Nowadays, Unmanned Aerial Vehicle (UAV)-assisted Mobile Edge Computing (MEC) has been recognized as a promising technique for flexibly handling computation tasks in 5G advanced and 6G networks. This paper investigates the joint optimization of service caching and computation offloading within a dual time-scale framework. We maximize the caching utility and minimize the task processing delay by jointly optimizing service caching policies, UAV flight trajectory, and computation offloading decisions. Specifically, for the long-term problem, we use the Latent Dirichlet Allocation (LDA) model to predict user preferences, and propose a Lagrangian dual decomposition based algorithm. For the short-term problem, a self-attention based Multi-Agent Proximal Policy Optimization (MAPPO) algorithm is designed. Under the Centralized Training with Decentralized Execution (CTDE) framework, this algorithm integrates a multi-head self-attention mechanism with curriculum learning. Each UAV is regarded as an agent, and a self-attention encoder is integrated at the front-end of each Actor network. This enables the agent to dynamically capture the relative importance between itself and all users, and context-aware features are extracted to make more intelligent and trajectory designs. Through extensive simulation experiments, the long-term algorithm yields the performance improvements of 76.5% in cache hit rate and 66% in caching utility, as compared to the second best baseline. In dynamic scenarios, the short-term algorithm achieves a 16% reduction in total processing latency with respect to the proximal policy optimization policy.

In this paper, we propose a secure content caching and delivery strategy to support multi-level viewing services based on scalable video coding (SVC). With the existence of multiple eavesdroppers (Eves), the base layer (BL) and enhancement layers (ELs), that are encoded by SVC are cached in the macro-cell base station (MBS) and small-cell base stations (SBSs), respectively, using a probabilistic caching strategy. Power domain non-orthogonal transmission is utilized to deliver ELs for the serving SBS. We analyze the transmission secrecy performance for the end user. Specifically, employing the tool of stochastic geometry, the successful transmission probabilities (STPs) from the serving MBS and SBS are derived, and the worst-case secrecy outage probability (SOP) is also analyzed. After obtaining the expressions for STPs and SOP, a more comprehensive performance metric to evaluate the system security and caching efficiency, called the secrecy cache-aided data rate (SCADR), is analyzed and derived. Simulation results validate the accuracy of our derived STPs and SOP, and show how the SCADR varies with important system parameters under different caching schemes.

Reconfigurable intelligent surface (RIS) and nonorthogonal multiple access (NOMA) are two promising technologies for future wireless communication networks. This paper proposes a new transmission design for short-packet communications (SPCs) in a downlink RIS-assisted NOMA system, considering hardware impairments at the transceiver nodes, non-optimal continuous phase shifts (CPSs) with phase estimation errors at the RIS, and imperfect successive interference cancellation (SIC) for a pair of NOMA users. Using the method of moment matching, we approximate the end-to-end channel gain with a Gamma distribution, and derive the closed-form approximate expressions for the average block error rates (BLERs) for NOMA users. Moreover, the system throughput is obtained to characterize the transmission efficiency of the network. Analytical results of the proposed scheme indicate that the phase estimation errors can seriously affect the system performance. Therefore, when conducting theoretical analysis, the non-optimal CPSs should be considered for RIS deployment. Numerical results validate our theoretical analysis, and confirm the performance improvement of transmission reliability and effectiveness of the proposed scheme, as compared to existing transmission schemes.

To reduce the heavy traffic burden over backhaul links, this paper studies a new reconfigurable intelligent surface (RIS) aided wireless caching system. The RIS is equipped with a smart controller to control reflective elements. The functions of content caching and energy harvesting (EH) are endowed to the controller. A two-phase transmission policy is designed. In the first phase, the requested contents that are not locally cached are delivered by the base station (BS) to the RIS, and the smart controller harvests the energy from ambient radio frequency (RF) signals. In the second phase, the smart controller distributes cached contents utilizing non-orthogonal multiple access (NOMA) transmission with the aid of the harvested energy, while the RIS forwards the fetched contents. The beamforming vectors of the BS and the power allocation factors for NOMA transmission are jointly optimized to maximize the achievable sum rate of the system. The original problem is difficult to solve owing to coupled variables. After being processed by fractional programming (FP) and quadratic transformation (QT), it is divided into two sub-problems. The first sub-problem is solved by introducing auxiliary variables, and the second one is converted to be convex by rational transformations to the quality of service constraint. The optimal solution is achieved by alternatively solving the two sub-problems. Simulation results reveal that the original problem can converge quickly, and the proposed algorithm outperforms benchmarks in terms of the achievable sum rate.

