimodal Transformer Encoder: A Multimodal Transformer Encoder refers to a variation or extension of the Transformer architecture specifically designed to process multimodal data. Transformers typically utilize self-attention mechanisms, allowing each token to attend to other tokens within the same input sequence. In a multimodal setting, cross-modal attention mechanisms are introduced to allow tokens from one modality to attend to tokens from other modalities, enabling the model to learn inter-modal relationships.

[0061] The (multimodal) transformer takes as input the flattened output of the convolutional layers, and the user location data.

[0062] Linear Neural Lavers: The output of the transformer is forwarded to a series of linear neural network layers. The neural network layers allow for the transformation of encoded features into the final output. The output layer is of the dimension M, corresponding to the number of beamforming vectors available for a UE and more precisely to the q-value or cost associated to each beamforming vector. These q- values can be used in the contextual bandits disclosed above to obtain an optimal set of beamforming vectors for all UEs connected to the AP.

[0063] Transfer Reinforcement Learning

[0064] Transfer reinforcement learning is a machine learning method focusing on leveraging knowledge gained from one or more source tasks to improve learning efficiency and performance in a target task. This approach aims to reduce the amount of data and time required to train models on new tasks by utilizing previously acquired skills and experiences. It focuses on adapting learned policies to new environments, ensuring models generalize well across different tasks and scenarios.

[0065] In the illustrative example, the model trained on the single-agent Scenario 44 is used for the initial training and transfer reinforcement learning is used to transfer the trained model for the multiple agents in Scenario 42. Further, the multiple agents in Scenario 42 are trained for one further epoch.

[0066] Simulation Setup

[0067] In some embodiments, the agents are trained using the first 80% of the data and the last 20% of the data is reserved for testing. Each scenario in the illustrative example consists of 2100 time steps, with 100ms between each step. The AP has a uniform rectangular array with 2 vertical and 8 horizontal antennas. The carrier frequency of 60 MHz is used. Out of the predefined 64 beam codebook, we use the beams 12 to 49, that roughly approximate +- 30 degrees in the azimuth angle. In certain aspects, an epoch is defined as the contextual bandit iterating over the training data once. In certain aspects, an e-greedy policy is used to balance the exploration and exploitation tradeoff in RL problems. The e-greedy policy is a strategy used in RL where an agent predominantly exploits the best-known action to maximize rewards but occasionally explores other actions with a probability of e to discover potentially better options. This balance between exploration and exploitation helps the agent avoid local optima and improve its overall performance by ensuring that all actions are eventually tried.

[0068] In certain aspects, the method of the disclosure is referred to as Transformer Reinforcement Learning (TRL) and compared with the following benchmarks for the illustrative example to demonstrate the efficacy of the method:

[0069] • The agents are trained without using the multimodal transformer. In certain aspects, the flattened output of convolutional layers and the position data are concatenated and directly forwarded to the stage 3 Linear Neural layers. This model is called Deep Reinforcement Learning (DRL).

[0070] • Exhaustive Search algorithm: For each time step, all the actions are tested, and the actions that result in the highest sum spectral efficiency are selected.

[0071] • Random allocation: The action is selected randomly for each agent.

[0072] Performance Metrics: Apart from the spectral efficiency, we also use a modified regret function to demonstrate how far away our model is from the optimal action strategy. Regret is defined as the difference between the expected reward of the optimal policy and the expected reward of the policy chosen by the algorithm over a series of rounds: where at* is the optimal action that maximizes the expected reward

[0073] In certain aspects, the average spectral efficiency (SE)-regret is defined, which is the average of the difference between the spectral efficiency obtained from a model, and the spectral efficiency obtained through exhaustive search, over a series of rounds. where at* is the action selected by exhaustive search algorithm, and atis the action selected by the test model. Regret measures how close the model is to the optimal policy. Results for Scenario 44

[0074] The results for the illustrative example, Scenario 44, are presented. Recall that in Scenario 44 there is a single UE moving across the room, served by a single AP.

[0075] Table 1 : Average SE regret, spectral efficiency, and % of times with optimal action for different models on Scenario 44 testing data. Table 1 summarizes the result. For 1 epoch, the DRL performs better than the TRL. The reason is that both transformer and RL require a large amount of data to train effectively. However, increasing the number of epochs to 100 results in average SE regret of 0.0430 for TRL, as compared to 0.0854 for DRL. This shows that given sufficient data, TRL is 50% closer to the optimal value as compared to DRL. Figure 3 shows the performance of DRL and TRL, when the model is trained for varying number of epochs. It can be seen that for 50 and more epochs, the TRL outperforms DRL, and achieves lower regret.

