Stacked intelligent metasurface (SIM) and dual-polarized SIM (DPSIM) enabled wave-domain signal processing have emerged as promising research directions for offloading baseband digital processing tasks and efficiently simplifying transceiver design. However, existing architectures are limited to employing SIM (DPSIM) for a single communication function, such as precoding or combining. To further enhance the overall performance of SIM (DPSIM)-assisted systems and achieve end-to-end (E2E) joint optimization from the transmitted bitstream to the received bitstream, we propose an SIM (DPSIM)-assisted E2E orthogonal frequency division multiplexing (OFDM) system, in which traditional communication tasks such as channel coding, modulation, precoding, combining, demodulation, and channel decoding are performed synchronously within the electromagnetic (EM) forward propagation. Furthermore, inspired by the idea of abstracting real metasurfaces as hidden layers of a neural network, we propose the EM neural network (EMNN) to enable the control of the E2E OFDM communication system. In addition, transfer learning is introduced into the model training, and a training and deployment framework for the EMNN is designed. Simulation results demonstrate that both SIM-assisted E2E OFDM systems and DPSIM-assisted E2E OFDM systems can achieve robust bitstream transmission under complex channel conditions. Our study highlights the application potential of EMNN and SIM (DPSIM)-assisted E2E OFDM systems in the design of next-generation transceivers.

To mitigate the issue of limited base station coverage caused by severe high-frequency electromagnetic wave attenuation, Extremely Large Reconfigurable Intelligent Surface (XL-RIS) has garnered significant attention due to its high beam gain. However, XL-RIS exhibits a narrower beam width compared to traditional RIS, which increases the complexity of beam alignment and broadcast. To address this problem, we propose a variable-width beam generation algorithm under the near-field assumption and apply it to the near-field codebook design for XL-RIS. Our algorithm can achieve beam coverage for arbitrarily shaped codeword regions and generate a joint codebook for the multi-XL-RIS system. The simulation results demonstrate that our proposed scheme enables user equipment (UE) to achieve higher spectral efficiency and lower communication outage probability within the codeword region compared to existing works. Furthermore, our scheme exhibits better robustness to codeword region location and area variations.

In the future, NR-V2X networks and platoon-based driving modes will become essential components of Intelligent Transportation Systems. However, with the advent of the Internet of Vehicles, the large-scale deployment of sensors and the explosive growth of data are driving the need for new solutions. Mobile edge computing has emerged as a key technology for addressing the distributed computing requirements. In this paper, we propose a Joint Optimization framework based on Federated multi-agent Reinforcement Learning (JOFRL) to solve the task offloading and resource allocation problems in a platoon-based NR-V2X network. Existing RL algorithms focus on either the communication link capacity or the completion rate of computing tasks. Differently, we consider the mutual influence between the allocation of computing and communication resources. We modify the reward function in the MARL framework so that each agent's communication performance on V2V links is aligned with its computing performance. Our experimental results demonstrate that JOFRL outperforms other baseline algorithms. Specificly, JOFRL achieves improvements of 10.87%, 22.43% and 23.02% in computing and communication respectively compared with MADDPG, SAC and DDPG algorithm.

In this study, we investigate a two-user multiple-input multiple-output (MIMO) multiple access wiretap channel, in which the channel matrices of users exhibit rank deficiency. In this model, the legitimate transmitters and receiver are equipped with$M$and$N$antennas, respectively, while the eavesdropper has$K$antennas. The channel matrices of legitimate users are rank-deficient, whereas those between transmitters and the eavesdropper are full-rank, representing a worst-case scenario. We derive the optimal secure degrees of freedom (SDoF) for this model by combining two separate outer bounds. Considering the variations in the number of antennas at each node and the rank of user channels, we categorize our analysis into several regimes. We then present achievable schemes for each regime, grounded in spatial interference alignment, symbol extension and zero-forcing techniques.

