Spectrum sharing in massive multiple-input multiple-output (MIMO) networks

Abstract

A method, system, and apparatus for spectrum sharing in a massive multiple-input multiple-output (MIMO) network are disclosed. According to one aspect, there is provided a method in a secondary network node of a secondary network, the secondary network node being configured to communicate with a plurality of secondary users. The method includes performing channel estimation of a primary user of the primary network during a learning phase consistent with a training phase of the primary network. The method further includes determining a beamformer and a power allocation based at least in part on the channel estimate to maximize at least one of a weighted uplink data rate and a weighted downlink data rate under constraints with respect to a data rate of the primary network.

Description

Technical Field

[0001] The present disclosure relates to wireless communications, and in particular to spectrum sharing in massive Multiple-Input Multiple-Output (MIMO) networks. Background Art

[0002] The 3rd Generation Partnership Project (3GPP) has developed and is developing standards for fourth generation (4G) (also known as Long Term Evolution (LTE)) and fifth generation (5G) (also known as New Radio (NR)) wireless communication systems. Such systems provide, among other features, broadband communications between network nodes (such as base stations) and mobile wireless devices (WDs), as well as communications between network nodes and between WDs. Sixth generation (6G) wireless communication systems are also under development.

[0003] Underlay spectrum sharing (USS) is a promising technology in cognitive radio to address spectrum scarcity in communication systems. USS technology provides unlicensed networks (often referred to as secondary networks (SNs)) with concurrent access to the spectrum of licensed networks (often referred to as primary networks (PNs)) while guaranteeing the required quality of service (QoS) of the PNs.

[0004] USS technology uses massive multiple-input multiple-output (MIMO) technology, in which the network nodes of the network are equipped with a very large number of antennas. Massive MIMO technology allows the same time-frequency resources to be used to serve many users in the network, thereby improving the spectrum efficiency of the network.

[0005] Several studies have focused on taking advantage of massive MIMO technology in USS cognitive radio systems. In a known method, downlink (DL) communications of a system including multi-cell multi-user PN and single-cell multi-user SN are proposed. The system uses massive MIMO technology and matched filter (MF) precoding at the primary base station (PBS) and the secondary base station (SBS). The known method solves the problem of power allocation optimization problem (OP) at the SN by maximizing the DL sum rate of the SN while satisfying the quality of service (QoS) of each PU. The technology can only produce reliable performance when the number of PBS antennas and SBS antennas increases unrestrictedly at the same time. In addition, in this scheme, both PN and SN are required to share the channel state information (CSI) of their users in addition to the location of their users. In addition, using this method, the number of secondary users (SU) served by the SBS is limited to the number of inactive primary users (PU), resulting in a reduction in spectral efficiency in the SN.

[0006] A setup has been considered that includes a point-to-point PN and a multi-cell multi-user massive MIMO SN equipped with two SBSs in each cell. For such a system, the number of SUs served by the SBS is maximized through a DL scheduling strategy. The scheme relies on estimating the angular information of both the PU and the SU, and on UL (DL) training at the SBS (SU). The scheme may produce high latency in the SN, thereby reducing the SN spectral efficiency. In addition, deploying two SBSs in each cell increases the implementation cost of the network. Multi-user massive MIMO SNs and multiple pairs of PNs have been considered. Assuming imperfect CSI of the PU and SU at the SBS, the joint problem of power allocation and maximization of the number of SUs served by the SBS in DL transmission remains to be solved. A modified zero-forcing (MZF) has been considered that allows the SBS to operate in the spatial spectrum holes of the channel of the PUS, thereby protecting the PU.

[0007] Time division duplex (TDD) mode solutions have been considered. Relying on channel reciprocity, TDD leads to less training overhead than frequency division duplex (FDD) mode. However, due to pilot contamination, using TDD will cause more interference in the coexisting network. To solve this problem, reverse TDD (rTDD) is adopted, in which the coexisting networks operate synchronously in opposite transmission directions. In other words, unlike traditional TDD, when one network is in the UL phase, the other network is in the DL phase, and vice versa. The performance of TDD and rTDD schemes in a massive MIMO USS setting including multi-cell multi-user PN and multi-cell multi-user SN has been compared. To this end, assuming the imperfect CSI of the SU (PU) at the SBS (PBS), when both the PBS and the SBS adopt the traditional ZF beamformer, the UL and DL achievable rates of the PN and SN can be derived. Using the rTDD protocol, the SN performance and the PN performance are asymptotically independent. However, in this protocol, the design of PN parameters (such as the length of the UL / DL interval) depends on the SN parameters.

[0008] USS performance has been studied for UL and DL transmissions. In addition, some of the available solutions focus only on SN performance using the USS method. A known solution depends on channel estimation errors. In addition, in this solution, PN parameters (such as the length of the UL / DL interval) depend on SN parameters. In fact, PN operating parameters must be changed by changing SN parameters, thereby placing a heavy burden on PN in practice. In addition, in some known methods, PN performance is significantly reduced in the presence of SN because they do not fully protect PN. In addition, although PN QoS is met, existing methods in traditional USS cannot fully protect SN performance from interference caused by PN. In addition, existing methods require a large amount of cooperation between PN and SN to obtain PBSCSI, location, allocated power, etc. The cooperation required between PN and SN is impractical for the implementation of using USS technology. Regarding all the above-mentioned points, the PN and SN performance in both UL and DL has not yet been studied for the purpose of protecting PN from the influence of SN presence and vice versa. Summary of the invention

[0009] Some embodiments advantageously provide methods and network nodes for spectrum sharing in massive multiple-input multiple-output (MIMO) networks.

[0010] In some embodiments, the performance of both the primary network (PN) and the secondary network (SN) in both uplink (UL) and downlink (DL) are considered in order to protect the PN from the presence of the SN and vice versa.

[0011] Some embodiments address the problem of underlay spectrum sharing (USS) in a MIMO secondary network for accessing the same licensed spectrum of a multi-user massive MIMO primary network. It is assumed that both the primary network and the secondary network are equipped with beamforming technology and they both provide services to single antenna users. The beamforming and power allocation scheme of the secondary network in DL and UL can be configured to maximize the weighted achievable uplink and downlink sum rate of the secondary network while at least partially protecting the primary network from the presence of the secondary network. A solution is disclosed that utilizes massive MIMO technology to enable the secondary network to operate in the spatial spectrum holes of the primary network to protect the secondary network from the interference of the primary network. To this end, a zero-forcing beamformer can be used at the secondary base station. More specifically, a solution is disclosed in which, unlike known solutions, the data rates of the primary network and the secondary network are not highly dependent on the channel estimation error. To this end, a structure of a communication frame for the secondary network is used that includes a learning phase. In the learning phase, the nodes of the secondary network do not transmit and listen to the primary user for the purpose of estimating the channel of the primary user. The SN frame avoids pilot pollution between the primary network and the secondary network while minimizing the communication exchange between the primary network and the secondary network. In addition, to further mitigate the interference between the two networks, reverse time division duplex (rTDD) can be adopted and is referred to as scenario B. The performance of rTDD is compared with the performance of traditional time division duplex (TDD) referred to as scenario A.

[0012] For both scenarios A and B above, the transmit and receive beamformers of the secondary network nodes can be jointly designed with the power allocation scheme of the secondary network in both the uplink and downlink phases. To this end, two optimization problems (corresponding to scenarios A and B) can be formulated to maximize the sum rate of the secondary network while considering the power budget of the nodes of the secondary network and trying to guarantee a given data rate for the primary user. The optimization problem corresponding to each scenario can be decomposed into two independent optimization problems: one corresponding to the uplink of the secondary network, and the other corresponding to the downlink of the secondary network.

[0013] In order to solve the optimization problem corresponding to the uplink of the secondary network in scenario A (TDD), the receiving beamformer at the secondary base station is configured so that the secondary base station can protect itself from the influence of the primary user. To this end, at the secondary base station, an improved zero-forcing beamformer is configured, which depends on the CSI of the primary user and the CSI of the secondary user. The power allocation scheme for the secondary user is determined by a computationally efficient convex optimization problem. For the downlink of the secondary network in scenario A, a zero-forcing beamforming method is used to configure the transmit beamformer of the secondary base station so that the secondary base station can protect the primary user while maximizing the achievable total rate of the SN. The secondary network can be configured to operate in the spatial spectrum hole of the primary network. Using the improved zero-forcing design, the power allocation strategy of the secondary base station can adopt a water injection algorithm.