[0076] Figure 4 shows the results of TRL for 100 epochs across the training stage. It can be seen that the model performs very closely to the exhaustive search algorithm. Results for Scenario 42

[0077] In the illustrative example, Scenario 42, 2 UEs are moving across the environment served by a single AP. In this scenario, the users will need to cooperatively select actions to minimize the interference between the UEs. Table 2: Average SE regret and sum spectral efficiency for different models in Scenario 42 test data.

[0078] Table 2 shows that training for 100 epochs, TRL gets a regret of 0.6995, which is 9.4% closer to exhaustive search as compared to DRL with 0.7720. In this scenario, all scenarios were used for training for 100 epochs, which results in regret of 0.8666. In some aspects, single agent for both users were used, which resulted in average SE regret of 0.902.

[0079] Using the models trained on Scenario 44, the results for transfer learning are presented next, in Table 3.

[0080] Table 3: Average SE regret for DRL and TRL with transfer learning.

[0081] For DRL, the model trained on Scenario 44 was used for the agents of Scenario 42 directly, we obtain a regret of 0.9174. Training the model for 1 epoch on the scenario improves the regret to 0.8714. However, for TRL, using scenario 44 model directly generates average SE regret of 0.7621. Training it for 1 epoch improves it to 0.5618, which is the best result obtained. The transformer-based model of the disclosure is hence capable of learning a more generalized model that performs effectively across various scenarios. Moreover, even a single additional training epoch can significantly enhance model performance.

[0082] Figure 5 shows the sum spectral efficiency comparison between TRL model trained for 100 epoch and Transfer Learning TRL model for 1 epoch. As can be seen, the two lines track each other very closely, and additionally that both lines track the exhaustive search model very closely, indicating that the obtained model is easy to transfer with minimal further training needed for excellent performance.

[0083] A cloud implementation of the proposed multimodal transformer-based beam prediction is shown in Figure 6. In cloud embodiments, the data collected at the AP can be transmitted to the cloud. On the cloud server, the computing intensive tasks of model training can be performed. After training, the model parameters can be transferred back to the AP, where the model can be deployed and used for beam prediction.

[0084] Figure 7 shows an example of a communication system 700 in accordance with some embodiments.

[0085] In the example, the communication system 700 includes a telecommunications network 702 that includes an access network 704, such as a radio access network (RAN), and a core network 706, which includes one or more core network nodes 708. The access network 704 includes one or more access network nodes, access points, or base stations of various types, access network nodes 710A and 71 OB are depicted (which may be collectively referred to as network nodes 710), or any other similar 3rdGeneration Partnership Project (3GPP) access nodes or non-3GPP access points (APs). Some embodiments of the access network 704 may include more than one access network technology. The network nodes 710 of access network 704 facilitate direct or indirect connection of wireless devices, also referred to as user equipments (UEs), such as by connecting UEs 712A, 712B, 712C, and 712D (one or more of which may be generally referred to as UEs 712) to the core network 706 over one or more wireless connections.

[0086] Moreover, a network node is not necessarily limited to an implementation in which a radio portion and a baseband portion are supplied and integrated by a single vendor. Thus, it will be understood that network nodes include disaggregated implementations or portions thereof. For example, in some embodiments, the telecommunications network 702 includes one or more Open-RAN (ORAN) network nodes. An ORAN network node is a network node in the telecommunications network 702 that supports an ORAN specification (e.g., a specification published by the O-RAN Alliance, or any similar organization) and may operate alone or together with other network nodes to implement one or more functionalities of any network node in the telecommunications network 702, including one or more access network nodes 710 and / or core network nodes 708.

