The coexistence of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication in 6G-enabled vehicular networks introduces complex challenges in spectrum sharing, semantic prioritization, and real-time coordination. To address these issues, we propose G-FEDMAP, a graph-based federated multi-agent deep reinforcement learning framework that supports semantic- and intent-aware resource allocation in distributed vehicle-to-everything (V2X) environments. G-FEDMAP integrates GraphSAGE-based spatiotemporal embeddings, shared actor–centralized critic multi-agent proximal policy optimization (MAPPO) training under a centralized training and decentralized execution (CTDE) paradigm, and event-adaptive reward shaping guided by user intent profiles. To preserve data locality and promote scalable collaboration, federated policy coordination is introduced across geographically partitioned vehicular domains. The system dynamically rebalances semantic priorities using intent reprioritization weights, enabling responsiveness to mission-critical context. We evaluate G-FEDMAP in a federated urban vehicular network comprising multiple regions with high agent density, dynamic event intents, and shared spectrum constraints. The proposed framework is tested under diverse communication and traffic conditions, and compared against MAPPO, graph neural network-MAPPO (GNN-MAPPO), and federated PPO variants. G-FEDMAP demonstrates improved V2V delivery success, higher semantic retention, greater intent satisfaction, and better coexistence of V2V and V2I links through graph-based scheduling and federated policy adaptation. These results position G-FEDMAP as a reliable and trustworthy AI-driven solution for future 6G-internet of things (6G-IoT) vehicular networks.

In this paper, we propose a novel semantic communication framework (SCF6) tailored for 6G-enabled vehicular networks, targeting ultra-reliable low-latency communication (URLLC) scenarios. By integrating semantic encoding/decoding with traditional channel processing, our framework optimizes data transmission, focusing on the meaning of the data rather than raw information. To quantify the integrity of the transmitted messages, we employ advanced natural language processing techniques, such as BERT (bidirectional encoder representations from transformers), ensuring semantic similarity between the sent and received information. We formulate an optimization problem that maximizes semantic spectrum efficiency evaluation (SSEE) and success rate (SR) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications under stringent URLLC constraints. The optimization solutions are solved by the multi-agent hierarchical attention-based semantic deep reinforcement learning (MAHAS-DRL) framework, which coordinates resource allocation and spectrum sharing among multiple vehicles. By incorporating hierarchical attention mechanisms at both semantic and channel levels, MAHAS-DRL enhances decision-making, optimizes transmission power, and reduces interference. Extensive simulation results demonstrate that our proposed framework outperforms traditional DRL approaches in terms of spectrum efficiency and reliability while significantly reducing transmission delays, making it ideal for dynamic urban vehicular networks.

In this paper, a green learning (GL)-based precoding framework is proposed for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-aided millimeter-wave (mmWave) MIMO broadcasting systems. Motivated by the growing emphasis on environmental sustainability in future 6G networks, this work adopts a broadcasting transmission architecture for scenarios where multiple users share identical information, improving spectral efficiency and reducing redundant transmissions and power consumption. Different from conventional optimization methods, such as block coordinate descent (BCD) that require perfect channel state information (CSI) and iterative computation, the proposed GL framework operates directly on received uplink pilot signals without explicit CSI estimation. Unlike deep learning (DL) approaches that require CSI-based labels for training, the proposed GL approach also avoids deep neural networks and backpropagation, leading to a more lightweight design. Although the proposed GL framework is trained with supervision generated by BCD under full CSI, inference is performed in a fully CSI-free manner. The proposed GL integrates subspace approximation with adjusted bias (Saab), relevant feature test (RFT)-based supervised feature selection, and eXtreme gradient boosting (XGBoost)-based decision learning to jointly predict the STAR-RIS coefficients and transmit precoder. Simulation results show that the proposed GL approach achieves competitive spectral efficiency compared to BCD and DL-based models, while reducing floating-point operations (FLOPs) by over four orders of magnitude. These advantages make the proposed GL approach highly suitable for real-time deployment in energy-and hardware-constrained broadcasting scenarios.