This paper proposes a novel content-centric framework for joint content update, push, and delivery in wireless caching networks. We employ a two-stage transmission strategy by grouping users based on their distribution locations and content caching status of the content server (CS). Users can be served either by the base station (BS) through multicast transmission or by the CS covering them and caching the requested contents. The content update scheme is designed based on the caching function that takes the request popularity and file size into account. Afterwards, the content push and delivery process is conducted in two stages. In the first stage, the requested and updated contents for multicast groups and the CS are delivered, while the CS harvests energy by employing the function of simultaneous wireless information and power transfer (SWIPT). In the second stage, the CS serves its covering users utilizing non-orthogonal multiple access (NOMA) transmission. Based on the proposed caching and transmission designs, we formulate the sum rate optimization problem, which is challenging due to its non-convexity. Thereafter, we convert it into a convex second-order cone programming problem using semi-definite relaxation (SDR), successive convex approximation (SCA), and other approximation techniques, and solve it with CVX solvers. Simulation results exhibit the fast convergence property of the proposed algorithm and demonstrate the advantages of our twostage content transmission approach, particularly in terms of the content hit rate and the sum rate.

Reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) are two promising technologies for future wireless communication networks. This paper proposes a new transmission design for short-packet communications (SPCs) in a downlink RIS-assisted NOMA system, considering hardware impairments at the transceiver nodes, non-optimal continuous phase shifts (CPSs) and discrete phase shifts (DPSs) at the RIS, and imperfect successive interference cancellation (SIC) for a pair of NOMA users. Using the method of moment matching, we approximate the end-to-end channel gain with a Gamma distribution, and derive the closed-form approximate expressions for the average block error rates (BLERs) for NOMA users, along with the diversity order and the asymptotic expressions for the average BLERs at the high signal-to-noise ratio (SNR) region. Inspired by the asymptotic average BLER, the minimum blocklength is derived, and the optimal power allocation factor is obtained. Then, the system throughput is obtained to characterize the transmission efficiency of the RIS-assisted short-packet NOMA network. Analytical results of the proposed scheme indicate that: 1) The phase estimation error of the RIS has a significant impact on the system performance, and thus the non-optimal CPSs should be used in the theoretical analysis; 2) The DPSs with 3-bit quantization can effectively achieve a comparable performance to the optimal CPS scheme in practical applications. Numerical results validate our theoretical analysis, and clearly show the performance improvement of transmission reliability and effectiveness of the proposed scheme, as compared to existing transmission schemes.

This paper proposes a new joint random caching and hierarchical transmission scheme for delivering multimedia content in cache-assisted heterogeneous networks. We use scalable video coding (SVC) and wireless edge caching to offer personalized video-watching services to end users. Resorting to stochastic geometry, we derive new expressions for successful transmission probabilities (STPs). Using the derived STPs, the achievable transmission rates (ATRs) and transmission delay experienced by multimedia users are obtained. The Gaussian-Chebyshev quadrature is employed to approximate the STPs and ATRs in closed form to improve mathematical tractability. We also attain the user satisfaction index (USI) concerning user’s delay experience. A transmission delay minimization problem is formulated and solved efficiently using the standard gradient projection approach to determine the optimal layer placement design under a random caching criterion. Simulation results confirm the correctness of the derived STPs, show the negligible approximation loss of Gaussian-Chebyshev quadratures, and manifest the performance superiority of the devised SVC-based caching design to the energy-optimal caching and the non-caching approach.