[0087] Examples of an ORAN network node include an open radio unit (O-RU), an open distributed unit (O-DU), an open central unit (O-CU), including an O-CU control plane (O-CU-CP) or an O-CU user plane (O-CU-UP), a RAN intelligent controller (near-real time or non-real time) hosting software or software plug-ins, such as a near-real time control application (e.g., xApp) or a non-real time control application (e.g., rApp), or any combination thereof (the adjective “open” designating support of an ORAN specification). An ORAN network node may support a specification by, for example, supporting an interface defined by the ORAN specification, such as an A1 , F1 , W1 , E1 , E2, X2, Xn interface, an open fronthaul user plane interface, or an open fronthaul management plane interface. Moreover, an ORAN network node may be a logical node in a physical node. Furthermore, an ORAN network node may be implemented in a virtualization environment (described further below) in which one or more network functions are virtualized. For example, the virtualization environment may include an O-Cloud computing platform orchestrated by a Service Management and Orchestration Framework via an 0-2 interface defined by the 0-RAN Alliance or comparable technologies.

[0088] The network nodes 710 facilitate direct or indirect connection of one or more UEs 712 to the core network 706 over one or more wireless connections. 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 700 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 700 may include and / or interface with any type of communication, telecommunication, data, cellular, radio network, and / or other similar type of system.

[0089] The UEs 712 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 710 and other communication devices. Similarly, the network nodes 708, 710 are arranged, capable, configured, and / or operable to communicate directly or indirectly (e.g., via other devices of telecommunications network 702) with the UEs 712 and / or with other network nodes or equipment in the telecommunications network 702 to enable and / or provide network access, such as wireless network access, and / or to perform other functions, such as administration in the telecommunications network 702. More specifically, UEs 712 may send messages, data, and / or other signals to network nodes 708, 710 or other elements of the telecommunications network 702 by transmitting such signals to the relevant device directly without the signals passing through any intervening devices or by transmitting such signals to the relevant device indirectly through an intervening device (or multiple intervening devices) that then transmit the signal to the relevant device. Similarly, network nodes 708, 710 may send messages, data, and other signals to UEs 7122, other network nodes 708, 710, and other devices in telecommunications network 702 directly or indirectly. As one specific example, a core network node 108 may transmit a particular message to a UE 712 by transmitting the message to an access network node 710 that will then transmit the message to the intended UE 712. Similarly, a core network node 108 may receive a particular message from a UE 712 by receiving the message from an access network node 710 that itself received the message from the UE 712.

[0090] In the depicted example, the core network 706 connects elements of the access network 704 (e.g., one or more of the network nodes 710) to one or more host computing systems, such as host 716. 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 706 includes one or more core network nodes (e.g., core network node 708) of various types, one or more of which may be generally referred to as network nodes 708. Network nodes 708 are structured with hardware and software components. Features of these components may be substantially similar to those described with respect to the UEs, access network nodes, and / or hosts, such that the descriptions thereof are generally applicable to the corresponding components of the core network node 708. Example core network nodes provide 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 (SIDF), Unified Data Management (UDM), Security Edge Protection Proxy (SEPP), Network Exposure Function (NEF), and / or a User Plane Function (UPF).

[0091] The host 716 may be under the ownership or control of a service provider other than an operator or provider of the access network 704 and / or the telecommunications network 702. The host 716 may be operated by the service provider or on behalf of the service provider. The host 716 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. As a whole, the communication system 700 of Figure 7 enables connectivity between the UEs, network nodes, and hosts. In that sense, the communication system 700 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 (Wi-Fi); and / or any other appropriate wireless communication standard, such as the Worldwide Interoperability for Microwave Access (Wi-Max), Bluetooth, Z-Wave, Near Field Communication (NFC) ZigBee, Li- Fi, and / or any low-power wide-area network (LPWAN) standards such as LoRa and Sigfox. Moreover, the communication system 700 may be configured to support multiple different standards, protocols, or other rule sets, with individual components supporting all of the relevant rule sets or with different components or sub-systems within the communication system 700 supporting different standards, protocols, or rule sets.

[0092] As one example, in certain embodiments, access network 704 may contain some access network nodes 710 that support 3GPP radio access technologies (RAT), such as LTE or NR, while other access network nodes 710 support (or the same access network nodes 710 additionally support) non-3GPP RATs, such as Wi-Fi or a proprietary RAT. As another example, telecommunications network 702 may support multiple generations of related communication standards (e.g., 4G and 5G 3GPP communication standards) and, as a result, may include an access network 104 and / or a core network 106 that supports multiple different standard generations or may include multiple access networks 104 and / or multiple core networks 106 with individual networks 104, 106 supporting different standard generations.

