The existing unmanned aerial vehicle (UAV) enabled integrated sensing and communications (ISAC) systems primarily focus on the sensing capabilities of the UAV itself, overlooking the fact that the existing ground access points (APs) can receive the reflected signals for passive sensing, which can enhance the overall sensing performance. To address this issue, this paper introduces a UAV empowered air-ground ISAC system, where a UAV serves multiple communication users and performs detection for a potential target simultaneously, with the help of several ground APs. Specifically, the UAV works in active mode by transmitting ISAC signals and extracting information from echoes reflected from the target. In contrast, the ground APs function as sensing receivers, receiving and processing the reflected sensing signals from the target. Considering the limited capacity of the wireless backhaul links, we propose a two-step joint detection method, which contains local detection and result fusion steps. By incorporating the knowledge about the distribution of the reflection coefficient, we propose a maximum a-posteriori ratio test (MAPRT) detector, which is a generalization of earlier approaches such as the generalized likelihood ratio test detectors. Subsequently, the asymptotic distribution of test statistics of the MAPRT detector is derived. Furthermore, to improve the target detection performance, we propose an optimization algorithm that jointly optimizes the placement and transmit beamformer of the UAV, aiming at minimizing the probability of fusion error while guaranteeing the quality of service requirements of the users. Finally, numerical results demonstrate the effectiveness of the proposed algorithm.

The multi-base-station cooperative (MBC) ISAC mechanism is promising to satisfy the higher-quality sensing requirements of accuracy and coverage in 6G. STAR-RIS can help ISAC BSs provide cooperative sensing and communication (S&C) across full-space radio signal coverage. Short-packet transmission (SPT) is expected to be performed by ISAC BSs in 6G vertical applications to provide enhanced ultra-reliable low-latency S&C services. This paper proposes a STAR-RIS-assisted heterogeneous (SRH) MBC ISAC mechanism within the finite blocklength (FBL) regime. Multiple ISAC BSs and a communication-only (Comm-only) BS collaborate to provide S&C services to users and the target using STAR-RISs and cooperative detection results. A composite SPT (C-SPT) time frame structure is proposed by dynamically designing the S&C block lengths from the perspectives of the sub-frame and the entire transmission block. The systemic sum achievable capacity (SAC) over the entire transmission block is maximized by jointly optimizing the pilot and data block lengths and powers, as well as the active and passive beamforming of ISAC BSs and STAR-RISs under constraints of temporal, spatial, and power resources. The non-convex optimization problem is solved by combining the methods of alternating optimization (AO), quadratic transform (QT), sequential rotation (SR), and particle swarm optimization (PSO). Simulation results demonstrate the superiority of the proposed SRH-MBC ISAC mechanism with the C-SPT frame structure in achieving higher systemic communication performance with the assistance of STAR-RIS.

The existing unmanned aerial vehicle (UAV) enabled integrated sensing and communications (ISAC) systems primarily focus on the UAV’s own sensing capabilities, overlooking the potential of existing ground access points (APs) in performing passive sensing via the reflected signals—thus limiting the overall performance. To address this issue, this paper introduces a UAV empowered air-ground ISAC system, where a UAV cooperates with several ground APs in detecting a potential target, with the UAV and APs working in active and passive modes, respectively. Different from the conventional generalized likelihood ratio test detectors, we exploit the reflection coefficient’s distribution to design a maximum a-posteriori ratio test (MAPRT) detector and derive its asymptotic test statistic distribution. To break through the capacity limitations of the wireless backhaul links, we introduce a two-step joint detection method involving local detection and result fusion. Further, we propose an optimization algorithm to jointly optimize the UAV’s placement and transmit beamforming, aiming at enhancing the target detection performance. Numerical results demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, assisted by an existing receive access point. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme, where the beamforming and placement of the UAV are dynamically adjusted in different stages. Specifically, in the first stage, without prior knowledge about the target’s location, the UAV fixes at the initial location and performs wide beam sensing to probe the target. In the following stages, given the coarse estimation result of the target’s location (obtained in the previous stage), the UAV adjusts its location and performs narrow beam sensing to locate the target. Besides, the quality of service requirements of the users are guaranteed in all stages. Based on the proposed sensing scheme, we formulate and solve two optimization problems to improve the sensing accuracy. Finally, numerical results demonstrate the effectiveness of the proposed algorithm.