[0014] In order to solve the optimization problem corresponding to the uplink of the secondary network in scenario B (rTDD), the receiving beamformer at the secondary base station can be configured so that the secondary base station can protect itself from the influence of the main base station. To this end, at the secondary base station, in addition to the channel between the secondary base station and all secondary users, a zero-forcing beamforming method can also be adopted, which relies on the knowledge of the subspace of the main base station-secondary base station channel matrix. As a result, the zero-forcing method does not require downlink training, resulting in a significant reduction in the training overhead of the main network and the secondary network. Therefore, the beamformer disclosed in this article can greatly improve the spectral efficiency of both the main network and the secondary network. The zero-forcing method disclosed in this article determines the power allocation scheme for secondary network users through a convex optimization problem. In the downlink of the secondary network in scenario B (using rTDD), a subspace-based zero-forcing is used for the transmit beamformer of the secondary base station, which enables the secondary base station to fully protect the main base station. Based on such a transmit beamformer, the optimal power allocation scheme for the secondary base station leads to a water injection algorithm.

[0015] In some embodiments, the data rate of a multi-user SN is maximized by using a USS scheme based on massive MIMO to exploit spatial spectrum holes of the multi-user PN. Some embodiments can achieve one or more of the following purposes:

[0016] The rTDD (scenario B) communication mode for SN achieves better performance in maximizing the sum rate of SN and PN when compared to the TDD (scenario A) mode. Unlike existing communication modes, the communication mode for SN mitigates the sensitivity of SN and PN performance to channel estimation errors. To achieve this, a learning phase at the SN can be employed;

[0017] An optimization problem (OP) for SN in both UL and DL is formulated to design the SBS beamformer and SN power allocation strategy by maximizing the SN data rate while guaranteeing PN QoS. The power budget of SN in OP is considered to achieve green communication;

[0018] The SN receive beamformer in both TDD and rTDD modes can be designed to enable the SBS to protect itself from interference caused by PN nodes;

[0019] SN transmit beamformers in both TDD and rTDD modes can be configured so that the SBS protects the PN node, thereby enabling the PN to operate independently of the presence of a secondary network. Employing these beamformers does not require an infinite number of antennas at the PBS and SBS as in some known approaches. Some embodiments are applicable to MIMO systems with a limited number of base station antennas. However, performance may improve as the number of network node antennas approaches infinity;

[0020] In order to obtain the CSI of the PN nodes at the SBS that can be used to design (configure) the beamformer, a solution to reduce the training cost of the PN and SN is proposed;

[0021] In some embodiments, USS techniques may be implemented with minimal cooperation between PNs and SNs; and / or

[0022] • In some embodiments, PN design parameters are not meant to be changed by changing SN design parameters such as the number of secondary users (SUs).

[0023] According to one aspect, a secondary network node in a secondary network is provided. The secondary network node includes processing circuitry configured to perform channel estimation for a primary user of the primary network during a learning phase consistent with a training phase of the primary network, and determine a beamformer and power allocation based at least in part on the channel estimation to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on a data rate of the primary network.

[0024] According to this aspect, in some embodiments, the processing circuit is further configured to perform reverse time division duplex rTDD. In some embodiments, determining the beamformer and the power allocation is based at least in part on information about a subspace of a channel matrix corresponding to a channel between the primary network and the secondary network. In some embodiments, determining the beamformer and the power allocation includes determining the power allocation based at least in part on performing a convex optimization process. In some embodiments, determining the power allocation includes performing a water filling process. In some embodiments, determining the beamformer and the power allocation is performed without downlink training. In some embodiments, maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is based at least in part on information about a spectrum hole of the primary network. In some embodiments, maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed under a first constraint on interference caused by a primary user of the primary network. In some embodiments, maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed under a second constraint on the number of antennas of the secondary network node being greater than the sum of the number of secondary users and the primary user. In some embodiments, the estimation and determination are performed without receiving channel information from the primary network node of the primary network.

[0025] According to another aspect, a method implemented in a secondary network node of a secondary network is provided. The secondary network node is configured to communicate with a plurality of secondary users. The method includes performing channel estimation for primary users of the primary network during a learning phase consistent with a training phase of the primary network. The method also includes determining a beamformer and a power allocation based at least in part on the channel estimation to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on a data rate of the primary network.

[0026] According to this aspect, in some embodiments, the method includes performing reverse time division duplexing rTDD. In some embodiments, determining the beamformer and the power allocation is based at least in part on information about a subspace of a channel matrix corresponding to a channel between the primary network and the secondary network. In some embodiments, determining the beamformer and the power allocation includes determining the power allocation based at least in part on performing a convex optimization process. In some embodiments, determining the power allocation includes performing a water filling process. In some embodiments, determining the beamformer and the power allocation is performed without downlink training. In some embodiments, maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is based at least in part on information about a spectrum hole of the primary network. In some embodiments, maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed under a first constraint on interference caused by a primary user of the primary network. In some embodiments, maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed under a second constraint on the number of antennas of the secondary network node being greater than the sum of the number of secondary users and the primary users. In some embodiments, the estimation and determination are performed without receiving channel information from the primary network node of the primary network. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] A more complete understanding of the present embodiments and attendant advantages and features will be more readily appreciated by reference to the following detailed description when considered in conjunction with the accompanying drawings, in which:

[0028] Figure 1 is a plot of the average achievable rate per user (APAR) of the primary network (PN) and the secondary network (SN) versus the total power allocated to the PN with perfect CSI and estimated CSI;

[0029] Figure 2 is a graph of uplink (UL) plus downlink (DL) APAR versus the number of antennas of the secondary base station for two scenarios A and B;

[0030] Figure 3 is a graph of the UL APAR of the PN versus the total power allocated to the SN in scenarios A and B;

[0031] Figure 4 is a graph of the relationship between the UL plus DL APAR and the total power of the PN for PN plus SN and for only PN for scenario B;

[0032] Figure 5 is a schematic diagram illustrating an example network architecture of a communication system connected to a host via an intermediate network according to the principles of the present disclosure;

[0033] Figure 6 is a block diagram of a host communicating with a wireless device via a network node through at least a partially wireless connection according to some embodiments of the present disclosure;

[0034] Figure 7 is a flow chart illustrating an example method for executing a client application at a wireless device implemented in a communication system including a host, a network node, and a wireless device according to some embodiments of the present disclosure;

[0035] Figure 8 is a flow chart illustrating an example method for receiving user data at a wireless device implemented in a communication system including a host, a network node, and a wireless device according to some embodiments of the present disclosure;

[0036] Fig. 9 is a flow chart illustrating an example method for receiving user data from a wireless device at a host, implemented in a communication system including a host, a network node, and a wireless device, according to some embodiments of the present disclosure;

[0037] Fig.10 is a flow chart illustrating an example method for receiving user data at a host implemented in a communication system including a host, a network node, and a wireless device according to some embodiments of the present disclosure;

[0038] Fig.11 is a flow chart of an example process in a network node for spectrum sharing in a massive multiple-input multiple-output (MIMO) network;

[0039] Fig.12 is a flow chart of another example process in a network node for spectrum sharing in a massive multiple-input multiple-output (MIMO) network;

[0040] Fig.13 is a communications network having a primary network and a secondary network; and

[0041] Fig.14 is a diagram of communication frames for scenario A and for scenario B. DETAILED DESCRIPTION

[0042] Before describing the example embodiments in detail, it should be noted that the embodiments reside primarily in combinations of device components and processing steps related to spectrum sharing in massive multiple-input multiple-output (MIMO) networks. Accordingly, components have been represented by conventional symbols in the drawings where appropriate, and only those specific details relevant to understanding the embodiments are shown so as not to obscure the disclosure with details that will be readily apparent to one of ordinary skill in the art having the benefit of the description herein. Like numbers represent like elements throughout the description.

[0043] As used herein, relational terms such as "first" and "second", "top" and "bottom", etc., may be used only to distinguish one entity or element from another entity or element, without necessarily requiring or implying any physical or logical relationship or order between such entities or elements. The terms used herein are only for the purpose of describing specific embodiments, and are not intended to limit the concepts described herein. As used herein, the singular forms of "a", "an" and "the" are also intended to include the plural forms, unless the context clearly indicates otherwise. It will also be understood that the terms "comprises" and / or "comprising", when used herein, specify the presence of stated features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0044] In the embodiments described herein, connection terms "in communication with..." and the like may be used to indicate electrical or data communication, which may be achieved, for example, by physical contact, induction, electromagnetic radiation, radio signal transmission, infrared signal transmission, or optical signal transmission. Those of ordinary skill in the art will appreciate that multiple components may interoperate, and modifications and variations in achieving electrical and data communication are possible.