[0093] Telecommunications network 702 may support network slicing to provide different logical networks to different devices that are connected to the telecommunications network 702. For example, the telecommunications network 702 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)ZMassive loT services to yet further UEs.

[0094] In some examples, one or more of the UEs 712 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 704 on a predetermined schedule, when triggered by an internal or external event, or in response to requests from the access network 704. Additionally, a UE may be configured for operating in single- or multi-RAT or multi-standard 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).

[0095] In the example, the hub 714 communicates with the access network 704 to facilitate indirect communication between one or more UEs (e.g., UE 712C and / or 712D) and network nodes (e.g., network node 710B). In some examples, the hub 714 may be a controller, router, content source and analytics, or any of the other communication devices described herein regarding UEs. For example, the hub 714 may be a broadband router enabling access to the core network 706 for the UEs. As another example, the hub 714 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 710, or by executable code, script, process, or other instructions in the hub 714.

[0096] As another example, the hub 714 may be a data collector that acts as temporary storage for UE data and, in some embodiments, may perform analysis or other processing of the data. As another example, the hub 714 may be a content source. For example, for a UE that is a VR headset, display, loudspeaker or other media delivery device, the hub 714 may retrieve VR assets, video, audio, or other media or data related to sensory information via a network node, which the hub 714 then provides to the UE either directly, after performing local processing, and / or after adding additional local content. In still another example, the hub 714 acts as a proxy server or orchestrator for the UEs, in particular if one or more of the UEs are low energy loT devices. The hub 714 may have a constant / persistent or intermittent connection to the network node 71 OB. The hub 714 may also allow for a different communication scheme and / or schedule between the hub 714 and UEs (e.g., UE 712C and / or 712D), and between the hub 714 and the core network 706. In other examples, the hub 714 is connected to the core network 706 and / or one or more UEs via a wired connection. Moreover, the hub 714 may be configured to connect to an M2M service provider over the access network 704 and / or to another UE over a direct connection. In some scenarios, UEs may establish a wireless connection with the network nodes 710 while still connected via the hub 714 via a wired or wireless connection. In some embodiments, the hub 714 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 71 OB. In other embodiments, the hub 714 may be a non-dedicated hub - that is, a device which is capable of operating to route communications between the UEs and network node 71 OB, but which is additionally capable of operating as a communication start and / or end point for certain data channels.

[0097] Figure 8 is another example of a communication system 800 according to some embodiments. As used herein, the communication system 800 includes multiple access points (APs) 810 (with four exemplary APs 810A, 810B, 810C, and 810D being depicted) and multiple wireless devices, referred to in the context of communication system 800 as stations (STAs) 812 (referred to individually as STA 812A, STA 812B, STA 812C, STA 812D, and STA 812E). STA 812A is served by AP 810A in a first basic service set (BSS) 820A. STA 810B and STA 810C are served by AP 810B in a second BSS, BSS 820B. STA 812D is served by AP 810C in a third BSS, BSS 820C. STA 812E is served by AP 810D in a fourth BSS, BSS 820D. Stations 812 may be non-AP STAs and correspond to various kinds of wireless devices, for example, user terminals, such as mobile or stationary computing devices like smartphones, laptop computers, desktop computers, tablet computers, gaming devices, head-mounted displays (HMDs) for Augmented Reality (AR) or Virtual Reality (VR), or the like. Further, stations 812 could, for example, correspond to other kinds of equipment like smart home devices, printers, multimedia devices, data storage devices, or the like.

[0098] Each of STAs 812 may connect through a radio link to one of APs 810. For example, depending on location or channel conditions experienced by a given STA 812, the STA may select an appropriate AP and BSS for establishing the radio link. The radio link may be based on one or more orthogonal frequency-division multiplexing (OFDM) carriers from a frequency spectrum that is shared on the basis of a contention-based mechanism, e.g., an unlicensed or license exempt band like 2.4 GHz Industrial, Scientific, and Medical (ISM) band, the 5 GHz band, the 6 GHz band, or the 60 GHz band.