The extensive deployment of the Internet of Things (IoT) devices has highlighted significant challenges related to data privacy, security, and communication bandwidth. Federated learning (FL), as a promising distributed machine learning paradigm, has been integrated with IoT to enhance data privacy, reduce latency, and improve learning quality. However, the integration of FL with IoT still faces inevitably challenges due to system heterogeneity and data heterogeneity. We propose an FL framework based on tensor-assisted heterogeneous model with quality-based aggregation (FedTHQ). FedTHQ can attain a global model with high accuracy and rapid convergence in heterogeneous IoT scenario. FedTHQ mainly includes following two subprocesses. First, the tensor-based heterogeneous model split scheme (TBFL) is proposed to develop a high-performance small model, which is applied in the FL initialization phase. In TBFL, each client reconstructs the local model according to the binary parameter groups. Second, we propose the quality-based aggregation scheme (QBFL) to assign appropriate weights to each local model considering the heterogeneity. QBFL is applied in the aggregation phase. Experimental results demonstrate the effectiveness and superiority of the proposed schemes. FedTHQ outperforms the benchmarks in global model accuracy, while achieving rapid convergence.

Travel time estimation is a key task in navigation apps and web mapping services. Existing deterministic and probabilistic methods, based on the assumption of trip independence, predominantly focus on modeling individual trips while overlooking trip correlations. However, real-world conditions frequently introduce strong correlations between trips, influenced by external and internal factors such as weather and the tendencies of drivers. To address this, we propose a deep hierarchical joint probabilistic model,ProbETA, for travel time estimation, capturing both inter-trip and intra-trip correlations. The joint distribution of travel times across multiple trips is modeled as a low-rank multivariate Gaussian, parameterized by learnable link representations estimated using the empirical Bayes approach. We also introduce a data augmentation method based on trip sub-sampling, allowing for fine-grained gradient backpropagation when learning link representations. During inference, our model estimates the probability distribution of travel time for a queried trip, conditional on spatiotemporally adjacent completed trips. Evaluation on two real-world GPS trajectory datasets demonstrates thatProbETAoutperforms state-of-the-art deterministic and probabilistic baselines, with Mean Absolute Percentage Error decreasing by over 12.60%. Moreover, the learned link representations align with the physical network geometry, potentially making them applicable for other tasks.

This paper investigates a novel client selection for the volatile Federated Learning (FL) systems, where volatility means that the state of the client set, client datasets, and client training status will change over time. We study how to select clients dynamically to mitigate the volatility. Particularly, the volatile client selection problem is formulated as a classification problem, and we propose two new metric features. The Multi-Feature Volatile Client Selection (MFVCS) algorithm, which considers client training capacity, client-weighted data quality, and client historical selection entropy, is proposed to solve the volatile client selection problem. Moreover, we have developed an adaptive dynamic weighting algorithm that allows for dynamic updating of the weight for each feature. We propose a volatility ratio to measure client volatility. The experimental results indicate that the proposed algorithm demonstrates strong robustness and better performance under different volatility ratios of the client set. In particular, the proposed MFVCS algorithm improves the model accuracy at most by $\mathbf{9.2\%}, \mathbf{9.6\%}$ and $\mathbf{12.5\%}$ under 0.01 volatility ratio, 0.05 volatility ratio and 0.1 volatility ratio, respectively.

With the development of 5G, the coexistence problem of the enhanced Mobile Broadband (eMBB) users and the Ultra-Reliable Low Latency Communications (URLLC) users poses a significant challenge to existing schedule methods. Existing solutions, such as reserved resource schedules, puncturing schedules, and hybrid puncturing schedules combined with non-orthogonal multiple access (NOMA) technologies, have effectively addressed the competition of resource blocks for communication between URLLC and eMBB services. However, no viable solution has been proposed to address the resource competition problem between eMBB and URLLC services, while ensuring a relatively equitable transmission of eMBB services. In this paper, we propose a dynamic resource allocation method, which has a multiplexing mode of puncturing preemptive schedule and non-orthogonal multiple access (NOMA). Once the multiplexing mode is determined, subsequent resource scheduling is performed based on the fair priority of eMBB users. We introduce a deep reinforcement learning network based on Dueling Deep QNetwork (DDQN) to ensure the long-term gains of this scheduling strategy. Simulation results demonstrate that the proposed method achieves the maximum network throughput while satisfying the QoS requirements of URLLC services, ensuring communication fairness for eMBB services.