This paper investigates the joint computation offloading and content caching scheme in fog radio access networks (F-RANs), where users and fog nodes (FNs) are all endowed with caching and computing capabilities. The Kuhn-Munkres (KM) algorithm is first used to make the user-and-FN matching decisions. We then comprehensively reveal the service modes when a user receives the requested data to perform a computation task. The optimization problem of minimizing the task response latency is formulated under the constraints of limited cache sizes for users and FNs, aiming to yield the optimal decisions for computation offloading and content caching. The formulated problem is NPhard, and transformed into the convex form by introducing the methods of difference of convex (DC) function, successive convex approximation (SCA), and arithmetic geometric mean (AGM) inequality. Simulation results demonstrate the fast convergence speed of the proposed algorithm, and show superior latency performance of our designed scheme to the benchmarks.

Wireless edge caching can alleviate the transmission pressure and improve the quality of experience (QoE) provided to users, which has been proposed as a promising technique in the sixth generation (6G) communication systems. This paper considers a clustered device-to-device (D2D) caching network, where users having similar request preference are classified into the same cluster. Due to the fact that recommendation policy will have a significant effect on user requests, the joint design of content recommendation and caching is investigated, aiming to enhancing the system caching efficiency (CE). Under the constraints of the recommendation quality, recommendation quantity and cache size, we formulate the CE maximization problem. The original problem is an NP-hard integer programming problem, making it difficult to solve. Therefore, it is partitioned into two sub-problems. The binary variables in the first sub-problem, namely, the recommendation design sub-problem, is relaxed to be continuous, and then the relaxed problem is solved by applying the methods of successive convex approximation and arithmetic geometric mean inequality. The recommendation decisions are recovered following a greedy strategy based principle. The second sub-problem, i.e., the caching design sub-problem, is addressed by using the smooth function to replace the $l_{0}$ norm. Simulation results demonstrate the fast convergence speed of the proposed algorithm, and show the obtained CE performance is superior to existing homogeneous recommendation method.

Compared with terrestrial reconfigurable intelligent surface (RIS), aerial RIS provides improved transmission performance owing to its high adaptability in three-dimensional space and the existence of established line-of-sight links. In this paper, we study an aerial RIS-aided over-the-air federated learning (AirFL) system and propose an effective resource allocation scheme to maximize the energy efficiency (EE) of the AirFL system, where the transmit power of the Internet-of-Things devices, the receive scalar at the base station (BS), and the phase shifts of the RIS are jointly optimized. Since the formulated EE maximization problem consists of coupled variables, it is essentially non-convex. The original problem is then transformed into several sub-problems, which are addressed by adopting Dinkelbach's method, successive convex approximation and difference-of-convex programming approach. Finally, simulation results demonstrate that the proposed scheme is superior in terms of the EE performance for RIS-aided AirFL system compared with the existing scheme.

In the emergency communication scenario, unmanned aerial vehicle (UAV)-based relaying and mobile edge computing (MEC) networks can quickly collect ground users' (GUs) information and provide high-quality computing services. In this paper, we investigate a UAV and reconfigurable intelligent surface (RIS) assisted uplink non-orthogonal multiple access MEC networks for emergency communications, where the UAV serves as a relay to assist the transmission from GUs to base station and provides MEC services. For the considered system, the computation efficiency (CE) is maximized by jointly optimizing the phase shift of RIS, communication and computation resources, bandwidth allocation coefficients, and the space location of UAV. The formulated problem is non-convex, and we divide the problem into four sub-problems. An alternating optimization-based algorithm is proposed to deal with it. In particular, with the aid of Dinkelbach's method, semi-definite relaxation, first-order Taylor expansion, successive convex approximation and improved particle swarm optimization, the decoupled non-convex sub-problems are effectively solved. Simulation results show that the proposed scheme can effectively improve the CE of the system, as compared with the conventional schemes.