[0099] Each AP 810 may provide data connectivity to STAs 812 connected to a particular AP 810. As illustrated, APs 810 may be connected to a data network 830. In this way, APs 810 may also provide data connectivity between STAs 812 and other entities, e.g., to one or more servers, service providers, data sources, data sinks, user terminals, or the like. Accordingly, the radio link established between a given STA 812 and its serving AP 810 may be used for providing various kinds of services to STA 812, e.g., a voice service, a multimedia service, or other data service. Such services may be based on applications that are executed on STA 812 and / or on a device linked to STA 812. By way of example, Figure 8 illustrates an application service platform 832 provided in data network 830. The application(s) executed on STA 812 and / or on one or more other devices linked to STA 812 may use the radio link for data communication with one or more other STA 812 and / or the application service platform 832, thereby enabling utilization of the corresponding service(s) at STA 812.

[0100] Figure 9 shows a wireless device 900, which may be configured to operate in communication system 700 of Figure 7 or in communication system 800 of Figure 80. The wireless device 900 may be alternatively referred to as a UE 900, like a UE 712 within the context of communication system 700, or as a station (STA) 900 or as a non-access-point station (non-AP STA) 900, like a STA 812 within the context of the communication system 800, in accordance with respective embodiments. As used herein, a wireless device refers to a device capable, configured, arranged and / or operable to communicate wirelessly with network nodes and / or other wireless devices. Examples of a wireless device 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, vehicle-mounted or vehicle embedded / integrated wireless device, and wireless terminal. Other examples include any type of UE identified by the 3rd Generation Partnership Project (3GPP), including a narrow band internet of things (NB-loT) UE, a machine type communication (MTC) UE, and / or an enhanced MTC (eMTC) UE.

[0101] A wireless device 900 may support device-to-device (D2D) communication, for example by implementing a 3GPP 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, wireless device 900 may not necessarily have a user in the sense of a human user who owns and / or operates the relevant device. Instead, wireless device 900 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, wireless device 900 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).

[0102] In particular embodiments, wireless device 900 includes processing circuitry 902 that is operatively coupled via a bus 904 to an input / output interface 906, a power source 908, a memory 910, a communication interface 912, and / or any other component, or any combination thereof. Certain embodiments of wireless device 900 may include all or a subset of the components shown in Figure 9. The level of integration between the components may vary from one embodiment of wireless device 900 to another. In general, in a particular embodiment of wireless device 900, processing circuitry 902, input / output interface 906, power source 908, memory 910, and communication interface 912 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of wireless device 900. Further, certain embodiments of wireless devices 900 may contain multiple instances of a component, such as multiple processors, memories, transceivers, transmitters, receivers, etc.

[0103] The processing circuitry 902 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 910. The processing circuitry 902 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 902 may include multiple central processing units (CPUs).

[0104] In the example, the input / output interface 906 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 wireless device 900. 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, a sensor, 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.

[0105] In some embodiments, the power source 908 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 to supply power to circuitry or to charge an associated battery. The power source 908 may further include power circuitry for delivering power from the power source 908 itself, and / or an external power source, to the various parts of wireless device 900 via input circuitry or an interface such as an electrical power cable. Power source 908 may perform any formatting, converting, or other modification to make accessible power suitable for the respective components of the wireless device 900 to which power is supplied.

[0106] The memory 910 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 910 includes one or more programs 914, such as an operating system, web browser application, a widget, gadget engine, or other application, and corresponding data 916. The memory 910 may store, for use by wireless device 900, any of a variety of various operating systems or combinations of operating systems.

[0107] The memory 910 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 minidual 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 for example be an embedded UICC (eUlCC), integrated UICC (iUICC) or a removable UICC commonly known as ‘SIM card.’ The memory 910 may allow wireless device 900 to access instructions, programs and the like, stored on transitory or non-transitory memory media, to offload 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 910, which may be or comprise a device-readable storage medium.

[0108] The processing circuitry 902 may be configured to communicate with an access network or other network via or using the communication interface 912. The communication interface 912 may comprise one or more communication subsystems and may include or be communicatively coupled to an antenna 922. The communication interface 912 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 wireless device or a network node in an access network). Each transceiver may include a transmitter 918 and / or a receiver 920 appropriate to provide network communications (e.g., optical, electrical, frequency allocations, and so forth). Moreover, the transmitter 918 and receiver 920 may be coupled to one or more antennas (e.g., antenna 922) and may share circuit components, software or firmware, or alternatively be implemented separately.