In this work, we investigate a task-driven delay minimization problem for autonomous aerial vehicle (AAV) enabled mobile crowdsensing (MCS) networks. Our focus is to reduce overall latency and improve data collection efficiency for delay-sensitive tasks through jointly optimizing the sensing data size, bandwidth allocation, and AAV hovering position. The formulated problem is a mixed-integer programming problem, which is also nonconvex. We introduce an efficient alternating optimization algorithm to address this challenge. Specifically, the original minimization problem is decomposed into two subproblems. The first subproblem focuses on bandwidth and sensing data allocation. By leveraging the latent structure property of the problem, we derive the optimal sensing data allocation given a bandwidth allocation policy. Based on it, we reveal that the first optimization subproblem can be converted into a maximum weighted matching problem in a bipartite graph, which can be optimally solved using the Hungarian algorithm. To address the optimization of the AAV’s hovering position subproblem, we employ the successive convex approximation (SCA) technique, which transforms it into a convex problem that can be efficiently solved by standard convex optimization solvers. We also analyze the convergence and the time complexity for the developed joint optimization algorithm. Afterward, we approximate the global optimal solutions in closed form for several specific cases of the problem. Extensive simulations confirm the superior performance of our proposed scheme compared to various benchmark strategies in terms of both delay and energy consumption.

Integrated sensing and communication (ISAC) has been identified as a potential key 6G technology that empowers cellular systems with wireless sensing capabilities. It is challenging for an individual ISAC node to meet the increased sensing demands of 6G Internet of Everything (IoE) applications, such as larger coverage and higher accuracy. To address this issue, multinode cooperative ISAC (MNC-ISAC) schemes have recently been explored to improve sensing performances for 6G IoE applications. This article innovatively proposes a distributed MNC-ISAC (DMNC-ISAC) scheme, which differs from the conventional centralized MNC-ISAC (CMNC-ISAC) scheme where all cooperative ISAC nodes form a single sensing coalition. In the proposed DMNC-ISAC scheme, multiple ISAC nodes form a satisfied number of cooperative groups by coordinating their sensing abilities and resources in a distributed way. The interactions between multiple ISAC nodes, characterized by collaboration in sensing and competition for resources, are modeled as a local cooperation-based game. Then, to maximize the overall sensing and communication (S&C) performance of the proposed DMNC-ISAC scheme, the cooperation grouping and transmission beamforming of multiple ISAC nodes are jointly optimized. The stochastic learning automata (SLA) and particle swarm optimization algorithms are utilized to obtain the equilibrium grouping results and design the beamforming of multiple ISAC nodes. Finally, simulation results show that the distributed cooperative ISAC scheme enhances sensing performance by at least 5%, leading to improved systemic performance in most ISAC scenarios. This improvement is particularly notable in challenging sensing environments and when the possible status of the target is ambiguous.

With the rapid development and widespread deployment of Internet of Things devices, existing networks face significant challenges in meeting the demands of emerging large-scale applications. In this article, we propose a novel paradigm to address these challenges by decomposing large applications/services into lightweight microservices (MSs) distributed among small base stations (SBSs), each responsible for specific functions. Upon receiving a service request, a macro base station (MBS) invokes a series of SBSs that cache the required MSs to execute the associated computational tasks. The computed results are then returned to the MBS, which integrates and delivers the final result to the user. Under this framework, we investigate the joint problem of MS caching, computation task assignment, and computing resource allocation, aiming to minimize the total energy consumption. Various practical constraints, such as users’ latency requirements, and the limited caching and computing resources of SBSs are taken into account. To facilitate the analysis, we transform the original minimization problem into an equivalent problem focusing on MS computation task assignment and computing resource allocation, which remains NP-hard. To tackle this challenge efficiently, we devise a two-stage method. In the first stage, we derive a closed-form expression for the computing resource allocation policy based on the MS computation task assignment. Subsequently, we introduce a two-side swapping oriented approach to explore an improved MS computation task assignment strategy. In addition, we propose the use of exhaustive and simulated annealing algorithms to approach the optimal and near-optimal solutions, respectively. Extensive simulation results demonstrate that our proposed algorithm achieves close-to-optimal performance and outperforms benchmark schemes significantly.