This paper investigates the secrecy communications of a downlink wireless-powered cooperative non-orthogonal multiple access (NOMA) system with a source node, a relay, a pair of near and far users, and an eavesdropper (Eve). A wireless-powered relay assists the transmission from the source to the far user, and the Eve intends to wiretap the confidential message transmitted to the near user. We propose a dynamic power allocation and friendly jamming based secure transmission scheme. Specifically, the optimal power allocation factors at the source node for the paired NOMA users are obtained to guarantee the perfect successive interference cancellation performance and improve the transmission reliability of the pair of NOMA users. In the direct transmission phase, the source transmits a superimposed signal to the near user and the wireless-powered relay, and the far user serves as a friendly jammer to emit artificial noise to confuse the Eve. In the cooperative transmission phase, the relay utilizes the harvested energy to assist the transmission to the far user. By applying the Gaussian-Chebyshev and Gaussian-Laguerre quadratures, closed-form expressions for the secrecy outage probability of the near user and the connection outage probability of the far user are derived. Simulation results verify the correctness of our theoretical analysis. The superiority of the proposed scheme is also demonstrated for improving the secrecy performance, as compared to the existing cooperative NOMA schemes.

Confronted with the soaring growth of wireless traffic, edge caching has been deemed as a promising technique to address the challenges caused by redundant file deliveries and finite backhaul/fronthaul capacities. When making caching decisions, popularity prediction is preliminary but crucial. Con-sidering diversified viewing preferences of users, scalable video coding (SV C) is used to divide the file into multiple layers. The layers are used as caching units, and then delivered to users. Owing to this fact, we study the layer-based popularity prediction and content placement schemes. To protect users' privacy, a distributed framework based on federated learning is proposed to acquire the layer popularity. During the local training process, users are divided into privacy-sensitive and privacy-insensitive users. Taking users' privacy sensitivity into account, different types of nodes are nominated as edge nodes (EN s) for training, further reducing the risk of privacy leakage. The stacked auto encoder is used to extract features from both users and videos. By resorting to collaborative filtering, the features are used as inputs for predicting the layer popularity. Extensive experiments are conducted using a real dataset. Results reveal that, in contrast with the scheme that only provides basic viewing quality, the cache hit rate (CHR) is doubled by applying the SVC-based scheme when the cache size of each EN is set to 160 Mbits. We also find that the proposed algorithm improves the CHR by 55 % and decreases the transmission delay by 49 %, as compared to the Least Frequently Used (LFU) scheme.

In this paper, we investigate the physical layer security for simultaneous transmission and reflection (STAR) reconfigurable intelligent surface (RIS) aided systems. We consider a more practical case where multiple eavesdroppers can wiretap the transmitted signals from the access point (AP) to the legitimate users located in the transmission and reflection regions, respectively. An artificial noise (AN) aided secrecy transmission scheme is then proposed, aiming to improve the secrecy performance. The transmit beamforming and AN vectors of AP as well as the transmission and reflection coefficients are jointly optimized to maximize the sum secrecy rate. To effectively solve the formulated problem, it is firstly divided into two subproblems, and then the methods of semi-definite relaxation and Sion’s minimax theorem are employed to deal with the subproblems. By alternatively solving the sub-problems, the optimal solutions are obtained. Simulation results demonstrate the effectiveness of the proposed scheme compared to the conventional scheme without AN and STAR-RIS, and also bring useful insights into the design of secrecy communications in STAR-RIS aided systems with multiple eavesdroppers.

This paper investigates a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink non-orthogonal multiple access (NOMA) system, consisting of a base station (BS), a STAR-RIS, a transmission user and a reflection user, where STAR-RIS uses mode switching protocols. Under the proposed transmission model, the base station serves the two users via the STAR-RIS and a direct link exists from the base station to the reflection user. By using the Beaulieu series, the statistical property of the Nakagami-m fading cascaded channel is characterized. Based on this, closed-from expressions of outage probability and diversity order for the reflection user and transmission user are obtained, respectively. In addition, to ensure the maximum gain in system throughput, a component allocation scheme is proposed, and the optimal allocation interval between the transmission and reflection components is obtained. Numerical results verify the theoretical analysis and demonstrate the superiority of the proposed scheme in terms of outage probability and system throughput compared to existing schemes.