[0109] In the illustrated embodiment, communication functions of the communication interface 912 may include cellular communication, Wi-Fi communication (e.g., according to an IEEE 802.11 family standard), 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 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 / internet protocol (TCP / IP), synchronous optical networking (SONET), Asynchronous Transfer Mode (ATM), QUIC, Hypertext Transfer Protocol (HTTP), and so forth.

[0110] In particular embodiments, wireless device 900 may provide an output of data captured via a sensor, through its communication interface 912, via a wireless connection to a network node, and / or in any appropriate manner. Data captured by sensors of a wireless device 900 can be communicated through a wireless connection to a network node via another wireless device 900. In particular embodiments, such 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 from several 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). As another example, wireless device 900 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, wireless device 900 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.

[0111] Wireless device 900, 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, 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 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. In particular embodiments, wireless device 900 represents an loT device that 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 example embodiment of wireless device 900 shown in Figure 9.

[0112] As yet another specific example, in an loT scenario, wireless device 900 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 wireless device and / or a network node. Wireless device 900 may in this case be an M2M device, which may in a 3GPP context be referred to as an MTC device. As one particular example, wireless device 900 may implement the 3GPP NB-loT standard. In other scenarios, wireless device 900 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.

[0113] In practice, any number of wireless devices 900 may be used together with respect to a single use case. For example, a first wireless device 900 might be or be integrated in a drone and provide the drone’s speed information (obtained through a speed sensor) to a second wireless device 900 that is a remote controller operating the drone. When a user makes changes from the remote controller, the first wireless device 900 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 wireless device 900 can also include more than one of the functionalities described above. For example, wireless device 900 might comprise the sensor and the actuator, and handle communication of data for both the speed sensor and the actuators.

[0114] Figure 10 shows a network node (access point) 1000 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 telecommunications network. In accordance with respective embodiments, network node 1000 may be configured to operate in communication system 700 of Figure 7, like network nodes 708 or 710, or in communication system 800 of Figure 8, like an AP 810 or a station 812. 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)), O-RAN nodes or components of an O- RAN node (e.g., O-RU, O-DU, O-CU).

[0115] Network nodes 1000 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. Network node 1000 may be a relay node or a relay donor node controlling a relay. Network nodes 1000 may also include one or more (or all) parts of a distributed radio base station such as centralized digital units, distributed units (e.g., in an O-RAN access node) 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).

[0116] Other examples of network nodes 1000 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, SelfOrganizing Network (SON) nodes, positioning nodes (e.g., Evolved Serving Mobile Location Centers (E-SMLCs)), and / or Minimization of Drive Tests (MDTs).

[0117] In particular embodiments, network node 1000 includes a processing circuitry 1002, a memory 1004, a communication interface 1006, and a power source 1008. In general, in a particular embodiment of network node 1000, processing circuitry 1002, memory 1004, communication interface 1006, and power source 1008 may, in whole or in part, represent or include physical components common to or shared by one or more of the other elements of network node 1000.

[0118] The network node 1000 may be composed of multiple distinct network entities (e.g., a NodeB entity and a RNC entity, or a BTS entity and a BSC entity, etc.), which may each have or utilize their own respective physical components. In certain scenarios in which the network node 1000 comprises multiple such entities (e.g., BTS and BSC), one or more of the separate entities 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 1000 may be configured to support multiple radio access technologies (RATs). In such embodiments, some components may be duplicated (e.g., separate memories 1004 or portions of memory 1004 for different RATs) and some components may be reused (e.g., a same antenna 1010 may be shared by different RATs). The network node 1000 may also include multiple sets of the various illustrated components for different wireless technologies integrated into network node 1000, for example GSM, WCDMA, LTE, NR, Wi-Fi (e.g., according to an IEEE 802.11 family standard), 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 1000.

[0119] The processing circuitry 1002 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 components, such as the memory 1004, to provide network node 1000 functionality.

[0120] In some embodiments, the processing circuitry 1002 includes a system on a chip (SOC). In some embodiments, the processing circuitry 1002 includes one or more of radio frequency (RF) transceiver circuitry 1012 and baseband processing circuitry 1014. In some embodiments, the RF transceiver circuitry 1012 and the baseband processing circuitry 1014 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 1012 and baseband processing circuitry 1014 may be on the same chip or set of chips, boards, or units.