[0045] In some embodiments described herein, the terms "coupled," "connected," and the like may be used herein to indicate a connection, although not necessarily a direct connection, and may include wired and / or wireless connections.

[0046] The term "network node" used herein may be any kind of network node included in a radio network, which may further include any of the following: base station (BS), radio base station, base transceiver station (BTS), base station controller (BSC), radio network controller (RNC), g Node B (gNB), evolved Node B (eNB or eNodeB), Node B, multi-standard radio (MSR) radio node (such as MSR BS), multi-cell / multicast coordination entity (MCE), integrated access and backhaul (IAB) node, relay node, donor node control relay, radio access point (AP), transmission point, transmission node, remote radio unit (RRU), remote radio head (RRH). Core network node (such as mobility management entity (MME), self-organizing network (SON) node, coordination node, positioning node, MDT node, etc.), external node (such as third-party node, node outside the current network), node in distributed antenna system (DAS), spectrum access system (SAS) node, element management system (EMS), etc. The network node may also include test equipment. As used herein, the term "radio node" may also be used to refer to a wireless device (WD), such as a wireless device (WD), or a radio network node.

[0047] In some embodiments, non-limiting terms wireless device (WD) or user equipment (UE) can be used interchangeably. WD herein can be any type of wireless device capable of communicating with a network node or another WD via a radio signal, such as a wireless device (WD). WD can also be a radio communication device, a target device, a device-to-device (D2D) WD, a machine type WD, or a WD capable of machine-to-machine communication (M2M), a low-cost and / or low-complexity WD, a sensor equipped with a WD, a tablet computer, a mobile terminal, a smart phone, a laptop embedded device (LEE), a laptop mounted device (LME), a USB dongle, a customer premises equipment (CPE), an Internet of Things (IoT) device, or a narrowband IoT (NB-IOT) device, etc.

[0048] Furthermore, in some embodiments, the general term "radio network node" is used. It may be any kind of radio network node, which may include any of the following: base station, radio base station, base transceiver station, base station controller, network controller, RNC, evolved Node B (eNB), Node B, gNB, multi-cell / multicast coordination entity (MCE), IAB node, relay node, access point, radio access point, remote radio unit (RRU), remote radio head (RRH).

[0049] Note that although terminology from one particular wireless system, such as 3GPP LTE and / or New Radio (NR), may be used in the present disclosure, this should not be viewed as limiting the scope of the present disclosure to only the above-mentioned systems. Other wireless systems, including but not limited to Wideband Code Division Multiple Access (WCDMA), Worldwide Interoperability for Microwave Access (WiMax), Ultra Mobile Broadband (UMB), and Global System for Mobile Communications (GSM), may also benefit from utilizing the ideas encompassed within the present disclosure.

[0050] Further note that the functions described herein as being performed by a wireless device or network node may be distributed across multiple wireless devices and / or network nodes. In other words, it is contemplated that the functions of the network nodes and wireless devices described herein are not limited to being performed by a single physical device and, in fact, may be distributed across several physical devices.

[0051] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those commonly understood by those of ordinary skill in the art to which the present disclosure belongs. It will be further understood that the terms used herein should be interpreted as having the same meaning as they have in the context of this specification and the relevant art, and will not be interpreted in an idealized or overly formal sense unless explicitly defined as such in this article.

[0052] Some embodiments provide spectrum sharing in massive multiple-input multiple-output (MIMO) networks.

[0053] A massive MIMO-based USS method is disclosed herein, which provides a low-complexity solution to the problem of maximizing the data rate of a secondary network (SN) while meeting a given data rate of a primary network (PN). The SN and PN performance in both the UL and DL phases are considered. The impact of the PN (SN) on the overall performance of the SN (PN) in each communication frame can also be considered. By maximizing the SN data rate, the beamformer of the SBS and the SN power allocation scheme are jointly designed. To this end, an SN communication frame including a learning phase is adopted. In the learning phase consistent with the training phase of the PN, the SN is quiet and listens to the PN to protect the PN training performance while obtaining the required information about the PN nodes (such as their CSI). Using this frame structure, pilot contamination between the PN and SN is avoided. Therefore, the PN and SN performance are slightly sensitive to channel estimation errors, such as Figure 1 In addition, utilizing the learning phase of the SN reduces the cooperation between the PN and the SN required to obtain the CSI of the PN node at the SN node.

[0054] In addition, in order to mitigate the interference between PN and SN, an improved rTDD mode is used, and its performance is compared with the improved TDD mode. Compared with known methods, TDD (referred to as scenario A) and rTDD (referred to as scenario B) modes include a learning phase. In order to implement scenario A, the SN can obtain the global CSI of the PN node, which may not be available in practice, or require DL training of the PN. In order to overcome this problem common in existing USS schemes, an approximation of the CSI is proposed in some embodiments. However, this approximation may result in a reduction in the SN data rate or solve an infeasible optimization problem. As disclosed herein, the implementation of scenario B does not require global CSI or approximation, and therefore does not suffer from infeasibility in the corresponding OP. Compared with scenario A and known methods, the implementation of substrate spectrum sharing in scenario B requires less cooperation between PN and SN, and lower training costs. This is due to the fact that in scenario B, the SN estimates the CSI without involving the PN in the training process and without any mathematical approximation or relaxation. In addition, as Figure 2 As shown, scenario B mostly performs better than scenario A in terms of maximizing the data rate of SN.

[0055] In order to design the receive and transmit beamformers of the SBS in these two scenarios, a method is disclosed that enables the SBS to operate in the spatial spectrum holes of the PN. In other words, the SBS is able to protect the PN nodes from the signals it transmits while protecting itself from the signals transmitted by the PN nodes. As a result, the PN performance is only slightly affected by changing the SN parameters. See Figure 3 More specifically, the beamformer in scenario B can also rely on the knowledge of subspaces of the SBS-PBS channel matrix in addition to the SBS-SU channel matrix. These subspace-based beamformers can alleviate the training overhead of both networks and increase the degrees of freedom available for SBS-SU communication.

[0056] Numerical results show that the data rate of the entire network (PN+SN) is higher than the data rate of a single network (PN only without SN). Figure 4 As shown, the overall data rate of the network can be improved by using the disclosed USS method.

[0057] Based on such a beamformer, the optimal power allocation scheme for SBS leads to a water-filling type algorithm or to solving a computationally efficient convex OP.

[0058] Compared to scenario A, scenario B achieves a higher data rate with the same consumed power, leading to green communications for 5G and beyond. Figure 1-4 The graphs are intended to illustrate examples of the relative performance advantages of given solutions.

[0059] Figure 5 , a schematic diagram of a communication system 10 according to an embodiment is shown, the communication system 10 is a 3GPP type cellular network that can support standards such as LTE and / or NR (5G), and includes an access network 12 (such as a radio access network) and a core network 14. The access network 12 includes a plurality of network nodes 16a, 16b, 16c (collectively referred to as network nodes 16), such as NBs, eNBs, gNBs or other types of wireless access points, each defining a corresponding coverage area 18a, 18b, 18c (collectively referred to as coverage area 18). Each network node 16a, 16b, 16c can be connected to the core network 14 via a wired or wireless connection 20. In some embodiments, the network node 16a can be in a primary network (PN) and is referred to herein as a primary base station (PBS) 16a. In some embodiments, the network node 16b can be in a secondary network (SN) and is referred to herein as a secondary base station (SBS) 16b. Note that the secondary network can be served by the same core network 14 as the primary network, or by a completely different core network 14.

[0060] A first wireless device (WD) 22a located in a coverage area 18a is configured to wirelessly communicate with or be paged by a corresponding network node 16a. A group of WD 22a can be served by PBS 16a and is referred to herein as a primary user (PU). A second WD 22b in a coverage area 18b wirelessly communicates with a corresponding network node 16b. Note that coverage area 18a and coverage area 18b can overlap at least partially. A group of WD 22b can be served by SBS 16a and is referred to herein as a secondary user (SU) 22b. Although multiple WDs 22a, 22b (collectively referred to as wireless devices 22) are shown in this example, the disclosed embodiments are equally applicable to situations where a separate WD is located in a coverage area or a separate WD is connected to a corresponding network node 16. Note that although only two WDs 22 and three network nodes 16 are shown for convenience, the communication system may include more WDs 22 and network nodes 16.