In the emergency communication scenario, how to quickly collect ground users (GUs) information and carry out reliable transmission has become a difficult point. In this paper, we consider the unmanned aerial vehicle (UAV)-assisted emergency communication system based on uplink non-orthogonal multiple access (NOMA). For the considered system, an alternative optimization scheme for the space location of UAV and resource allocation is proposed to maximize the uplink throughput from the UAV to base station (BS). The original joint optimization problem turns out to be non-convex and is divided into two sub-problems. Firstly, we optimize the space location of UAV by using particle swarm optimization algorithm, which has significant advantages in solving the optimal space location problem. Secondly, by using the tools of the first-order Taylor expansion and successive convex approximation, we jointly optimize the bandwidth allocation coefficients and transmit powers of GUs to maximize the throughput from the UAV to BS. Finally, simulation results verify our proposed scheme is superior in terms of the throughput for UAV-assisted emergency communication system compared with the existing scheme and the baseline schemes.

In this paper, the non-orthogonal multiple access (NOMA) and simultaneous wireless information and power transfer (SWIPT) are applied to wireless caching networks (WCN). Particularly, in the content pushing stage, base station sends the most popular contents to helpers with NOMA and the helpers harvest energy from the received signals. Then, the helpers send the requested files to the users with NOMA in the content delivery stage. In the content pushing stage, we minimize the maximum delay of the helpers by jointly optimizing the power allocation and time switching factors. Due to the non-convexity of the problem, the bisection method and two-layer iterative algorithm are devised to solve the problem. In the content delivery stage, the energy efficiency (EE) optimization problem is formulated under the quality-of-service, energy harvesting, and transmit power constraints. By using the nonlinear fractional programming theory and the Lagrange dual method, the original optimization problem is solved and the optimal power and time allocation coefficients are obtained. Simulation results show that the proposed algorithm reduces the delay of the helpers and improves the EE performance of helpers compared to the benchmark schemes.

In this paper, we study a distributed reconfigurable intelligent surface-aided downlink multiple-input multiple-output non-orthogonal multiple access systems with random distributed users, where the signal alignment can be achieved within the paired users by controlling discrete phase shifts. In particular, a unified precoder and decoder are provided to cancel inter-cluster interference. To evaluate the performance of proposed framework, the near and far users’ channel statistics with Nakagami-m fading are derived. Further, the approximate expressions of average outage probability are obtained by utilizing the proposed channel statistics, and the corresponding diversity orders can also be obtained to acquire more insights. Finally, simulation results reveal that not only discrete phase shifts design by selecting a 3bit resolution can realize the near optimal performance but also the diversity gain can be significantly improved by increasing the number of reflection elements.

The combination of reconfigurable intelligent surface (RIS) and non-orthogonal multiple access (NOMA) has been recognized as a critical method to improve the sixth generation networks performance. In this paper, a distributed RIS-aided downlink multiple-input multiple-output (MIMO) NOMA systems with discrete phase shifts are studied, where the channel directions from base station to paired users can be manipulated with the assistance of RISs by employing the concept of signal alignment. In order to ensure base station can flexibly serve some users with NOMA and others users with spatial division multiple access, a unified precoder and decoder are provide to cancel inter-cluster interference. Subsequently, the channel statistics over Nakagami-$m$fading channels are derived for near and far users. In particular, considering the cascade channel gain may exists two different cases, Beaulieu series is further adopted to characterize their corresponding cumulative distribution function. In what follows, the outage probability and ergodic rate for three situations within one cluster are derived by utilizing the obtained channel statistics, respectively. Based on the derived results, we also analyze the diversity order and high signal-to-noise ratio slope to provide essential insights into the considered systems. Finally, simulation results are presented to reveal that: 1) selecting the setting of 3-bits resolution can realize a near-aligned performance for our proposed systems; 2) the cascade channel statistic caused by RIS can be evaluated with any number of RIS element and any channel gain via Beaulieu series.