In this study, we propose SCF6, a novel semantic communication framework for 6 G -enabled vehicular networks tailored to ultra-reliable low-latency communication (URLLC) scenarios. SCF6 integrates semantic encoding/decoding with conventional channel processing, optimizing transmission by focusing on data meaning. Leveraging BERT (bidirectional encoder representations from transformers)-based natural language processing, it ensures high semantic similarity between transmitted and received messages. To maximize semantic spectrum efficiency (SSEE) and success rate (SR) for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications under strict URLLC constraints, we design a multi-agent hierarchical attention-based semantic deep reinforcement learning (MAHAS-DRL) framework. MAHASDRL coordinates resource allocation and spectrum sharing, embedding hierarchical attention at both semantic and channel levels to enhance decision-making, optimize power control, and reduce interference. Simulations demonstrate SCF6's superiority over traditional DRL methods in spectrum efficiency, reliability, and latency, proving effective for dynamic urban vehicular networks.

In this study, we introduce a digital twin (DT)-assisted IoV framework that combines a semi-synchronous adaptive federated learning (AdFL) method with multi-agent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAE). This framework optimizes partial task offloading across distributed mobile edge computing (MEC) servers, ensuring scalable and efficient decision-making in diverse vehicular networks. By continuously reflecting the real-time conditions of vehicles and roadside units (RSUs), the DT framework ensures precise resource distribution and adaptive task handling. To handle the complexity of dynamic environments, we develop a global model that includes transformer layers in the federated learning (FL) process, which captures long-range dependencies. A semi-synchronous aggregation mechanism is introduced to maintain a balance between timely updates and model quality. The adaptive federated multi-agent reinforcement learning (AF-MARL) algorithm enables decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost and energy use, reducing delays, and improving task completion rates. Comprehensive simulations show the framework's effectiveness compared to existing methods, emphasizing its potential to revolutionize real-time decision-making in IoV networks.

Achieving real-time decision-making and efficient resource management in dynamic, large-scale Internet-of-Vehicles (IoV) networks is a significant challenge due to their inherent complexity and scale. To address this, we propose a digital twin (DT)-assisted IoV framework that integrates a novel semi-synchronous adaptive federated learning (AdFL) approach with multiagent deep reinforcement learning, enhanced by generative artificial intelligence (GenAI) techniques, specifically conditional variational autoencoders (CVAEs). The framework optimizes partial task offloading across distributed mobile-edge computing (MEC) servers, ensuring scalable, efficient, and accurate decision-making in heterogeneous vehicular networks. By continuously mirroring the real-time states of vehicles and roadside units (RSUs), the DT framework enables precise resource allocation and adaptive task management. To tackle the complexities of dynamic environments, we design a global model with transformer layers embedded in the federated learning (FL) process, capturing long-range dependencies. A novel semi-synchronous aggregation mechanism is introduced to balance timely updates with model quality. The proposed adaptive federated multiagent reinforcement learning (AF-MARL) algorithm facilitates decentralized, collaborative learning among vehicles and RSUs, optimizing overall cost, and energy efficiency, reducing delay, and improving task completion rates. Extensive simulations demonstrate the effectiveness of the proposed framework against other existing approaches, highlighting its potential to transform real-time decision-making in IoV networks.

In this paper, we consider a novel hybrid reconfigurable intelligent surface (HRIS) consisting of active as well as passive reflecting elements mounted on unmanned aerial vehicle (UAV). The aim is to improve air-to-ground communication by assisting multiple users, while detecting several low mobility targets. We formulate a sum-rate optimization problem that accounts for statistical channel estimation errors (SCEEs) to concurrently fine-tune both active and passive phase-shift matrices, UAV trajectory, and transmit beamformer for integrated sensing and communication (ISAC). Subsequently, we introduce a transfer learning based approach combining with deep deterministic policy gradient (DDPG) to enhance the overall data rate while minimizing the time it takes for users to transmit data. Additionally, we present an alternating optimization (AO) algorithm that employs a repetitive method to address the combinatorial nonconvex optimization problem and offers a solution that is very close to optimal. Finally, we showcase the superiority of the proposed scheme through Monte Carlo simulations. Also, we have compared the performance with perfect channel state information (CSI) counterpart. The outcomes of simulations confirm the theoretical analysis and demonstrate the efficiency of the proposed framework. Additionally, the results reveal the advantages of incorporating HRIS aided UAV assisted ISAC in improving the quality of both communication and sensing performance.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO) based approach for the DRL-based decision-making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, non-uniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that mPPO outperforms conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential for real-world applications.