In this paper, we propose an unmanned aerial vehicle (UAV) enabled multi-stage integrated sensing and communications (ISAC) system, where a multi-antenna equipped UAV performs location sensing for a target whose location is initially unknown, while serves the communication users simultaneously, with the aid of an existing receive access point (RAP). By fusing the measurement results of the UAV and RAP, the location of the target is estimated. To improve the location sensing accuracy, we propose a multi-stage location sensing scheme. Specifically, in the first stage, in the absence of prior knowledge about the target’s location, the UAV fixes at the initial location and adjusts the beamformer to perform wide beam sensing to probe the target. In the following stages, with the previous coarse estimation result of the target’s location, the UAV performs narrow beam sensing by jointly adjusting the placement and also transmit beamformer. Besides, the quality of service requirements of the users are guaranteed in all stages. Accordingly, optimization problems are formulated for the first and following stages, respectively. By involving the semidefinite relaxation technique and then solving a quadratic semidefinite programming problem, the solution in the first stage is obtained. In the following stages, we jointly apply the alternating optimization, successive convex approximation, trust region, and also Dinkelbach’s methods to address the intricate coupling between the UAV placement and beamformer. Finally, numerical results demonstrate the effectiveness of the proposed algorithms.

For fifth-generation wireless networks to transition to sixth-generation wireless networks, the integration of coordinated multipoint (CoMP) and non-orthogonal multiple access (NOMA) techniques is expected to overcome new challenges and enhance performance compared to the CoMP or NOMA scheme. The joint-transmission CoMP (JT-CoMP) technique is a typical technical implementation of the CoMP scheme. In this study, we investigate a downlink network with a joint JT-CoMP-NOMA scheme. Based on the generalized Stienen model from stochastic geometry, we divide far and near NOMA user equipment (UE) and develop a theoretical framework to analyze the system performance. Expressions for the coverage probabilities and average achievable rates of two types of UEs (named CoMP and non-CoMP UEs) are derived. By comparing analytical results with Monte Carlo simulations, we show that the approximations in the analytical derivations are tight. The impact of certain network parameters, such as the power allocation coefficient, on the system performance is also studied. Notably, the developed transmission scheme is shown to outperform the NOMA-only and the JT-CoMP-only schemes.

Integrated sensing and communication (ISAC) has the potential to facilitate coordination gains from mutual assistance between sensing and communication (S&C), especially sensing-aided communication enhancement (SACE). Reconfigurable intelligent surface (RIS) is another potential technique for achieving resource-efficient communication enhancement. Therefore, this paper proposes an innovative RIS-assisted SACE (R-SACE) mechanism with the goal of improving the systemic communication performance of the ISAC system in practical scenarios where the channel status information (CSI) is imperfectly known. In the proposed R-SACE mechanism, a dual-functional base station (BS) provides downlink communication services to both the communication user and the dynamically changing target that is detected using the communication signals. RIS assists in both sensing and communications of the BS. A typical scenario is investigated in which either or both the direct and RIS-assisted reflected communication links are available depending on sensing results. The average systemic throughput (AST) over the entire timeline of the R-SACE mechanism is maximized by jointly optimizing both temporal and spatial resources under the probabilistic constraint and the sensing performance, transmission power, and communication interference constraints. The non-convex probabilistic mixed optimization problem is transformed and then solved by the proposed fixed-point iterative (FPI) algorithm. Simulation results demonstrate that the proposed FPI algorithm and R-SACE mechanism outperform the baseline algorithms and communication enhancement mechanisms in achieving higher systemic communication performance.

Abstract Cache‐enabled device‐to‐device (D2D) network has been deemed as an effective technique to offload the data traffic. However, the gain of the caching schemes is closely related to the homogeneity among users' preference distribution. To tackle this issue, recommendation is a promising proactive approach. It increases the request probability of recommended contents, reshaping users' contents demand patterns, and improving caching performance. Moreover, considering the heterogeneous network settings, i.e. content retrieval costs vary, the routing design becomes a non‐negligible factor on caching performance optimization. On these grounds, the average system cost of D2D‐enabled wireless caching networks with multiple BSs is first described. Then the routing strategies are designed together with caching and recommendation policies by minimizing the average cost of these networks. The optimization problem is proven as NP‐hard. To facilitate the analysis, the original problem is decoupled into two sub‐problems and solve them respectively. Afterwards, all the variables are optimized in an alternating manner until the convergence is achieved. The proposed algorithm's convergence performance and benefits over benchmark strategies in terms of total cost and cache hit ratio are supported by Monte‐Carlo simulation results.