The optimal economical caching strategy in cache-enabled heterogeneous networks is studied in this paper. In the meanwhile, the multimedia video services with personalized viewing qualities are delivered to mobile subscribers. We analytically derive the successful transmission probability and ergodic service rate, and then the closed-form EConomical Efficiency (ECE) is acquired. In order to improve the ECE performance, we formulate the ECE optimization problem. With equal cache size equipped at each serving small-cell base station (SBS), the layer caching indicator is determined. This problem is an integer programming problem, and is NP-hard in essence. After the l0-norm approximation, the original maximization problem is relaxed to be convex. Next, based on the optimal solution derived from the relaxed problem, we propose a heuristic algorithm based on the greedy strategy to acquire the layer caching indicators. Numerical results verify the correctness of our theoretical analysis. We also find that the proposed caching scheme is superior to the most popular layer placement strategy in terms of the ECE performance.

We develop the optimal economical caching schemes in cache-enabled heterogeneous networks, while delivering multimedia video services with personalized viewing qualities to mobile users. By applying scalable video coding (SVC), each video file to be requested is divided into one base layer (BL) and several enhancement layers (ELs). In order to assign different transmission tasks, the serving small-cell base stations (SBSs) are grouped into $K$ clusters. The SBSs are able to cache and cooperatively transmit BL and EL contents to the user. We analytically derive expressions for successful transmission probability and ergodic service rate, and then the closed-form expression for EConomical Efficiency (ECE) is obtained. In order to enhance the ECE performance, we formulate the ECE optimization problems for two cases. In the first case, with equal cache size equipped at each SBS, the layer caching indicator is determined. Since this problem is NP-hard, after the l0-norm approximation, the discrete optimization variables are relaxed to be continuous, and this relaxed problem is convex. Next, based on the optimal solution derived from the relaxed problem, we devise a greedy-strategy based heuristic algorithm to achieve the near-optimal layer caching indicators. In the second case, the cache size for each SBS, the layer size, and the layer caching indicator are jointly optimized. This problem is a mixed integer programming problem, which is more challenging. To effectively solve this problem, the original ECE maximization problem is divided into two subproblems. These two subproblems are iteratively solved until the original optimization problem is convergent. Numerical results verify the correctness of the theoretical derivations. Additionally, compared to the most popular layer placement strategy, the performance superiority of the proposed SVC-based caching schemes is testified.

This paper investigates an energy‐constrained two‐way multiplicative amplify‐and‐forward (AF) relay network, where a practical nonlinear energy harvesting (NLEH) model is equipped at the relay to realize simultaneous wireless information and power transfer (SWIPT). We focus on the design of dynamic power splitting (DPS) strategy, in which the PS ratio is able to adjust itself according to the instantaneous channel state information (CSI). Specifically, we first formulate an optimization problem to maximize the outage throughput, subject to the NLEH. Since this formulated problem is nonconvex and difficult to solve, we further transfer it into an equivalent problem and develop a Dinkelbach iterative method to obtain the corresponding solution. Numerical results are given to verify the quick convergence of the proposed iterative method and show the superior outage throughput of the designed DPS strategy by comparing with two peer strategies designed for the linear energy harvesting (LEH) model.

In this paper, we investigate the cache-enabled small cell networks to provide on-demand video services with differential perceptual qualities, i.e., standard definition video (SDV) and high definition video (HDV). As the extension technology of advanced video coding/H.264, scalable video coding is adopted in the considered networks and videos to be transmitted are divided into a base layer (BL) and N enhancement layers (ELs). In our proposed caching protocol, the n-th small cell base station (SBS) caches BLs and the n-th EL of the most popular videos. Depending on the distances between the typical user and SBSs in the observed cluster, the closest SBS is regarded as the serving node (SN) and the others are cooperative nodes (CNs). When SDV is required, the SN will transmit BL of the required video file to the typical user, while SN and CNs can cooperatively transmit BL and ELs to provide superior video quality if HDV is required. Based on the proposed caching and transmission protocol, we derive the expressions of the key performance indicators, i.e., local serving probability, ergodic service rate and service delay. Numerical results validate the theoretical analysis and show the superiority of our proposed scheme compared to the benchmarks.