This paper delves into the dynamic resource allocation challenges for spectrum sharing in Vehicle-to-Everything (V2X) communication, influenced by the time-varying channel conditions. We concentrate on the dual goals of sub-channel and power allocation in complex V2X environment, underscoring the pivotal role that the Age of Information (AoI) plays in preserving the reliability of safety-critical data across Vehicle-to-Vehicle (V2V) links. To address the complex interplay between minimizing AoI for V2V links and maximizing the overall capacity for vehicle-to-infrastructure (V2I) links, we introduce a novel approach grounded in multi-agent reinforcement learning. This strategy enables adaptive learning in response to V2X rapidly changing channel conditions, with V2V links conceptualized as agents. These agents employ an actor network to select actions and a critic network to evaluate those actions through Q-values, incorporating observations, actions, and individual contributions via attention mechanisms. Our method is further refined by incorporating maximum entropy to enhance action exploration. Through extensive simulations, we demonstrate that our algorithm allows agents to effectively sample and assess the states of their counterparts, leading to optimized decision-making processes.

Platoon-based vehicular network in NR-V2X has been considered as a promising technology to assist reducing traffic congestion, saving vehicle fuel, and enhancing driving experience. Resource allocation is the basis for ensuring stable and safety vehicular networks. In this paper, we propose a Distributed Resource Allocation algorithm using Federated Multi agent Deep Reinforcement Learning (DRAFRL), which mathematically utilize the federated averaging (FedAvg) mechanism to reduce the variance between agents and achieve better transmission performance. The proposed algorithm consists of four steps: Firstly, each agent updates local model by deep deterministic policy gradient (DDPG) algorithm. Secondly, the agents upload local model parameters to the base station (BS) for federated aggregation. Thirdly, the BS performs weight aggregation using the FedAvg method and updates the global model. Finally, the BS distributes the optimized global model parameters to each agent. The simulation results show that the proposed algorithm outperforms other baseline algorithms while reducing the variance between agents by 93.5% and 99.1% compared with two baselines.

As an important research field in time series processing, traffic prediction has a profound impact on people’s daily lives and social development. Conventional traffic prediction relies on complete observation data. However, data missing is common in cities due to equipment failure, network interruption, etc., which poses a huge obstacle to traffic prediction. In this paper, we design a novel Spatiotemporal Ego-graph Domain Adaptation framework (SEDA) to predict traffic state in data missing scenarios. Based on the multi-dimensional topological information of local network (ego-graph), isomorphic ego-graphs are aligned across the missing data in target domain and the external data in source domain to obtain alternative data. Furthermore, a Dual-branch Cross reCoupling method (DCC) is proposed to reconstruct missing features according to the alternative data. Experimental results on real public datasets with 10%-40% missing show that SEDA averagely outperforms both the state-of-the-art knowledge transfer-based prediction baselines and the incomplete data prediction baselines by more than 0.45% and 0.86%. Ablation experiments and visualization analysis further demonstrate the effectiveness of SEDA components.

As a promising distributed learning technology, federated learning (FL) is used in wireless communication to efficiently utilize distributed data. However, statistical heterogeneity is often ignored as a crucial factor affecting wireless federated learning (WFL) performance. Besides, free rider is common in real world. In this paper, we consider the statistical heterogeneity and free rides jointly with limited resources. We first define a new measurement considering the substitutability and wholeness of client, called contribution degree. Then we propose the Contribution Degree-based Client Selection (CDCS) algorithm to improve WFL performance. Experiments validate that the proposed algorithm improves the global model accuracy, achieves fast convergence and reduces total delay.

Abstract Deep neural network (DNN) shows its powerful performance in terms of image classification and many other applications. However, as the number of network layers increases, it brings huge pressure on devices with limited resources. In this article, a novel network compression algorithm is proposed that compresses the original network by up to about 60 times. In particular, a tensor Canonical Polyadic(CP) decomposition based algorithm is proposed to compress the weight matrix in the fully connected(FC) layer and the convolution kernel in the convolution layer. Traditional tensor decomposition algorithms are usually to first pre‐train the weights, and decompose the weights, finally perform fine‐tuning on the factors in the second training phase. Instead, the decomposed factors are directly updated by performing tensor CP decomposition on weight without fine‐tuning. The proposed algorithm is called Fast CP‐Compression Layer method in this paper. Experiments show that the proposed algorithm cannot only reduce computing time and improve compression factor but also improve accuracy on some datasets.