[0061] Furthermore, it is contemplated that the WD 22 may communicate with more than one network node 16 and more than one type of network node 16 simultaneously and / or be configured to communicate with more than one network node 16 and more than one type of network node 16, respectively. For example, the WD 22 may have dual connectivity with a network node 16 supporting LTE and the same or different network node 16 supporting NR. As an example, the WD 22 may communicate with an eNB for LTE / E-UTRAN and a gNB for NR / NG-RAN.

[0062] The communication system 10 itself can be connected to a host 24, which can be embodied in the hardware and / or software of a stand-alone server, a cloud-implemented server, a distributed server, or as a processing resource in a server farm. The host 24 can be under the ownership or control of a service provider, or can be operated by or on behalf of the service provider. The connections 26, 28 between the communication system 10 and the host 24 can extend directly from the core network 14 to the host 24, or can extend via an optional intermediate network 30. The intermediate network 30 can be one or a combination of more than one of a public, private, or managed network. The intermediate network 30, if any, can be a backbone network or the Internet. In some embodiments, the intermediate network 30 can include two or more subnetworks (not shown).

[0063] Figure 5The communication system as a whole implements a connection between one of the connected WDs 22a, 22b and the host 24. The connection can be described as an over-the-top (OTT) connection. The host 24 and the connected WDs 22a, 22b are configured to use the access network 12, the core network 14, any intermediate network 30 and possible additional infrastructure (not shown) as intermediaries to communicate data and / or signaling via the OTT connection. The OTT connection can be transparent in the following sense: at least some of the participating communication devices that the OTT connection passes through do not know the routes of the uplink and downlink communications. For example, the network node 16 may not be informed or need not be informed of the past routes of incoming downlink communications, which have data that will be forwarded (e.g., handed over) to the connected WD 22a from the host 24. Similarly, the network node 16 does not need to know the future routes of the outgoing uplink communications from the WD 22a toward the host 24.

[0064] The network node 16 is configured to include an optimization unit 32 configured to determine a beamformer and a power allocation to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on a data rate of a primary network, the determination of the beamformer and the power allocation being at least partially based on the channel estimation.

[0065] Now refer to Figure 6 An example implementation according to an embodiment of the WD 22, network node 16, and host 24 discussed in the previous paragraphs is described. In the communication system 10, the host 24 includes hardware (HW) 38, which includes a communication interface 40, which is configured to establish and maintain a wired or wireless connection with the interface of different communication devices of the communication system 10. The host 24 also includes a processing circuit 42 that may have storage and / or processing capabilities. The processing circuit 42 may include a processor 44 and a memory 46. In particular, in addition to or as an alternative to a processor (such as a central processing unit) and a memory, the processing circuit 42 may include an integrated circuit for processing and / or control, such as one or more processors and / or processor cores and / or an FPGA (field programmable gate array) and / or an ASIC (application-specific integrated circuit) suitable for executing instructions. The processor 44 may be configured to access (e.g., write to and / or read from) a memory 46, which may include any kind of volatile and / or non-volatile memory, such as cache and / or buffer memory and / or RAM (random access memory) and / or ROM (read only memory) and / or optical memory and / or EPROM (erasable programmable read-only memory).

[0066] Processing circuit 42 can be configured to control any method and / or process described herein, and / or to cause such method and / or process to be performed, for example, by host 24. Processor 44 corresponds to one or more processors 44 for performing the functions of host 24 described herein. Host 24 includes memory 46, which is configured to store data, programming software code, and / or other information described herein. In some embodiments, software 48 and / or host application 50 may include instructions that, when executed by processor 44 and / or processing circuit 42, cause processor 44 and / or processing circuit 42 to perform the processes described herein with respect to host 24. The instructions may be software associated with host 24.

[0067] The software 48 may be executable by the processing circuit 42. The software 48 includes a host application 50. The host application 50 may be operable to provide services to a remote user (such as a WD 22 connected via an OTT connection 52 terminated at the WD 22 and the host 24). In the process of providing services to the remote user, the host application 50 may provide user data sent using the OTT connection 52. "User data" may be data and information described herein as implementing the described functions. In one embodiment, the host 24 may be configured to provide control and functionality to the service provider, and may be operated by or on behalf of the service provider. The processing circuit 42 of the host 24 may enable the host 24 to observe, monitor, control, send and / or receive from the network node 16 and / or the wireless device 22 to the network node 16 and / or the wireless device 22.

[0068] The communication system 10 also includes a network node 16, which is disposed in the communication system 10 and includes hardware 58 that enables it to communicate with the host 24 and the WD 22. The hardware 58 may include a communication interface 60 for establishing and maintaining a wired or wireless connection to the interface of different communication devices of the communication system 10, and a radio interface 62 for establishing and maintaining at least a wireless connection 64 with the WD 22 located in the coverage area 18 served by the network node 16. The radio interface 62 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and / or one or more RF transceivers. The communication interface 60 may be configured to facilitate a connection 66 to the host 24. The connection 66 may be direct, or it may pass through the core network 14 of the communication system 10 and / or through one or more intermediate networks 30 external to the communication system 10.

[0069] In the illustrated embodiment, the hardware 58 of the network node 16 also includes a processing circuit 68. The processing circuit 68 may include a processor 70 and a memory 72. In particular, in addition to or as an alternative to a processor (such as a central processing unit) and a memory, the processing circuit 68 may include an integrated circuit for processing and / or control, for example, one or more processors and / or processor cores and / or an FPGA (field programmable gate array) and / or an ASIC (application-specific integrated circuit) adapted to execute instructions. The processor 70 may be configured to access the memory 72 (e.g., write to and / or read from the memory 72), and the memory 72 may include any kind of volatile and / or non-volatile memory, such as a cache and / or buffer memory and / or a RAM (random access memory) and / or a ROM (read-only memory) and / or an optical memory and / or an EPROM (erasable programmable read-only memory).

[0070] Therefore, the network node 16 also has software 74, which is stored internally, for example, in the memory 72, or stored in an external memory (e.g., a database, a storage array, a network storage device, etc.) that the network node 16 can access via an external connection. The software 74 can be executed by the processing circuit 68. The processing circuit 68 can be configured to control any method and / or process described herein, and / or cause such a method and / or process to be performed, for example, by the network node 16. The processor 70 corresponds to one or more processors 70 for performing the network node 16 functions described herein. The memory 72 is configured to store data, programming software code, and / or other information described herein. In some embodiments, the software 74 may include instructions that, when executed by the processor 70 and / or the processing circuit 68, cause the processor 70 and / or the processing circuit 68 to perform the process described herein with respect to the network node 16. For example, the processing circuit 68 of the network node 16 may include an optimization unit 32 configured to determine a beamformer and a power allocation to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to constraints regarding a data rate of the primary network, wherein determining the beamformer and the power allocation is based at least in part on the channel estimation.

[0071] The communication system 10 also includes the already mentioned WD 22. The WD 22 may have hardware 80 that may include a radio interface 82 configured to establish and maintain a wireless connection 64 with a network node 16 serving the coverage area 18 in which the WD 22 is currently located. The radio interface 82 may be formed as or may include, for example, one or more RF transmitters, one or more RF receivers, and / or one or more RF transceivers.

[0072] The hardware 80 of the WD 22 also includes a processing circuit 84. The processing circuit 84 may include a processor 86 and a memory 88. In particular, in addition to or as an alternative to a processor (such as a central processing unit) and a memory, the processing circuit 84 may include an integrated circuit for processing and / or control, for example, one or more processors and / or processor cores and / or FPGAs (field programmable gate arrays) and / or ASICs (application-specific integrated circuits) adapted to execute instructions. The processor 86 may be configured to access the memory 88 (e.g., write to and / or read from the memory 88), and the memory 88 may include any kind of volatile and / or non-volatile memory, such as a cache and / or buffer memory and / or a RAM (random access memory) and / or a ROM (read-only memory) and / or an optical memory and / or an EPROM (erasable programmable read-only memory).

[0073] Therefore, WD 22 may also include software 90, which is stored in, for example, a memory 88 at WD 22, or in an external memory (e.g., a database, a storage array, a network storage device, etc.) accessible by WD 22. Software 90 may be executed by processing circuit 84. Software 90 may include a client application 92. Client application 92 may be operable to provide services to human or non-human users via WD 22 with the support of host 24. In host 24, the executing host application 50 may communicate with the executing client application 92 via an OTT connection 52 terminated at WD 22 and host 24. In the process of providing services to users, client application 92 may receive request data from host application 50 and provide user data in response to the request data. OTT connection 52 may transmit both request data and user data. Client application 92 may interact with the user to generate the user data it provides.