This paper considers to mitigate the uplink cross- tier interference (CI) in heterogeneous networks (HetNets) by the idea of opportunistic transmission. Firstly, we introduce the conventional opportunistic interference alignment (OIA) into HetNets and it indeed reduces the CI from the macrocell users (MUs) effectively. Interestingly, based on the unique characteristic in HetNets, the performance of OIA can be further improved. Therefore, the novel opportunistic interference mitigation (OIM) schemes based on adaptive reference signal spaces (A-RSSs) are proposed. In the novel OIM, an A-RSS is defined for each FBS and the A-RSS of a FBS constantly updates as the channel state information (CSI) of its FUs changes. According to the A-RSSs of all FBSs, each MU calculates its scheduling metric and the macrocell base station (MBS) selects MUs by the metrics. However, broadcasting the A-RSS constantly makes the system more complicated, so a complexity- performance tradeoff is considered. To decrease the complexity of the system, the quantization codebook is introduced and the quantized A-RSS based OIM is proposed. At last, extensive simulations are conducted. It is shown that, in terms of femtocells, the performance of novel OIM is better than that of the codebook based OIM (CB-OIM) and the performance of CB-OIM is better than that of conventional OIA.

In this paper, distributed user scheduling schemes are proposed for the multi-user MIMO-Y channel, where three NT-antenna users (NT= 2N, 3N) are selected from three clusters to exchange information via an NR-antenna amplify-and-forward (AF) relay (NR= 3N), and N ≥ 1 represents the number of data stream(s) of each unicast transmission within the MIMO-Y channel. The proposed schemes effectively harvest multi-user diversity (MuD) without the need of global channel state information (CSI) or centralized computations. In particular, a novel reference signal space (RSS) is proposed to enable the distributed scheduling for both cluster-wise (CS) and group-wise (GS) patterns. The minimum user-antenna (Min-UA) transmission with NT= 2N is first considered. Next, we consider an equal number of relay and user antenna (ER-UA) transmission with NT= 3N, with the aim of reducing CSI overhead as compared to Min-UA. For ER-UA transmission, the achievable MuD orders of the proposed distributed scheduling schemes are analytically derived, which proves the superiority and optimality of the proposed RSS-based distributed scheduling. These results reveal some fundamental behaviors of MuD and the performance-complexity tradeoff of user scheduling schemes in the MIMO-Y channel.

A simple distributed user scheduling (DUS) scheme is proposed for the MIMO multi-way relay channel (MWRC), where K (K ≥ 2) M-antenna (M ≥ 2) users are selected from K clusters of users to conduct all-cast information exchange via an M-antenna amplify-and-forward (AF) relay. In particular, the proposed DUS is based on the individual performance metric calculated by each user with its local channel state information (CSI). Therefore, DUS bypasses global CSI and complicated computations at the scheduling center, both are often inevitable with traditional centralized user scheduling (CUS). Furthermore, the outage performance of DUS is analyzed and the achievable multi-user diversity (MuD) order is derived. Numerical results validate the theoretical derivations and show that DUS achieves comparable performances to CUS in the considered scenarios.

In this paper, we consider a K-cell multi-user interference network, where S single-antenna users are selected within each cell to carry out the uplink transmission with their M-antenna home base station, where 2 ≤ S ≤ M <; KS. For the considered scenario, a novel intra-cell performance aware opportunistic interference alignment (OIA) scheme is proposed to mitigate the inter-cell interference while reducing the intra-cell power loss caused by zero-forcing receiving. Unlike the traditional OIA schemes, the proposed scheme reuses the reference signal subspace (RSS) to balance not only the inter-cell interference but also the desired signal power and the intra-cell power leakage. It is shown that the refined selection further improves the achievable sum rate as compared to the existing schemes, and this observation is theoretically analyzed. Finally, numerical results validate that our scheme outperforms the existing uplink OIA schemes.