Traffic supply-demand mismatching has a severe impact on intelligent transportation systems. Fortunately, order dispatching is a promising option to mitigate the traffic supply-demand imbalance. Along this line, this article proposes the Multi-Driver Multi-Order Dispatching (MDMOD) method to make efficient order dispatching policy and enhance the experience of drivers and passengers. In the proposed MDMOD method, the Dynamic Multi-Objective Reward Learning (DMRL) algorithm is proposed to measure the driver-order-pair value, which illustrates the importance of a driver serving a specific order. A centralized matching algorithm is introduced to match all drivers and orders to maximize all driver-order-pair values. The multi-objective reward in the DMRL algorithm considers both immediate gains (i.e., pick-up distance) and future gains (i.e., the future traffic demand of order destination) to effectively improve the experience of drivers and passengers. Furthermore, by introducing the driver service level into the multi-objective reward, the “outstanding driver better reward” mechanism is realized to promote the ecological development of ride-sharing platforms. Notably, the Temporal-Graph Convolutional Network algorithm is proposed to predict the future traffic demand. Some virtual orders, which generated with the predicted future traffic demand, are dispatched to idle drivers to multiplex the traffic supply fully. A simulator is designed to test the performance of the proposed MDMOD method, experimental results demonstrate that the MDMOD method outperforms the state-of-the-art methods in terms of Average Driver Income and Order Response Rate.

Reconfigurable intelligent surfaces (RISs) become promising for enhancing non-orthogonal multiple access (NOMA) systems, i.e., enhancing the channel quality and altering the SIC orders. Invoked by stochastic geometry methods, we investigate the downlink coverage performance of RIS-aided multi-cell NOMA networks. We first derive the RIS-aided channel model, concluding the direct and reflecting links. The analytical results demonstrate that the RIS-aided channel model can be closely modeled as a Gamma distribution. Additionally, interference from other cells is analyzed. Lastly, we derive closed-form coverage probability expressions for the paired NOMA users. Numerical results indicate that 1) although the interference from other cells is enhanced via the RISs, the performance of the RIS-aided user still enhances since the channel quality is strengthened more obviously; and 2) the SIC order can be altered by employing the RISs since the RISs improve the channel quality of the aided user.

Influenced by the era of the sharing economy and mobile payment, Dockless Bike-Sharing System (Dockless BSS) is expanding in many major cities. The mobility of users constantly leads to supply and demand imbalance, which seriously affects the total profit and customer satisfaction. In this paper, we propose the Spatio-Temporal Mixed Integer Program (STMIP) with Flow-graphed Community Discovery (FCD) approach to rebalancing the system. Different from existing studies that ignore the route of trucks and adopt a centralized rebalancing, our approach considers the spatio-temporal information of trucks and discovers station communities for truck-based rebalancing. First, we propose the FCD algorithm to detect station communities. Significantly, rebalancing communities decomposes the centralized system into a distributed multi-communities system. Then, by considering the routing and velocity of trucks, we design the STMIP model with the objective of maximizing total profit, to find a repositioning policy for each station community. We design a simulator built on real-world data from DiDi Chuxing to test the algorithm performance. The extensive experimental results demonstrate that our approach outperforms in terms of service level, profit, and complexity compared with the state-of-the-art approach.

This letter proposes a new travel time estimation model based on graph neural network (GraphTTE) to improve the accuracy of travel time estimation. We design a Multi-layer Spatiotemporal Graph frame (MSG), which consists of static network and dynamic networks, to fully consider the influence of traffic temporal characteristics and road network topological characteristics on travel time. Moreover, we design an Attention Graph Nodes Impact Index algorithm (AGNII) to score the impact of each node on travel time. In particular, the dynamic networks utilize the graph convolution network and gate recurrent unit to obtain the traffic characteristics, the static network utilizes graph convolution network to obtain the road basic attributes. We combine the real paths sequence with the impact score of nodes to extract the subgraph with a great impact on the trajectory. After the graph representation learning and deep residual network, the estimated time is obtained. A simulator was designed to train and test our model in Chengdu and Xi'an datasets, the results show that the mean absolute percent error (MAPE) is 12.58% and 14.01%, which is 1.54% and 1.78% lower than the baselines.