In this paper, we propose Capacity-Net, a novel unsupervised learning approach aimed at maximizing the achievable rate in reflecting intelligent surface (RIS)-aided millimeter-wave (mmWave) multiple input multiple output (MIMO) systems. To combat severe channel fading of the mmWave spectrum, we optimize the phase-shifting factors of the reflective elements in the RIS to enhance the achievable rate. However, most optimization algorithms rely heavily on complete and accurate channel state information (CSI), which is often challenging to acquire since the RIS is mostly composed of passive components. To circumvent this challenge, we leverage unsupervised learning techniques with implicit CSI provided by the received pilot signals. Specifically, perfect CSI is usually required to evaluate the achievable rate as a performance metric of the current optimization result of the unsupervised learning method. Instead of channel estimation, the Capacity-Net is proposed to establish a mapping among the received pilot signals, optimized RIS phase shifts, and the resultant achievable rates. In our simulation, the proposed Capacity-Netbased unsupervised learning without channel estimation obtains performance similar to that of an iterative optimization algorithm and the unsupervised learning that is trained by perfect CSI in terms of achievable rates.

To attain diverse performance benchmarks for 6G systems, multi-input-multi-output (MIMO) systems assisted by reconfigurable intelligent surface (RIS) have been investigated intensively. Various deep learning (DL) based methods have been adopted to perform the non-convex optimization of such systems, which leads to significant enhancement in the spectral efficiency. However, the DL-based methods rely on the operations of neural network, which demands high computational cost and usually requires perfect channel state information (CSI). To tackle the challenge, we develop a green learning (GL) based method to optimize the system with the input of the received pilot signals. Bypassing the channel estimations, the proposed algorithm first extract the representations from the received pilot signals, following by selecting the most relevant representations for the precoding optimization. The model size of the proposed method can be greatly reduced by the analysis of the dominant subspace as well as relevant feature selection. It shows through simulation that the proposed method slightly outperforms the DL-based method with simply $1.3 \sim 1.8 \%$ floating-point operations (FLOPs).

In this work, we explore the use of active reconfigurable intelligent surfaces (A-RIS) to improve vehicle-to-everything (V2X) communication systems to address limitations in traditional vehicular communication. In particular, we formulate an optimization problem to maximize the uplink sum rate for vehicle-to-infrastructure (V2I) links by optimizing transmit precoders, phase-shift matrices, transmit power, and spectrum sharing for vehicle-to-vehicle (V2V) links. To handle complex hybrid control scenarios, we propose a mixed-action deep reinforcement learning (DRL) algorithm and compare it with conventional benchmark methods like deep deterministic policy gradient (DDPG) with discrete actions (DA) and alternating optimization (AO). We evaluate the proposed algorithm’s effectiveness under imperfect channel state information as well. Simulation results highlight the efficacy of our approach, demonstrating significant enhancement in vehicular communication quality through A-RIS. Furthermore, we illustrate the impact of various factors such as number of A-RIS elements, vehicle speed, loss, execution time, amplification power, and CSI error on the performance of the V2X system.

In this article, we propose a novel framework that combines simultaneous localization and communication (SLAC) using a reconfigurable intelligent surface (RIS) aided integrated sensing and communication (ISAC) systems. Our primary focus is on enhancing resource efficiency in such systems. We introduce Cloud Radio Access Networks (C-RAN) that facilitate collaboration between multiple base stations (BSs), enhancing cooperation benefits for both communication and sensing capabilities. To evaluate localization performance, we formulate an optimization problem to minimize the squared position error bound (SPEB) that reflects the system functional performance by optimizing the transmit beamformer, phase shift and subcarrier assignment under certain constraints. Moreover, in order to adjust the phase shift of the RIS, we propose a RIS-aided cooperative ISAC SLAC protocol. This approach utilizes the measurements collected to refine the location and velocity estimates of the agent, as well as to reconstruct the environmental map with enhanced accuracy. However, the high dimensionality of the decision space makes the problem computationally intensive and challenging to navigate using gradient-based or exhaustive search methods. To efficiently tackle these issues, we construct a framework based on Markov decision processes (MDPs) and address it by introducing a novel algorithm called hybrid deep reinforcement learning (HDRL) algorithm. We validate our proposed algorithm through various simulations, demonstrating its effectiveness in improving system performance by comparing with the baseline schemes.