[0074] The processing circuit 84 may be configured to control any method and / or process described herein, and / or to cause such a method and / or process to be performed, for example, by the WD 22. The processor 86 corresponds to one or more processors 86 for performing the WD 22 functions described herein. The WD 22 includes a memory 88 configured to store data, programming software code, and / or other information described herein. In some embodiments, the software 90 and / or the client application 92 may include instructions that, when executed by the processor 86 and / or the processing circuit 84, cause the processor 86 and / or the processing circuit 84 to perform the process described herein with respect to the WD 22.

[0075] In some embodiments, the internal workings of network node 16, WD 22, and host 24 may be as follows: Figure 6 shown, and independently, the surrounding network topology can be Figure 5topology.

[0076] exist Figure 6 In FIG. 5 , the OTT connection 52 is abstractly drawn to illustrate communication between the host 24 and the wireless device 22 via the network node 16, without explicitly mentioning any intermediate devices and the precise routing of messages via these devices. The network infrastructure can determine the routing, and the network infrastructure can be configured to hide the routing from the WD 22, or hide the routing from the service provider operating the host 24, or hide the routing from both. When the OTT connection 52 is active, the network infrastructure can also make decisions by which the network infrastructure dynamically changes the routing (e.g., based on load balancing considerations or reconfiguration of the network).

[0077] The wireless connection 64 between the WD 22 and the network node 16 is in accordance with the teachings of the embodiments described throughout this disclosure. One or more of the various embodiments improves the performance of OTT services provided to the WD 22 using the OTT connection 52, in which the wireless connection 64 may form the last leg. More specifically, the teachings of some of these embodiments may improve data rates, latency, and / or power consumption, and thereby provide benefits such as reduced user waiting time, relaxed restrictions on file size, better responsiveness, extended battery life, and the like.

[0078] In some embodiments, the measurement process may be provided for the purpose of monitoring data rates, delays, and other factors that are improved by one or more embodiments. There may also be an optional network function for reconfiguring the OTT connection 52 between the host 24 and the WD 22 in response to changes in the measurement results. The measurement process and / or network function for reconfiguring the OTT connection 52 may be implemented in the software 48 of the host 24 or the software 90 of the WD 22, or in both. In an embodiment, a sensor (not shown) may be deployed in or associated with a communication device through which the OTT connection 52 passes; the sensor may participate in the measurement process by supplying the value of the monitoring quantity exemplified above, or supplying the value of other physical quantities of the monitoring quantity according to which the software 48, 90 calculates or estimates the monitoring quantity. The reconfiguration of the OTT connection 52 may include message formats, retransmission settings, preferred routes, etc.; the reconfiguration does not need to affect the network node 16, and it may be unknown or imperceptible to the network node 16. Some such processes and functions may be known and practiced in the art. In some embodiments, the measurements may involve proprietary WD signaling that facilitates the host 24's measurement of throughput, propagation time, latency, etc. In some embodiments, the measurements may be implemented in such a way that the software 48, 90 causes a message (particularly a null message or 'dummy' message) to be sent using the OTT connection 52 while the software 48, 90 monitors propagation time, errors, etc.

[0079] Thus, in some embodiments, the host 24 includes a processing circuit 42 configured to provide user data, and a communication interface 40 configured to forward the user data to a cellular network for transmission to the WD 22. In some embodiments, the cellular network also includes a network node 16 having a radio interface 62. In some embodiments, the network node 16 is configured to and / or the processing circuit 68 of the network node 16 is configured to perform the functions and / or methods described herein for preparing / initiating / maintaining / supporting / terminating transmissions to the WD 22, and / or preparing / terminating / maintaining / supporting / terminating reception of transmissions from the WD 22.

[0080] In some embodiments, host 24 includes processing circuitry 42 and communication interface 40 configured to receive user data originating from a transmission from WD 22 to network node 16. In some embodiments, WD 22 is configured to perform and / or includes a radio interface 82 and / or processing circuitry 84 configured to perform the functions and / or methods described herein for preparing / initiating / maintaining / supporting / terminating transmissions to network node 16 and / or preparing / terminating / maintaining / supporting / terminating reception of transmissions from network node 16.

[0081] although Figure 5 and Figure 6 Various "units" such as optimization unit 32 are shown as being located within a corresponding processor, but it is conceivable that these units can be implemented so that a portion of the unit is stored in a corresponding memory within the processing circuit. In other words, the unit can be implemented in hardware or a combination of hardware and software within the processing circuit.

[0082] Figure 7 is a diagram showing a communication system (such as Figure 5 and Figure 6 The communication system may include a host 24, a network node 16, and a WD 22, which may be a reference Figure 6Those described in the present disclosure. In a first step of the method, the host 24 provides user data (box S100). In an optional sub-step of the first step, the host 24 provides the user data by executing a host application (such as the host application 50) (box S102). In a second step, the host 24 initiates a transmission carrying the user data to the WD 22 (box S104). In an optional third step, in accordance with the teachings of the embodiments described throughout the present disclosure, the network node 16 sends the user data carried in the transmission initiated by the host 24 to the WD 22 (box S106). In an optional fourth step, the WD 22 executes a client application associated with the host application 50 executed by the host 24, such as the client application 92 (box S108).

[0083] Figure 8 is a diagram showing a communication system (such as Figure 5 The communication system may include a host 24, a network node 16, and a WD 22, which may be a reference Figure 5 and Figure 6 Those described herein. In a first step of the method, host 24 provides user data (block S110). In an optional sub-step (not shown), host 24 provides user data by executing a host application (such as host application 50). In a second step, host 24 initiates a transmission carrying user data to WD 22 (block S112). According to the teachings of the embodiments described throughout this disclosure, the transmission can be transmitted via network node 16. In an optional third step, WD 22 receives the user data carried in the transmission (block S114).

[0084] Fig. 9 is a diagram showing a communication system (such as Figure 5 The communication system may include a host 24, a network node 16, and a WD 22, which may be a reference Figure 5 and Figure 6Those described. In the optional first step of the method, WD 22 receives input data provided by host 24 (box S116). In the optional sub-step of the first step, WD 22 executes client application 92, which provides user data in response to the received input data provided by host 24 (box S118). Additionally or alternatively, in the optional second step, WD 22 provides user data (box S120). In the optional sub-step of the second step, WD provides user data by executing a client application (such as client application 92) (box S122). In the process of providing user data, the executed client application 92 can also consider user input received from the user. Regardless of the specific manner in which the user data is provided, WD22 can initiate the transmission of user data to host 24 in an optional third sub-step (box S124). In the fourth step of the method, according to the teachings of the embodiments described throughout the present disclosure, host 24 receives user data sent from WD 22 (box S126).

[0085] Fig.10 is a diagram showing a communication system (such as Figure 5 The communication system may include a host 24, a network node 16, and a WD 22, which may be a reference Figure 5 and Figure 6 In an optional first step of the method, in accordance with the teachings of the embodiments described throughout the present disclosure, the network node 16 receives user data from the WD 22 (block S128). In an optional second step, the network node 16 initiates a transmission of the received user data to the host 24 (block S130). In a third step, the host 24 receives the user data carried in the transmission initiated by the network node 16 (block S132).

[0086] Fig.11Flowchart of an example process in a network node 16 for spectrum sharing in a massive multiple-input multiple-output (MIMO) network. One or more of the blocks described herein may be performed by one or more elements of the network node 16, such as by one or more of the processing circuit 68 (including the optimization unit 32), the processor 70, the radio interface 62, and / or the communication interface 60. The network node 16 (such as via the processing circuit 68 and / or the processor 70 and / or the radio interface 62 and / or the communication interface 60) is configured to estimate a channel of a primary user of the primary network during a learning phase consistent with a training phase of the primary network (block S134). The process also includes determining a beamformer and a power allocation to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on a data rate of the primary network, the determination of the beamformer and the power allocation being based at least in part on the channel estimate (block S136).

[0087] In some embodiments, the process further comprises performing reverse time division duplex rTDD. In some embodiments, determining the beamformer and the power allocation is based at least in part on information about a subspace of a channel matrix corresponding to a channel between the primary network and the secondary network. In some embodiments, determining the beamformer and the power allocation comprises determining the power allocation based at least in part on performing a convex optimization process. In some embodiments, determining the power allocation comprises performing a water filling process. In some embodiments, determining the beamformer and the power allocation is performed without downlink training. In some embodiments, maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is based at least in part on information about a spectrum hole of the primary network. In some embodiments, maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed under a constraint of interference caused by a primary user of the primary network. In some embodiments, maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed under a constraint that the number of antennas of the secondary network node is greater than the sum of the number of secondary users and the primary users. In some embodiments, the estimation and determination are performed without receiving channel information from the primary network node of the primary network.