In this paper, a fast deployment strategy of unmanned aerial vehicles (UAVs) served as base stations (BSs) in an object region is investigated. To be specific, it solves a problem of how to find proper BSs position for multi-UAV as quickly as possible, and it also achieves the goal of maximizing the sum of downlink rates in a communication network. For this purpose, we design a geographical position information learning (GPI-Learning) algorithm to learn the GPI relationship between users and UAVs. This approach consumes less time by avoiding calculation of exact channels and fills a gap existed in the scenario of setting multi-UAV rapidly to serve multi-user. Without loss of generality, we apply GPI-Learning in different scenarios, such as changes in user number or area size. As for different area size, simulation reveals that a proper size is adequate to any smaller size on condition that the smaller size is included in training set. Numerical results witness the good performance of our proposed algorithm.

In this paper, an unmanned aerial vehicle (UAV) flies as a base station (BS) to provide wireless communication service. We propose two algorithms for designing the trajectory of the UAV and analyze the impact of different training approaches on transferring to new environments. When the UAV is used to track users that move along some specific paths, we propose a proximal policy optimization (PPO) -based algorithm to maximize the instantaneous sum rate (MSR-PPO). The UAV is modeled as a deep reinforcement learning (DRL) agent to learn how to move by interacting with the environment. When the UAV serves users along unknown paths for emergencies, we propose a random training proximal policy optimization (RT-PPO) algorithm which can transfer the pre-trained model to new tasks to achieve quick deployment. Unlike classical DRL algorithms that the agent is trained on the same task to learn its actions, RT-PPO randomizes the features of tasks to get the ability to transfer to new tasks. Numerical results reveal that MSR-PPO achieves a remarkable improvement and RT-PPO shows an effective generalization performance.

We consider the degrees of freedom (DoF) of the 2 × 2 × 2 multiple-input multiple-output (MIMO) interference network with arbitrary antenna configuration and practical rank-deficiency. Different from existing researches on the 2 × 2 × 2 MIMO interference model, the antenna number can be arbitrarily different at the transmitters, relays and receivers simultaneously, and the ranks of the channel matrices can be arbitrarily different. The exact DoF sum of this scenario is obtained. For different configurations of antennas and ranks, the outer bound is proved mainly by the min-cut outer bound and the achievability is presented with effective schemes, which combine zero-forcing (ZF) scheme, ZF over broadcast channel, interference alignment in X channel, and aligned interference neutralization (AIN) to match the outer bound. The traditional AIN scheme is modified in this paper to maximize the utilization of signal space overlap. Some related earlier works can be seen as special cases of our research.

We investigate a general statistical inference framework based on the wire-tap channel to character the privacy threat. Based on this framework, data is distorted by a privacy-preserving mapping before being released, which can minimize the privacy leakage under distortion constraints. We demonstrate the optimal privacy-preserving mapping can be acquired by convex optimization. Moreover, considering a more practical situation, we provide the upper bounds of the effect on the true privacy leakage and corresponding distortions when the estimated probability distribution is used as an input to the optimization problem. These bounds declare that there is a little impact on privacy-accuracy-secrecy tradeoff if we estimate the joint probability well. Finally, we derive the corresponding lower bounds of the error probability in inferring private data from observations on account of the case where true distribution is known, and other case where there is only the estimate of the prior distribution.

Jamming attack is a serious threat to wireless communications. Reactive jammer is one of the most power-efficient jammers, which can listen for activities on channel and jam receivers when it detects communications. But to the best of our knowledge, there is no existing research of defense scheme focused on sustaining the multi-user and multi-antenna communications under reactive jamming attack. In this paper, we propose iterative estimation algorithm and interference cancellation algorithm to maintain the multiuser and multi-antenna orthogonal frequency-division multiplexing (OFDM) communications under reactive jamming attack. The iterative estimation algorithm means inserting some pilots in frames of the transmitted signals to estimate the channel matrixes. Furthermore, in order to remove interference, the received signals are projected onto the orthogonal subspace of the jamming signals by using the interference cancellation algorithm. Finally, the simulation results prove that the multi-user and multi-antenna OFDM communications are nearly throttled by jamming attack and our defense mechanisms can effectively turn it into operational scenario with considerable performance under reactive jamming attack.