Vehicular crowdsensing (VCS) within the social Internet of Vehicles (IoV) significantly advances urban transportation management by enhancing road safety, traffic efficiency, and the overall driving experience. This article presents an intelligent multiagent deep reinforcement learning (DRL) framework for augmented dynamic task prioritization in a multi-incentive VCS system. Our framework, named intelligent multiagent reinforcement learning (IMARL), leverages augmented intelligence to integrate human-like decision-making processes with autonomous vehicle operations, ensuring more adaptive and robust task management. The proposed IMARL framework offers several key advantages: it dynamically adjusts the sensing levels of each vehicle, ensuring efficient energy usage and minimized processing times, and employs a data-quality aware multi-incentive utility model to capture both functional and social incentives. Additionally, our framework incorporates a layered server architecture, enhancing system resilience and scalability. Simulation results demonstrate the superiority of our approach. IMARL achieves significant improvements in task completion rates, energy consumption, and processing delays compared to other DRL and non-DRL benchmark methods. Furthermore, our approach exhibits strong adaptability to changing environmental conditions, maintaining high performance even in high-density traffic scenarios. These quantified results validate the effectiveness of the proposed framework, highlighting its potential to significantly enhance VCS systems in real-world applications.

In this study, we present a hybrid deep reinforcement learning (DRL) algorithm, trained using vehicular federated learning (VFL), specifically tailored for dynamic vehicular networks with historical data. Our approach utilizes VFL-based DRL to refine the caching scheme in these networks, focusing on predicting and storing the most effective content nearby to enhance cache efficiency and reduce content request delays. We propose a modified proximal policy optimization (mPPO)-based approach for the DRL-based decision making for caching management, which combines the advantages of proximal policy optimization (PPO) and double deep Q-network (DDQN). Our study encompasses a vehicular framework that includes a central edge node (CEN), roadside units (RSUs), unmanned aerial vehicles (UAVs), and vehicles equipped with historical data. We tackle the challenges posed by varying vehicle density and mobility, nonuniform RSU coverage, and constrained caching capacity. Through comprehensive simulations, we demonstrate that the mPPO outperforms the conventional DRL methods like PPO and DDQN, as well as heuristic approaches. These results underscore the efficacy of the VFL-based mPPO in dynamic vehicular networks, confirming its potential as a viable solution for real-world applications.

In this work, we consider a full-duplex (FD) communication system that uses a simultaneous transmission and reflection (STAR) enabled reconfigurable intelligent surfaces (RIS) to assist the communication and sensing between a base station (BS) to a single set of UL and DL user, and target over the same-time frequency dimension. In order to explore the performance of the proposed framework, we offer an analytical framework and accordingly, we propose an optimization problem to jointly optimize the phase-shift matrices at the STAR RIS (S-RIS) that maximizes the possible sum-rate. Due to the non-convexity of the optimization problem, we then propose a low-complexity meta-reinforcement learning (MRL) algorithm that reduces the overall training overhead. We also demonstrate the effectiveness of the proposed algorithm in providing near-optimal design in the case of imperfect channel state information (ICSI). Additionally, in order to verify how well the proposed framework work and to show the superiority of the proposed algorithm, we provide a fair comparison with two baseline schemes a) twin delayed deep deterministic policy gradient (TD3) and b) deep deterministic policy gradient (DDPG). Simulation results verify that the proposed approach results in superior performance.

In this paper, we consider Simultaneous Transmission and Reflection (STAR) Reconfigurable Intelligent Surface (S-RIS) and passive RIS (P-RIS) assisted integrated sensing and communication system (ISAC), where S-RIS is enabled to broadcast communication signal, and P-RIS assists sensing functionalities. In particular, we jointly optimize the beamforming vector at the multi-antenna ISAC transmitter, and phase shift vector to maximize the weighted sum-rate (WSR) at the communication users while taking care of the maximum power limit at ISAC transmitter while ensuring the performance of sensing model to detect targets in its vicinity and limitations of phase and amplitude of S-RIS elements. To address the non-convexity of the above problem, we propose a low-complexity alternating optimization (AO) algorithm. Furthermore, we provide a comprehensive simulation-based graphical results to verify the viability of the proposed framework with its P-RIS assisted counterpart. Eventually, exhaustive simulation results are demonstrated to present the impact of RIS elements and the number of antennas at the ISAC transmitter. Accordingly, we illustrate the impact of S-RIS and the number of targets to highlight the trade-off between sensing and communication.