Cell-free massive multiple-input-multiple-out (CF-mMIMO) systems provide limited coverage because of expensive wired fronthaul between access points (APs) and central processing unit (CPU). To address this challenge, we propose a novel framework where an unmanned aerial vehicle (UAV), acting as an aerial AP, works coherently with the ground APs to expand the coverage of conventional CF-mMIMO system. To fully utilize the spectrum resource, the wireless fronthaul between the CPU and UAV shares the total bandwidth with the radio access networks. Considering limited power supply of the UAV and for the goal of green communications, we formulate a weighted sum power minimization problem to jointly optimize downlink beamforming and fronthaul compression, as well as UAV placement. The formulated problem is a mixed timescale problem, thus we propose a two-timescale optimization framework in which the UAV placement is optimized in each long timescale based on statistical channel state information (CSI), then the downlink beamforming and fronthaul compression are optimized in each short timescale based on instantaneous CSI. Specifically, uplink-downlink duality and semidefinite relaxation (SDR) based alternating optimization techniques are introduced to find solutions to the short timescale issue, while successive convex approximation and SDR methods are invoked to find solutions to the long timescale issue. Finally, simulation results corroborate the performance of the proposed algorithm.

Abstract The 5G and beyond (B5G) networks are expected to provide significantly increased capacity for diverse services with limited spectrum resources. However, new aspects of B5G networks particularly the ultra‐dense network deployment and the heterogeneous network structure, make spectrum resources highly distributed in three‐dimension (3D), which brings unprecedented challenges for highly efficient spectrum utilization, especially in an indoor environment. To tackle the challenges on dynamic spectrum utilization and improving the volume capacity of indoor 3D networks, a collaborated crowdsensing approach is proposed for the coordinated 3D spectrum utilization through integrating the crowdsensing, data analytics, and software defined network (SDN) techniques. The integration of sensing, learning, and intelligent control provides critical capabilities for timely observing 3D radio resources and enabling the coordinated radio resource utilization among co‐existing indoor heterogeneous networks (HetNets). Case study confirms the superiority of the proposed coordinating method on achieved volume capacity.

We exploit a behavior-shaping proactive mechanism, namely, recommendation, in cache-assisted non-orthogonal multiple access (NOMA) networks, aiming at minimizing the average system’s latency. Thereof, the considered latency consists of two parts, i.e., the backhaul link transmission delay and the content delivery latency. Towards this end, we first examine the expression of system latency, demonstrating how it is critically determined by content cache placement, personalized recommendation, and delivery associated NOMA user pairing and power control strategies. Thereafter, we formulate the minimization problem mathematically taking into account the cache capacity budget, the recommendation-oriented requirements, and the total transmit power constraint, which is a non-convex, multi-timescale, and mixed-integer programming problem. To facilitate the process, we put forth an entirely new paradigm nameddivide-and-rule. Specifically, we first solve the short-term optimization problem regarding user pairing as well as power allocation and the long-term decision-making problem with respect to recommendation and caching, respectively. On this basis, an iterative algorithm is developed to optimize all the optimization variables alternately. Particularly, for solving the short-timescale problem, graph theory enabled NOMA user grouping and efficient inter-group power control manners are invoked. Meanwhile, a dynamic programming approach and a complexity-controllable swap-then-compare method with convergence insurance are designed to derive the caching and recommendation policies, respectively. From Monte-Carlo simulation, we show the superiority of the proposed joint optimization method in terms of both system latency and cache hit ratio when compared to extensive benchmark strategies.

Unmanned aerial vehicle (UAV) has demonstrated its usefulness in goods delivery. However, the delivery distances are often restrained by the battery capacity of UAVs. This paper integrates UAVs into intelligent transportation systems for energy-efficient, delay-sensitive goods delivery. Dynamic programming (DP) is first applied to minimize the energy consumption of a UAV and ensure its timely arrival at its destination, by optimizing the control policy of the UAV. The control policy involves decisions including flight speed, hitchhiking (on collaborative ground vehicles), or recharging at roadside charging stations. Another key aspect is that we reveal the conditions of the remaining flight distance or the elapsed time, only under which the optimal action of the UAV changes. Accordingly, thresholds are derived, and the optimal control policy can be instantly made by comparing the remaining flight distance and the elapsed time with the thresholds. Simulations show that the proposed algorithms can improve the flight distance by 48%, as compared with existing alternatives. The proposed threshold-based technique can achieve the same performance as the DP-based solution, while significantly reducing the computational complexity.