In this paper, we study the degrees of freedom (DoF) characterization of cache-aided 8-user multi-hop line network. The leftmost user 1 wishes to send messages to the remaining users and the messages are relayed by user 2, 3,..., 7. Each user is equipped with a local cache and we assume that the messages received by userkcan also be cached. Thus, interference signals from the messages transmitted by userk+ 1,k+ 2, ... and 8 can be eliminated since userkhas already cached the contents of messages in previous data transmission. With this cache-aided scheme, the multi-hop topology can be transformed into a special partially connected interference channel (IC). Then we derived the DoF outer bound of the network information-theoretically. To deal with the challenge of this partial connected IC model, where the number of interferences increases with the indexk, we proposed a DoF outbound achievable transmission method by assigning equidifferent signal space allocation.

Interference is a key problem for multi-cell networks and interference alignment (IA) is a promising method to manage interference. This paper investigates whether a feasible IA scheme can improve the system performance of Device-to-Device(D2D) communication underlaying multiple-input-multiple-output (MU-MIMO) uplink in multi-cell networks. We propose a novel cell division pattern based on conventional fractional frequency reuse (FFR) to create user clusters to eliminate interference. For the intra-cell IA-cluster and inter-cell IA-cluster, we present the feasibility of IA, a feasible configuration and give the corresponding precoding, decoding matrices. The simulation results show that IA acquiring user clustering gains outperform the multiplex scheme and orthogonal scheme. Additionally, the optimal frequency reuse partitioning radius is found to obtain the highest spectrum efficiency.

Device-to-Device (D2D) communication underlaying cellular networks is expected to bring significant benefits for resource utilization and cellular coverage. However, the resource allocation and power control needs elaborate coordination, otherwise it may cause severe interference between D2D user equipment (DUE) and cellular user equipment (CUE). In this paper, we study the joint radio resource allocation and power control problem with energy efficiency as our optimization goal. To improve the system energy efficiency with low computational complexity, we propose an iterative combinatorial auction algorithm with flexible power control method, where the CUEs are considered as bidders, DUEs as goods and the cellular network plays a role as the auctioneer controlling the auction process. We also analyze the properties of the proposed algorithm and present numerical results to verify that our algorithm can significantly improve energy efficiency.

We consider the muti-user muti-content downloading service with the help of idle users working as contents servers in device-to-device (D2D) underlay network. Taking the contents distribution into account, we propose a hybrid transmitting strategy and investigate the performance enhancement it brings about. Then by formulating the helper finding and resource allocation as a minimum downloading time problem, we propose a low complexity scheme to determine: (1) the idle user which should serve as helper for each downloader through D2D underlay communication (2) the cellular user which shares the uplink resource with less interference. Numerical simulations show that the strategy of taking contents distribution into account can bring significant performance gain. And our proposed scheme is presented to have near optimal performance, especially works well when the contents reuse rate is high.

To meet the ever-increasing data traffic demand, heterogeneous cellular networks (HCNs) are to be deployed for wireless communication and coordinated multipoint (CoMP) is thought to be desirable to further improve the performance of HCNs. This paper considers the problem of base station (BS) cooperation in the downlink of HCNs. A novel and flexible cross-tier cooperative scheme is proposed in the paper. Our cooperative scheme allows BSs in each tier to cede their original cell range to cooperative region, thus extent the cooperative region, where users are better served through cross-tier cooperation. Using tools from stochastic geometry, we derive expressions for the outage probability as well as average ergodic rate of our scheme. Simulation results are given to verify the accuracy of our analysis and numerical results indicate that our cooperative scheme reduces the outage probability by about 21.2% and increases the average ergodic rate by about 9.2%, compared with the traditional maximum reference signal received power (RSRP) scheme.