[0088] Fig.12Flowchart of an example process in a network node 16 for spectrum sharing in a massive multiple-input multiple-output (MIMO) network. One or more of the blocks described herein may be performed by one or more elements of the network node 16, such as by one or more of the processing circuit 68 (including the optimization unit 32), the processor 70, the radio interface 62, and / or the communication interface 60. The network node 16 (such as via the processing circuit 68 and / or the processor 70 and / or the radio interface 62 and / or the communication interface 60) is configured to perform channel estimation of primary users of the primary network during a learning phase consistent with a training phase of the primary network (block S138). The process also includes determining a beamformer and a power allocation based at least in part on the channel estimation to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on the data rate of the primary network (block S140).

[0089] Having described the general process flow of the arrangements of the present disclosure and having provided examples of hardware and software arrangements for implementing the processes and functions of the present disclosure, the following section provides details and examples of arrangements for spectrum sharing in massive multiple-input multiple-output (MIMO) networks.

[0090] An example of an embodiment of a communication network configured according to the principles disclosed herein is provided in Fig.13 , where:

[0091] Single-cell PN and single-cell SN operate simultaneously in the same RF band;

[0092] PN includes:

[0093] οN p A single antenna PU 22a;

[0094] ο has a representation of M p (M p >>N p ) of a large number of antennas PBS 16a;

[0095] SN includes:

[0096] οN s A single antenna SU 22b;

[0097] ο has a representation of M s (M s >>N s +N p ) of the SBS 16b with a large number of antennas: This constraint enables the SBS 16b to have sufficient degrees of freedom to serve the SU 22b by operating in the spatial spectrum holes of the PN;

[0098] Both PBS 16a and SBS 16b use beamforming technology to serve their own users;

[0099] PBS 16a provides information about the pilot of the PU to SBS 16b;

[0100] Assume that the UL and DL channels in the two networks are reciprocal;

[0101] The channel is frequency-flat fading;

[0102] • In each communication frame, the number of PUs 22a and SUs 22b may be different in the UL and DL phases. As mentioned above, the coverage areas of PNs and SNs may at least partially overlap.

[0103] Communication frame structure

[0104] Consider F coherent communication time frames, where it is assumed that the channels of all possible links of the two networks are semi-static within the frame period. Using these coherent frames, the estimation of the required CSI at the SN node can be improved. At each frame, the TDD communication mode in both PN and SN is considered. In the PN network, each communication frame includes three modes, namely training mode, UL mode and DL mode. It is assumed that SN knows the PN communication time frame and therefore aligns its time frame with the time frame of PN. Consider two different network coexistence scenarios, namely scenario A (improved TDD mode) and scenario B (improved rTDD mode), as shown in Figure 2. Fig.14 The training phase of both networks is performed in UL mode, which means that the PU 22a and SU 22b send pilots to their corresponding base stations, and then the PBS 16a and SBS 16b estimate the user's channel. Fig.14 As shown, when the PN is in training mode ("T"), the SN remains quiet, that is, the SN is in a learning phase ("L"). The learning phase can be to avoid pilot contamination between the two networks and enable the SBS 16b to estimate the channel of the SU 22b and the channel of the PU 22a of the PN node without the cooperation of the PN. Therefore, the training in the SN can be consistent with the data exchange in the PN. Therefore, in both scenarios, the SN training is performed during the PN UL interval. In scenario A, the SN UL follows the SN training, and the SN DL is consistent with the PN DL. In scenario B, the SN DL occurs immediately after the SN training, and the SN UL is performed during the PN DL. Communication schemes for these two scenarios are disclosed herein.

[0105] Assumptions

[0106] a) It is assumed that there is no CSI exchange between PN and SN. In other words, each network is responsible for its CSI acquisition. The required CSI is obtained at the SN node during the learning phase;

[0107] b) Without loss of generality, it is assumed that the PBS 16a employs a conventional ZF beamformer in both the UL and DL phases. However, it is worth mentioning that the method may also be employed for other beamforming techniques used by the PN;

[0108] c) PN performs training and designs its parameters, such as beamformer and power allocated to PU 22a, without considering the presence of SN. Therefore, the presence of SN can have as little impact on PN operation as possible;

[0109] d) The PN provides the SN with the total power budget and the individual power budgets of the PN nodes. In addition, the PN shares the required QoS of its nodes to be guaranteed with the SN.

[0110] e) To control the interference caused by SU 22b to PBS 16a during the SN training phase, it is assumed that the training power of SU 22b may be different from (usually lower than) its UL power. In fact, some embodiments may not be affected by the SN training technique.

[0111] Optimization Problem and Example Method in Scenario A

[0112] The performance of scenario A (TDD) in maximizing the total UL and DL data rates of the SN can be examined while considering the power budget of the SN nodes (SBS 16b and SU 22b) and meeting the given data rate of PU 22a. The total data rate of the SN is defined in this article as a weighted combination of the SN UL and DL sum rates. Denoted as the sum rate of SN in UL and DL respectively, the optimization problem (OP) considered in scenario A can be formulated as follows:

[0113] Optimization Problem(1)

[0114]

[0115] stC1:0≤p 2,k ≤p 2,max , for k=1,2,...,N s

[0116] C2:R 1，l (W1,p1,p2)≥r 1,l , for l=1,2,...,N P

[0117]

[0118] in:

[0119] β and are two positive constant coefficients and may be selected depending on whether SN UL or SN DL has greater priority;

[0120] W2 and are the beamforming matrices adopted by the SBS 16b in the UL and DL phases, respectively;

[0121] W1 is the ZF beamforming matrix used by PBS 16a in both UL and DL phases;

[0122] in:

[0123] · and are the power adjustment vectors of SU22b in the UL and DL phases respectively. 2，k and are the power transmitted by and allocated to the kth SU in UL and DL, respectively;

[0124]

[0125] p1 and are the power adjustment vectors of PU 22a in UL and DL phases, respectively;

[0126] ·R 1，l and are the data rates of the first PU 22a in the UL and DL phases, respectively. 1，l (t) and are respectively represented as the signal to interference plus noise ratio of the l-th PU 22a in UL and DL,

[0127] Where N up and N dp denote the length of the UL and DL intervals of the PN respectively, and:

[0128] ·r 1，l and are the given data rates that should be met by the SN for the l-th PU 22a in the UL and DL phases, respectively;

[0129] ·p 2，max and is the maximum transmit power of each SU and the total transmit power of the SBS 16b in DL; and

[0130] · is the unit-norm beamforming weight vector used by the SBS 16b to serve the kth SU in the DL.

[0131] In OP(1), constraints C1, C3, C4 and C6 are considered with respect to the SN power budget. In addition, constraints C2 and C5 are considered to ensure that the SN meets the required PN QoS. The optimization problem of the sum of UL and DL rates can be divided into two independent optimization problems as follows:

[0132] Optimization Problem (2)

[0133]

[0134] stC1: 0≤p 2,k ≤p 2,max , for k=1,2,...,N s

[0135] C2: R 1,l (W1,p1,p2)≥r 1,l , for l=1,2,...,N P (2)

[0137] Optimization Problems(3)

[0138]

[0139] SN UL: Solving OP (2) corresponding to the UL is challenging in practice because it requires knowledge of the effective channel coefficients of the PBS-SU 22b link. In order to obtain such knowledge, DL training can be performed by the PBS 16a to increase the cooperation of the PN and the SN. Such DL training will reduce the PN spectral efficiency. To address this problem, an approximation to the required CSI can be obtained by utilizing a learning phase. This approximation may cause the problem to be infeasible. To overcome this problem, the constraints on the PU data rate can be tightened. In order to have a computationally affordable solution while maximizing the SN UL sum rate, a modified ZF beamformer is used so that the SBS 16b can invalidate the signal of the PU 22a. Although this ZF type beamformer has been considered for DL ​​transmission, a modified zero forcing (MZF) can also be considered here for UL transmission. The advantage of the MZF is that it enables the SBS 16b to protect itself from interference caused by the PU 22a. Using the MZF, the optimal transmit power of the SU 22b can be found. An example solution for SN UL OP(2) in scenario A is summarized in Algorithm I as follows.