Abstract Caching popular contents at base stations (BSs) can effectively avoid redundant data traffic and improve backhaul capacity. Techniques such as multicast (MC) and non‐orthogonal multiple access (NOMA) can significantly improve spectral efficiency by delivering popular contents to multiple users using a single channel. Therefore, we propose a cooperative transmission scheme of MC and NOMA in cache‐enabled wireless cellular networks. First, we derive the probability mass function (PMF) of the number of channels in the MC mode as well as the joint PMF of the number of channels and number of NOMA users in the NOMA mode. Second, we analyse the successful transmission probabilities of the two modes by utilising tools from stochastic geometry. Finally, with the derived probabilities using the two modes, we obtain the total successful transmission probability based on probability theory. The quasi‐closed form expression of the successful transmission probability is obtained in a special case, where the noise is neglected, and the path‐loss exponent is set to be four. Simulation results validate the accuracy of the analysis and demonstrate the performance gain due to the proposed cooperative transmission scheme of MC and NOMA.

The Internet of Things (IoT) will generate a large amount of data and the desired data are similar for users in particular regions. In these situations, data can be shared by D2D communication, which can significantly ease the traffic on BSs and effectively reduce the load. In this article, we propose an optimization algorithm for solving the joint problem of file caching and updating aimed at the D2D content-sharing scenario in the IoT. First, we construct a user-mobility model based on a Markov chain and a user-interest prediction model based on social proximity, user preference, and freshness. Then, we formulate the mobility-aware, freshness-based, and user-interest predicted optimization problem as a 0-1 multiple Knapsack problem, which is decomposed into two subproblems: 1) a cache problem and 2) an update problem. We prove that the cache problem's optimization objective is of the monotone submodular function over one matroid and multiple Knapsack constraints categories, while the update problem's optimization objective is a monotone decreasing function. The simulation results confirm that the optimization algorithm proposed in our article predicts the interests of users more accurately, improves the caching hit probability of files effectively, and maximizes utility for IoT network users.

We study the WiFi offloading problem in smart communications and adaptively seek for the optimal offloading strategies with the consideration of the mobility management and the dynamical nature of network state. With users mobility management, we formulate the offloading ratio optimization problem based on Markov process. Then, we propose a novel Congestion‐Optimal WiFi Offloading (COWO) algorithm based on subgradient method, which aims to obtain the optimal offloading ratio for each access point (AP) to maximize the throughput and minimize the network congestion. Due to the computational complexity of subgradient method, we further improve the COWO algorithm by the equivalent transformation. By viewing all the APs as one virtual WiFi network, we try to optimize the identical offloading ratio for virtual WiFi network and develop a Virtualized Congestion‐Optimal WiFi Offloading (VCOWO) algorithm with lower complexity. Under the equivalent conditions, the performance of the VCOWO algorithm could well approximate the optimal results obtained by the COWO algorithm. It is found that the VCOWO algorithm could obtain the upper bound of multiple APs WiFi offloading performance. Moreover, we investigate the impacts of user mobility on the WiFi offloading performance. Simulation results show that the proposed algorithm could achieve higher throughput with lower network congestion compared with other current offloading schemes.

The unprecedented data increase has imposed great challenges to cellular networks. Traffic offloading by heterogeneous radio access technologies (RATs) is an effective approach for enhancing network capacity. Without costly and time-consuming infrastructure investments, WiFi offloading is deemed as the prospective evolution for the heterogeneous integration. However, previously proposed schemes mainly focus on alleviating burden by offloading data to WiFi as much as possible, without systematic considerations of the network congestion. In this paper, based on Markov process, we investigate the congestion offloading problem in user random mobility model, and prove it could be solved in subgradient method with equivalent transformation. Moreover, the congestion-aware WiFi offloading algorithm is proposed for multiple WiFi APs network offloading scenario, aiming to balance the capacity increase with congestion characterized by blocking probability. In addition, the upper bound and lower bound of blocking probability is deduced in optimization. Simulation results show that, by optimizing user offloading probability, the algorithm proposed could achieve maximum throughput with lower blocking probability.

In 5G heterogeneous evolution, the unlicensed band has captured much attention. Specified by 3GPP Release 13, LTE WLAN aggregation (LWA) is deemed as an effective approach for spectrum integration of 5G heterogeneous networks (HetNet). However, most of previous works about LWA lie in the architecture design, and rarely investigate LWA algorithm analytically. In this paper, we formulate the network access and aggregation problem for delay- tolerant application in multiple slots, and further develop a delay-aware LTE WLAN aggregation algorithm (DLWA) based on dynamic programming, which is aimed to minimize the user payment with QoS requirement. To reduce the complexity, we prove optimal decision policy and simplify the searching space of scheme sets. Simulation results show that, comparing with the current WLAN interworking solutions, the algorithm could lower the payment and achieve high completion probability under specified transmission deadline. The framework presented can support WLAN offloading scheme as well, which enables the best use of unlicensed resource.