This paper presents the measurement results of channel attenuation characteristics of low voltage access network in China. The measurement campaign was performed in typical urban and rural residential areas, which represents the underground cable and the overhead line topologies respectively. Both narrow-band (30-500 kHz) and broad-band (500 kHz-20 MHz) attenuations are investigated. Based on the extensive measurement results, statistical methods are used in the comparison of the average signal attenuation obtained in different areas, the attenuation profile with coupling mode match/mismatch, the attenuation dynamic range at different frequencies. These analyses may provide a comprehensive understanding of the representative channel attenuation characteristics for the access domain. Besides, the classical multipath model was used to model the broad-band (0.5-20 MHz) PLC channel after simplified. Results indicated that the simplified model covers the practical channel quite well.

It is well known that relay is able to neutralize some interferences in destinations. In this paper we use not only relay to neutralize interferences but also interference alignment to reduce the number of antennas needed in destinations. To this end, a scheme of cognitive relay-aided interference neutralization and alignment is proposed. To neutralize and align interferences, the relay needs to retransmit the signals using proper transmitting vectors. We demonstrate that the 3-user MIMO interference channels with cognitive relay can achieve 2M degrees of freedom (DoF) when each node has M antennas. It is a big improvement compared to k-user system using interference alignment which is able to obtain 3M/2 DoF. The transmitting vectors in sources and relay are carefully designed to not only satisfy the neutralization and alignment constraints but also achieve higher sum-rate, which can be proved by simulation results.

The adaptive AF relay combined with the active antenna system (AAS) and angle estimation strategy is studied in this paper. To improve the system performance, we introduce the AAS scheme into AF relay. Meanwhile, we also propose a joint AAS relay (JAR) angle estimation strategy to obtain the useful horizontal angle information. Results show that, in the temporary cover or emergency communication scene, the adaptive AF relay with AAS helps to improve the system performance gain effectively and also enhance the robustness stably.

We propose a Groupwise algorithm for distributed transmit beamforming featuring high convergence rate based on the existing Pairwise algorithm. The source nodes are divided into K groups to adjust intergroup phase offsets in turn using the feedback from the receiver. Phase synchronization of all source nodes is achieved by iteratively generating new random grouping patterns. By theoretical modeling and numerical simulation, we find that the proposed algorithm saves 40% of convergence time of the Pairwise algorithm at K = 5, and more at larger K values. Larger group numbers also result in robustness against phase noise in the sense of steady-state mean and fluctuation. Costs and limitations of larger K values are pointed out as well: 2(K - 1)/K of the feedback used in Pairwise algorithm is now necessary, and performance degradation is observed in AWGN channels if each group has relatively small numbers of nodes at a given noise level.

This paper addresses joint source-relay precoder and decoder designs for a single data flow transmission in amplify-and-forward (AF) multiple-input-multiple-output (MIMO) relay networks with imperfect channel state information (CSI). First, the precoder is obtained by improving the lower bound of the received SNR under power constraints at source and relay. Then, we derive the decoder to maximize the average received signal-to-noise ratio (SNR). Numerical results show a great improvement on the received SNR by applying our proposed schemes. Besides, we also make a discussion about the impact which brought by the channel correlation coefficients based on our derived received SNR expression and the numerical results.

This paper presents Customer Satisfaction (CS) based resource allocation strategy in orthogonal frequency-division multiplexing (OFDM) wireless system with multimedia traffic. The risk aversion utility functions are analyzed, based on which, the CS utility and the CS resource allocation strategy are proposed. Compared with the Proportional Fairness (PF) utility, the CS utility enables the system to adjust its resource allocation according to both the traffic requirements and the resource situation. Numerical results demonstrate that the CS resource allocation strategy outperforms the PF strategy in both real-time (RT) traffic and best effort (BE) traffic.

This paper presents a simplified V-BLAST (vertical bell lab layered spaced-time) detection algorithm from the hardware implement perspective. Simulation shows that the BER (Bit Error Rate) performance is close to Golden detection algorithm, but the complexity is greatly less. Then the paper provides an efficient hardware structure to implement this algorithm in FPGA (field programmable gate array), which can be used in the B3G TDD-MIMO-OFDM (Beyond 3G Time-Duplex-Division Multi-Input Multi-Output Orthogonal frequency division multiplexing) system. By applying bit-width reduction technique, the fabrication area it takes can be significantly reduced. In uplink, we adopted 4 transmit and 8 receive antennas. The implementation with Virtex square Pro Series FPGA was verified to be worked well in B3G system.