[0121] The memory 1004 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 computer-executable memory devices that store information, data, and / or instructions that may be used by the processing circuitry 1002. The memory 1004 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 1002 and utilized by the network node 1000. The memory 1004 may be used to store any calculations made by the processing circuitry 1002 and / or any data received via the communication interface 1006. In some embodiments, the processing circuitry 1002 and memory 1004 is integrated. The communication interface 1006 is used in wired or wireless communication of signaling and / or data with UEs, other network nodes, and / or any other network equipment. In the illustrated embodiment, communication interface 1006 comprises port(s) / terminal(s) 1016 to send and receive data, for example to and from a network over a wired connection. In particular embodiments, network node 900 may be capable of wireless communication and communication interface 1006 may also include radio front-end circuitry 1018 that may be coupled to, or in certain embodiments a part of, an antenna 1010. Particular embodiments of radio front-end circuitry 1018 include filter(s) 1020 and amplifier(s) 1022. The radio front-end circuitry 1018 may be connected to an antenna 1010 and processing circuitry 1002. The radio front-end circuitry may be configured to condition signals communicated between antenna 1010 and processing circuitry 1002. The radio front-end circuitry 1018 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 1018 may convert the digital data into a radio signal(s) having the appropriate channel and bandwidth parameters using a combination of filters 1020 and / or amplifiers 1022. The radio signal(s) may then be transmitted via the antenna 1010. Similarly, when receiving data, the antenna 1010 may collect radio signals which are then converted into digital data by the radio front-end circuitry 1018. The digital data may be passed to the processing circuitry 1002. In other embodiments, the communication interface may comprise different components and / or different combinations of components.

[0122] In certain alternative embodiments, network node 1000 may be capable of wireless communication but does not include separate radio front-end circuitry 1018, instead, the processing circuitry 1002 includes radio front-end circuitry and is connected to the antenna 1010. Similarly, in some embodiments, all or some of the RF transceiver circuitry 1012 is part of the communication interface 1006. In still other embodiments, the communication interface 1006 includes one or more ports or terminals 1016, the radio front-end circuitry 1018, and the RF transceiver circuitry 1012, as part of a radio unit (not shown), and the communication interface 1006 communicates with the baseband processing circuitry 1014, which is part of a digital unit (not shown).

[0123] The antenna 1010 may include one or more antennas, or antenna arrays, configured to send and / or receive wireless signals. The antenna 1010 may be coupled to the radio front-end circuitry 1018 and may be any type of antenna capable of transmitting and receiving data and / or signals wirelessly. In certain embodiments, the antenna 1010 is separate from the network node 1000 and connectable to the network node 1000 through one or more interfaces or ports.

[0124] The antenna 1010, communication interface 1006, and / or the processing circuitry 1002 may be configured to perform some or all of the receiving operations and / or obtaining operations described herein as being performed by the network node 1000. Any information, data and / or signals may be received from a UE, another network node and / or any other network equipment. Similarly, the antenna 1010, the communication interface 1006, and / or the processing circuitry 1002 may be configured to perform some or all of the transmitting or sending operations described herein as being performed by the network node 1000. Any information, data and / or signals may be transmitted to a UE, another network node and / or any other network equipment.

[0125] The power source 1008 provides power to the various components of network node 1000 in a form suitable for the respective components (e.g., at a voltage and current level needed for each respective component). The power source 1008 may further comprise, or be coupled to, power management circuitry to supply the components of the network node 1000 with power for performing the functionality described herein. For example, the network node 1000 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 1008. As a further example, the power source 1008 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.

[0126] Embodiments of the network node 1000 may include additional components beyond those shown in Figure 10 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 1000 may include user interface equipment to allow input of information into the network node 1000 and to allow output of information from the network node 1000. This may allow a user to perform diagnostic, maintenance, repair, and other administrative functions for the network node 1000.

[0127] Figure 11 is a block diagram illustrating a virtualization environment 1100 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 include virtualizing 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 1100 hosted by one or more of hardware nodes, such as a hardware computing device that operates as an access network node, UE, core network node, or host. Further, in embodiments in which a virtual node does not require radio connectivity (e.g., a core network node or host), then the node may be entirely virtualized. In some embodiments, the virtualization environment 1100 includes components defined by the O-RAN Alliance, such as an O-Cloud environment orchestrated by a Service Management and Orchestration Framework via an O-2 interface.

[0128] Applications 1102 (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.