In heterogeneous networks (HetNets), the network energy efficiency is critically determined by the base station (BS) deployment density. In this paper, we consider a BS density optimization problem by turning on only a fraction of micro BSs according to an activation ratio to minimize the network average power consumption per area in a 2- tier HetNet. In contrast to previous studies where a BS is assumed to be transmitting packets all the time, such that the network power consumption monotonically increases as the BS density increases, we assume that each BS can be busy or idle depending on the dynamic packet arrivals. The network power consumption is thus closely related to the average traffic intensity of each tier. With the assumption of universal spectrum reuse, the average traffic intensity of each tier is found to be uniquely determined by a set of fixed-point equations, based on which the network average power consumption per area is characterized. Simulation results demonstrate that the network average power consumption per area can be minimized by properly tuning the activation ratio. It is further revealed that the optimal activation ratio increases as the mean packet arrival rate of each user increases.

Heterogeneous cellular networks (HCNs) are usually modelled as a Poisson point process (PPP) due to mathematical tractability. However, in urban areas, small cells are more likely to cluster around hotspots rather than be uniformly distributed. Meanwhile, in reality, small cell locations are not completely independent of the macro base station (MBS) locations. To capture both features, the authors propose a new modelling method considering inter‐tier dependence for a two‐tier HCN, where MBSs follow a PPP, small cells follow a Matern cluster process and are not allowed to be deployed inside the MBS exclusion regions (i.e. within a prescribed distance from each MBS). Approximate expressions of the average coverage probability and the area spectral efficiency (ASE) are obtained, respectively. Simulation results confirm the accuracy of the derivations. It is observed that the model can achieve a good tradeoff between coverage and ASE, and if the small cell density in this model is kept identical to the HCN without inter‐tier dependence, both coverage and ASE can benefit from involving the inter‐tier dependence in the HCN.

We focus on the performance analysis of the buffer-aided relaying system which allows data and energy packets to arrive independently and depart interactively. First, we profile the cooperative relaying system model as a data arrival and energy arrival coupling queuing model. Considering the influence of channel condition on the data departure rate, a new relay transmit protocol which permits exhausting more energy packet to send one data packet in the bad channel environment is proposed. Second, the joint data packet and energy packet handling problem is ascribed to a Coupled Processor Queuing Model which could achieve its steady state transition probability by Quasi-Birth and Death method. Third, the expressions of throughput, delay, and packet drop rate for both data queue and energy queue are also derived. Simulations are demonstrated to verify the analytical results under different data arrival rate, energy arrival rate, and relaying strategy.

In a VANET (Vehicular Ad Hoc Network), the collisions caused by vehicles' mobility lead to poor network performance, especially in high-density network. In order to increase the flexibility of communication, this paper presents a communication protocol with dynamic choosing relay node and automatic cooperative communication called VC-TDMA (Vehicular Cooperative TDMA). The nodes can choose the multi-hop relay nodes rationally and other idle nodes can provide cooperative communication automatically. Considering batch arrival data traffic, the buffer queue state of nodes in the network is numerical analyzed by Markov Chain. The simulation results show that VC-TDMA can improve the throughput of the network compared with the conventional TDMA protocol.

This paper considers a multipair massive multiple-input-multiple-output two-way relay network, in which multiple pairs of users are served by a relay station with a large number of antennas, which uses maximum ratio combining/maximum ratio transmission and a fixed amplification factor for reception/ transmission. First, the users' ergodic rates are derived for the case with a finite number of antennas, and then, the rate gain is analyzed when the transmit power of the senders and the relay is sufficiently large. We show that the ergodic rates increase with the number of antennas at the relay, i.e., N, but decrease with the number of user pairs, i.e., K, both logarithmically. The energy efficiency for the network is also investigated when the number of antennas grows to infinity. It is further revealed that the ergodic sum-rate can be maintained while the users' transmit power is scaled down by a factor of 1/N or the relay power by a factor of 2K/N. This indicates that users obtain an energy efficiency gain of N, but the relay has an energy efficiency gain of N divided by the number of users, i.e., 2K.