[0140] Algorithm I: Solving SN UL OP in scenario A

[0141] 1) Obtain the CSI of the SBS-PU link during the learning phase;

[0142] 2) Approximate the effective CSI of the PBS-SU link during the learning phase;

[0143] 3) Obtain the CSI of the SBS-SU link during the SN training phase;

[0144] 4) Connect the SBS-SU and SBS-PU channel vectors into a matrix;

[0145] 5) Calculate the traditional ZF based on the matrix obtained in step 4;

[0146] 6) Select the first N of the matrix obtained in step 5 s List;

[0147] 7) Insert the output of steps 2 and 6 into OP(2);

[0148] 8) Increase the PU rate threshold r 1，l and Until there is a feasible set of OPs for step 7; and / or

[0149] 9) Use the interior point method to numerically solve the convex OP in step 7.

[0150] SN DL: Solving OP (3) is challenging in practice because it requires knowledge of the effective channel coefficients of the PBS-PU link. In the case where these coefficients are known at PBS 16a, these coefficients may not be assumed to be known at SBS 16b in practice. Therefore, constraint C5 can be tightened so that SBS 16b fully protects PU 22a as an alternative to guaranteeing their given data rate. This tightened constraint enables SBS 16b to operate in the spatial spectrum holes of PN. To this end, a ZF type beamformer can be adopted so that SBS 16b can maximize the service to SU 22b while protecting PU 22a. In order to have a reliable power allocation scheme, the DL beamforming weights can be configured with unit power. By obtaining the beamformer in OP (3), an optimal power allocation strategy using a traditional water filling algorithm can be adopted. Based on this solution, SBS 16b can allocate more power to SU 22b with a stronger channel, resulting in the maximum achievable total rate of the network. The solution to the SN DL OP in scenario A is summarized in Algorithm II.

[0151] Algorithm II: Solving SN DL OP in Scenario A

[0152] 1) Obtain the CSI of the SBS-PU link during the learning phase;

[0153] 2) Obtain the CSI of the SBS-SU link during the SN training phase;

[0154] 3) Calculate the co-kernel of the SBS-PU channel matrix;

[0155] 4) Multiply the Hermitian conjugate of the SBS-SU channel matrix from the right by the output of step 3;

[0156] 5) Calculate the pseudo-inverse matrix of the matrix obtained in step 4;

[0157] 6) Multiply the outputs of steps 3 and 5;

[0158] 7) Normalizing the columns of the obtained matrix;

[0159] 8) Insert the output of step 7 into OP(3); and / or

[0160] 9) Solve the convex OP of step 8 by water injection algorithm.

[0161] Optimization Problem and Example Method in Scenario B

[0162] Similar to scenario A, the performance of scenario B can be analyzed in terms of maximizing the total data rate of the SN while considering the SN power budget and satisfying the given data rate of PU 22a. The OP in scenario B can be formulated as:

[0163] Optimization Problems(4)

[0164]

[0165] In OP(4), constraints C1, C3, C4 and C6 are considered with respect to the SN power budget. In addition, constraints C2 and C5 are considered to ensure that the SN meets the required PN QoS. The optimization problem of the sum of UL and DL rates can be divided into two independent optimization problems as follows:

[0166] Optimization Problems(5)

[0167]

[0168] Optimization Problems(6)

[0169]

[0170] SN UL: Solving OP (5) poses some challenges in practice because it requires knowledge of the effective channel coefficients between the SBS antennas and the PBS antennas. To obtain this knowledge, the MIMO channel between SBS 16b and PBS 16a can be estimated at SBS 16b. However, PBS 16a does not intend to perform any DL training. In addition, PBS 16a should provide knowledge of its beamformer to SBS 16b, which puts an additional undesirable burden on PN. To address this problem, the SBS 16b beamformer is configured to eliminate the interference caused by PBS 16a to SBS 16b. To this end, the column space of the MIMO channel matrix is ​​used instead of the entire matrix. The UL beamforming matrix can be configured by avoiding the column space in the MIMO channel matrix. This subspace can be estimated during the learning phase. This ZF-type beamforming method not only reduces the required cooperation of PN with SN, but also maximizes the achievable sum rate of SN by increasing the degrees of freedom of SBS. Using the beamforming matrix, the optimal transmit power of SU 22b can be found by convex OP. Our solution to SN UL OP in scenario B is summarized in Algorithm III.

[0171] Algorithm III: Solving SN UL OP in scenario B

[0172] 1) Obtaining the subspace of the MIMO channel matrix during the learning phase;

[0173] 2) Obtain the CSI of the SBS-SU link during the SN training phase;

[0174] 3) Calculate the residual kernel of the output of step 1;

[0175] 4) Multiply the Hermitian conjugate of the SBS-SU channel matrix by the output of step 3 from the right side;

[0176] 5) Calculate the pseudo-inverse matrix of the matrix obtained in step 4;

[0177] 6) Multiply the outputs of steps 3 and 5;

[0178] 7) Insert the output of step 6 into OP(5); and / or

[0179] 8) Use the interior point method to numerically solve the convex OP in step 7.

[0180] Comparing Algorithms I and III of the UL phase of SN, scenario B outperforms scenario A in terms of computational complexity and mathematical tractability.

[0181] SN DL: To solve OP(6), the same challenges as in OP(5) are proposed. Therefore, similar to UL, subspace-based ZF beamformers are used for DL. However, to achieve a reliable power adjustment scheme, the DL beamformers are configured with unit power. Using these beamformers, the SN DL power allocation scheme can be optimally found by a fast water filling algorithm. The solution to the SN DL OP in scenario B is summarized in Algorithm IV.

[0182] Algorithm IV: Solving SN DL OP in Scenario B

[0183] 1) Obtaining the subspace of the MIMO channel matrix during the learning phase;

[0184] 2) Obtain the CSI of the SBS-SU link during the SN training phase;

[0185] 3) Calculate the residual kernel of the output of step 1;

[0186] 4) Multiply the Hermitian conjugate of the SBS-SU channel matrix by the output of step 3 from the right side;

[0187] 5) Calculate the pseudo-inverse matrix of the matrix obtained in step 4;

[0188] 6) Multiply the outputs of steps 3 and 5;

[0189] 7) Normalizing the columns of the obtained matrix;

[0190] 8) Insert the output of step 7 into OP(6); and / or

[0191] 9) Solve the convex OP of step 8 by water injection algorithm.

[0192] Some embodiments may include one or more of the following:

[0193] Embodiment A1. A secondary network node in a secondary network, the secondary network node being configured to communicate with a plurality of secondary users, the secondary network node being configured to perform the following operations and / or comprising a radio interface and / or comprising a processing circuit configured to perform the following operations:

[0194] estimating a channel of a primary user of the primary network during a learning phase coincident with a training phase of the primary network; and

[0195] A beamformer and power allocation are determined to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on a data rate of a primary network, the beamformer and power allocation being determined based at least in part on the channel estimate.

[0196] Embodiment A2. The secondary network node of Embodiment A1, wherein the secondary network node, the radio interface, and / or the processing circuitry is further configured to perform reverse time division duplexing (rTDD).

[0197] Embodiment A3. The secondary network node of any of Embodiments A1 and A2, wherein determining a beamformer and power allocation is based at least in part on information about a subspace of a channel matrix corresponding to a channel between the primary network and the secondary network.

[0198] Embodiment A4. The secondary network node of any of Embodiments A1-A3, wherein determining a beamformer and a power allocation comprises determining a power allocation based at least in part on performing a convex optimization process.

[0199] Embodiment A5. The secondary network node of Embodiment A4, wherein determining power allocation includes performing a water filling process.

[0200] Embodiment A6. The secondary network node of any of Embodiments A1-A5, wherein determining beamformer and power allocation is performed without downlink training.

[0201] Embodiment A7. The secondary network node of any of Embodiments A1-A6, wherein maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is based at least in part on information about spectrum holes of the primary network.

[0202] Embodiment A8. The secondary network node of any one of Embodiments A1-A7, wherein maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed subject to constraints regarding interference caused by primary users of the primary network.

[0203] Embodiment A9. A secondary network node as described in any one of Embodiments A1-A8, wherein maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed under the constraint that the number of antennas of the secondary network node is greater than the sum of the number of secondary users and primary users.

[0204] Embodiment A10. The secondary network node of any one of Embodiments A1-A9, wherein the estimating and determining are performed without receiving channel information from a primary network node of the primary network.

[0205] Embodiment B1. A method implemented in a secondary network node of a secondary network, the secondary network node being configured to communicate with a plurality of secondary users, the method comprising:

[0206] During a learning phase coincident with a training phase of the primary network, estimating a channel of a primary user of the primary network; and

[0207] A beamformer and power allocation are determined to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on a data rate of a primary network, the beamformer and power allocation being determined based at least in part on the channel estimate.