[0129] Hardware 1104 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 1106 (also referred to as hypervisors or virtual machine monitors (VMMs)), provide VM 1108A and VM 1108B (which may be collectively referred to as VMs 1108), and / or perform any of the functions, features and / or benefits described in relation with some embodiments described herein. The virtualization layer 1106 may present a virtual operating platform that appears like networking hardware to one or more of the VMs 1108.

[0130] The VMs 1108 comprise virtual processing, virtual memory, virtual networking or interface and virtual storage, and may be run by virtualization layer 1106. Different embodiments of the instance of a virtual appliance 1102 may be implemented on one or more of VMs 1108, 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.

[0131] In the context of NFV, each of the VMs 1108 may be a software implementation of a physical machine that runs programs as if they were executing on a physical, nonvirtualized machine. Each of the VMs 1108, and that part of hardware 1104 that executes that VM, be it hardware dedicated 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 of the VMs 1108 on top of the hardware 1104 and corresponds to an application 1102.

[0132] Hardware 1104 may be implemented in a standalone network node with generic or specific components. Hardware 1104 may implement some functions via virtualization. Alternatively, hardware 1104 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 1110, which, among others, oversees lifecycle management of applications 1102. In some embodiments, hardware 1104 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 1112 which may alternatively be used for communication between hardware nodes and radio units.

[0133] 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 more operations 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.

[0134] 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

CLAIMS1 . A computer-implemented method (200) to utilize integrated sensing and communication, ISAC, data to enhance beam prediction in a communication network, the method comprising: obtaining (201 ), for each user equipment, UE, connected to an access point, ISAC data and UE position data; passing (203) the ISAC data and the UE position data through a multimodal transformer encoder; translating (205) the output of the multimodal transformer encoder into an output of dimension M corresponding to the number of beamforming vectors by passing the output of the multimodal transformer encoder through a linear neural network, where the output of the linear neural network is a predicted reward for each of the M possible beamforming vectors; using (207) the predicted rewards for each UE, train multi-agent contextual bandits to select beamforming vectors for the UEs, where the state-space for the multi-agent contextual bandits comprises the ISAC data and UE location data, and the action space comprises the M beamforming vectors.

2. The method (200) according to claim 1 , wherein obtaining UE position data comprises: aligning camera images from the access point with LiDAR data to pinpoint UE positions; or using location data obtained from the access point.

3. The method (200) according to claim 1 or 2, wherein the ISAC data is transformed into a 2D representation of the environment.

4. The method (200) according to any one of claims 1-3, wherein the obtained ISAC data is preprocessed (202) by using a convolutional neural network to extract features and patterns from the obtained data.

5. The method (200) according to claim 4, wherein the extracted features and patterns are further compressed to reduce dimensionality.

6. The method (200) of any one of claims 1-5 wherein the target of the multiagent contextual bandits is to select beams cooperatively to maximize the sum spectral efficiency as set out herein.

7. The method (200) of any one of claims 1-6, wherein the trained model is transferred using transfer reinforcement learning to an environment different from the environment the trained model was trained on.

8. The method (200) of claim 7, wherein in the transferred model is refined by a single epoch of training on data from the environment the model was transferred to.

9. An access point (710A, 710B, 810A, 810B, 810C, 810D, 1000) in a communication network (700, 800), the access point comprising a processor and a memory, the access point configured to: obtain, for each user equipment, UE, connected to the access point, integrated sensing and communication, ISAC, data and UE position data; pass the ISAC data and the UE position data through a multimodal transformer encoder; translate the output of the multimodal transformer encoder into an output of dimension M corresponding to the number of beamforming vectors by passing the output of the multimodal transformer encoder through a linear neural network wherethe output of the linear neural network is a predicted reward for each of the M possible beamforming vectors; use the predicted rewards for each UE to train multi-agent contextual bandits to select beamforming vectors for the UEs, where the state-space for the multi-agent contextual bandits comprises the ISAC data and UE location data, and the action space comprises the M beamforming vectors.

10. The access point (710A, 71 OB, 810A, 81 OB, 810C, 810D, 1000) according to claim 9, wherein the access point is virtualized (1100) and the training is performed in the cloud.11 . The access point (710A, 710B, 810A, 810B, 810C, 810D, 1000) according to claim 9 or 10, further configured to perform a method according to any one of claims 2-8.

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