[0208] Embodiment B2. The method of embodiment B1 further comprises performing reverse time division duplexing (rTDD).

[0209] Embodiment B3. The method of any of Embodiments B1 and B2, wherein determining the beamformer and power allocation is based at least in part on information about a subspace of a channel matrix corresponding to a channel between the primary network and the secondary network.

[0210] Embodiment B4. The method of any one of Embodiments B1-B3, wherein determining the beamformer and the power allocation comprises determining the power allocation based at least in part on performing a convex optimization process.

[0211] Embodiment B5. The method of embodiment B4, wherein determining the power allocation includes performing a water injection process.

[0212] Embodiment B6. A method as described in any of Embodiments B1-B5, wherein determining the beamformer and power allocation is performed without downlink training.

[0213] Embodiment B7. A method as described in any of Embodiments B1-B6, wherein maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is based at least in part on information about spectrum holes of the primary network.

[0214] Embodiment B8. A method as described in any one of embodiments B1-B7, wherein maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed under the constraint of interference caused by primary users of the primary network.

[0215] Embodiment B9. A method as described in any one of embodiments B1-B8, wherein maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed under the constraint that the number of antennas of the secondary network node is greater than the sum of the number of secondary users and primary users.

[0216] Embodiment B10. The method of any one of Embodiments B1-B9, wherein the estimating and determining are performed without receiving channel information from a master network node of the master network.

[0217] As will be appreciated by those skilled in the art, the concepts described herein may be embodied as methods, data processing systems, computer program products, and / or computer storage media storing executable computer programs. Thus, the concepts described herein may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects, collectively referred to herein as a "circuit" or "module". Any process, step, action, and / or function described herein may be performed and / or associated with a corresponding module, which may be implemented in software and / or firmware and / or hardware. In addition, the present disclosure may take the form of a computer program product on a tangible computer-usable storage medium, in which a computer program code that can be executed by a computer is embodied. Any suitable tangible computer-readable medium may be utilized, including a hard disk, a CD-ROM, an electronic storage device, an optical storage device, or a magnetic storage device.

[0218] Some embodiments are described herein with reference to flowchart illustrations and / or block diagrams of methods, systems, and computer program products. It will be understood that each frame of the flowchart illustration and / or block diagram and the combination of frames in the flowchart illustration and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer (thereby creating a special-purpose computer), a special-purpose computer, or other programmable data processing device to produce a machine, so that instructions executed by the processor of the computer or other programmable data processing device create a device for implementing the function / action specified in one or more frames of the flowchart and / or block diagram.

[0219] These computer program instructions may also be stored in a computer-readable memory or storage medium, which can instruct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction device that implements the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0220] Computer program instructions may also be loaded onto a computer or other programmable data processing apparatus so that a series of operating steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions / actions specified in one or more boxes of the flowchart and / or block diagram.

[0221] It will be understood that the functions / actions noted in the blocks may be performed in a different order than that noted in the operational diagram. For example, two blocks shown in succession may actually be performed substantially concurrently, or the blocks may sometimes be performed in a reverse order, depending on the functions / actions involved. Although some figures include arrows on communication paths to illustrate the primary direction of communication, it will be understood that communication may occur in the opposite direction of the arrows depicted.

[0222] Computer program code for carrying out operations of the concepts described herein may be written in an object oriented programming language such as Python, Or C++. However, the computer program code for performing the operations of the present disclosure may also be written in a conventional procedural programming language, such as the "C" programming language. The program code may be executed entirely on the user's computer, partially on the user's computer as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer. In the latter case, the remote computer may be connected to the user's computer via a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet).

[0223] Many different embodiments have been disclosed herein in conjunction with the above description and accompanying drawings. It will be understood that it would be unduly repetitive and confusing to literally describe and illustrate every combination and subcombination of these embodiments. Therefore, all embodiments may be combined in any manner and / or combination, and this specification (including the accompanying drawings) should be interpreted as constituting a complete written description of all combinations and subcombinations of the embodiments described herein, as well as the manner and process of making and using them, and will support the claims to the extent of any such combination or subcombination.

[0224] Abbreviations that may be used in the preceding description include:

[0225] APAR Average Per User Rate

[0226] DL Downlink

[0227] FDD Frequency Division Duplex

[0228] MF Matched Filter

[0229] OP Optimization Question

[0230] PBS Main Base Station

[0231] PN Primary Network

[0232] PU Primary User

[0233] QoS Quality of Service

[0234] rTDD Reverse Time Division Duplex

[0235] SBS Secondary Base Station

[0236] SN Secondary Network

[0237] SU Secondary User

[0238] TDD Time Division Duplex

[0239] UL Uplink

[0240] USS Substrate Spectrum Sharing

[0241] WD Wireless Devices

[0242] ZF Force Zero

[0243] It will be appreciated by those skilled in the art that the embodiments described herein are not limited to the contents specifically shown and described herein above. In addition, unless otherwise mentioned above, it should be noted that all drawings are not drawn to scale. According to the above teachings, various modifications and variations are possible without departing from the scope of the appended claims.

Claims

1. A secondary network node (16) in a secondary network, the secondary network node (16) comprising a processing circuit (68), the processing circuit (68) being configured to: performing channel estimation for a primary user of the primary network during a learning phase coincident with a training phase of the primary network; and A beamformer and power allocation are determined based at least in part on the channel estimate to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on a data rate of the primary network.

2. The secondary network node (16) of claim 1, wherein: The processing circuit (68) is also configured to perform reverse time division duplexing (rTDD).

3. The secondary network node (16) as claimed in any one of claims 1 and 2, wherein: Determining the beamformer and power allocation is based at least in part on information about a subspace of a channel matrix corresponding to a channel between the primary network and the secondary network.

4. The secondary network node (16) according to any one of claims 1 to 3, wherein: Determining the beamformer and power allocation includes determining the power allocation based at least in part on performing a convex optimization process.

5. The secondary network node (16) of claim 4, wherein: Determining the power allocation includes performing a water injection process.

6. The secondary network node (16) according to any one of claims 1 to 5, wherein: Determining the beamformer and power allocation is performed without downlink training.

7. The secondary network node (16) according to any one of claims 1 to 6, wherein: Maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is based at least in part on information about spectrum holes of the primary network.

8. A secondary network node (16) as claimed in claims 1 to 7, wherein: Maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed subject to a first constraint regarding interference caused by a primary user of the primary network.

9. The secondary network node (16) according to any one of claims 1 to 8, wherein: Maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed subject to a second constraint that the number of antennas of the secondary network node (16) is greater than the sum of the number of secondary users and primary users.

10. The secondary network node (16) according to any one of claims 1 to 9, wherein: The estimating and determining are performed without receiving channel information from a master network node (16) of the master network.

11. A method implemented in a secondary network node (16) of a secondary network, the secondary network node (16) being configured to communicate with a plurality of secondary users, the method comprising: performing (S138) channel estimation for a primary user of the primary network during a learning phase that coincides with a training phase of the primary network; as well as A beamformer and power allocation are determined (S140) based at least in part on the channel estimate to maximize at least one of a weighted uplink data rate and a weighted downlink data rate subject to a constraint on a data rate of the primary network.

12. The method of claim 11, further comprising: Perform reverse time division duplexing (rTDD).

13. The method according to any one of claims 11 and 12, wherein: Determining the beamformer and power allocation is based at least in part on information about a subspace of a channel matrix corresponding to a channel between the primary network and the secondary network.

14. The method according to any one of claims 11 to 13, wherein: Determining the beamformer and power allocation includes determining a power allocation based at least in part on performing a convex optimization process.

15. The method of claim 14, wherein: Determining the power allocation includes performing a water injection process.

16. The method according to any one of claims 11 to 15, wherein: Determining the beamformer and power allocation is performed without downlink training.

17. The method according to any one of claims 11 to 16, wherein: Maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is based at least in part on information about spectrum holes of the primary network.

18. The method according to any one of claims 11 to 17, wherein: Maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed subject to a first constraint regarding interference caused by a primary user of the primary network.

19. The method according to any one of claims 11 to 18, wherein: Maximizing at least one of the weighted uplink data rate and the weighted downlink data rate is performed subject to a second constraint that the number of antennas of the secondary network node (16) is greater than the sum of the number of secondary users and primary users.

20. The method according to any one of claims 11 to 19, wherein: The estimating and determining are performed without receiving channel information from a master network node (16) of the master network.

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