In this study, we proposed a hierarchical multi-beam search algorithm along with a hierarchical codebook to efficiently estimate the angle-of-departures (AoDs) and angle-of-arrivals (AoAs) and design the precoder and decoder in millimeter-wave massive MIMO systems with a uniform linear array. Existing hierarchical codebooks were designated for the case with antenna numbers being a power of two. We propose a more flexible codeword-generation algorithm applicable to the case with antenna number equals to any composite numbers. Moreover, we proposed a least-square based refinement to raise the accuracy of the estimated AoDs and AoAs with lower training overhead. It shows through the simulation results that the proposed algorithm achieves a comparable unsuccess rate compared with an existing method, yet demands lower training cost and complexity. It also shows that the gap in spectral efficiency with the proposed multi-beam search algorithm and perfect CSI case is insignificant.

In this work, we investigate the performance of a federated edge learning (FEEL)-enhanced edge computing in unmanned aerial vehicles (UAV)-aided internet of things (IoT) system under the consideration of limited energy at each UAV. It consists of multiple UAVs which apply FEEL for local training and, then, transmit the required parameters to a centralized IoT-server. We use a new cost metric obtained by a linear combination of latency and energy consumption, and formulate an optimization problem to jointly optimize the central processing unit (CPU)-frequency during FEEL and allotted bandwidth under the consideration of the limited overall system bandwidth and energy available at each UAV. Due to the non-convex nature of the formulated problem, we propose a twin delayed deep deterministic policy gradient (TD3)-based algorithm that solves the problem and provides the optimum CPU frequency and allotted bandwidth to each user. We validate the accuracy and convergence of the proposed algorithm via exhaustive simulations and highlight its effectiveness by comparing its performance with that of deep deterministic policy gradient (DDPG) and deep Qnetwork (DQN)-based solutions.

This study considers a two-way amplify-and-forward relay system wherein two users exchange information with the assistance of an energy-harvesting MIMO relay node. With two-time-slot transmission, the relay receives the transmitted signal from both users in the first time-slot and splits it into two parts for information forwarding and energy harvesting, respectively, in accordance with a particular power splitting ratios. The phases of the RF signals used for energy harvesting at each relay antenna are shifted before being combined to achieve the highest power. Meanwhile, the RF signals used for information forwarding are passed through a linear precoder and then broadcast to the users in the second time-slot. We aim to optimize the power splitting ratios, precoding matrix, and RF phase shifting factors such that the sum-rate is maximized. Since joint optimization is intractable, the parameters are partitioned into three sub-optimization problems. For each parameter, a sub-optimal solution is derived in a (semi-)closed form. It shows numerically that the proposed sub-optimal solutions for the optimal phase shift and power split ratio are close to the global optimal solutions. Moreover, the proposed solution for the precoding matrix outperforms the so-called ’General Power Iterative’ algorithm proposed in the literature.

In this article, we investigate the nonlinear energy harvesting (EH)-based unmanned aerial vehicle (UAV)-assisted full-duplex (FD) Internet of Things (IoT) network with infinite and finite blocklength (FBL) codes. The reliability performance of the considered network, having two half-duplex UAVs and an FD IoT device, is analyzed in terms of the block error rate (BLER) with given ultrareliable and low-latency communication constraints. With the assumption of the combined effect of fading and shadowing, the closed-form expressions for BLER and network goodput are obtained over the Rician shadowed fading channels considering various shadowing scenarios, EH receiver architecture, IoT device mobility, inter-UAV interference, and self-interference (SI) cancelation capabilities at FD IoT device. The obtained results over the Rician shadowed fading for the nonlinear EH receiver architecture are also compared with the linear EH and over the Rician fading channels. The numerical results reveal important observations related to the impact of time-selective fading channels with imperfect channel state information, shadowing severity in the suburban areas, SI cancelation capabilities, blocklength, and the number of channel uses on the reliability performance of the UAV-assisted FD IoT network. Furthermore, the tightness of the approximation presented is verified through the Monte-Carlo simulations.

In this work, we consider full duplex multi-input-multi-output (MIMO) cooperative systems with amplify-and-forward relaying protocol. In order to boost spectrum efficiency, the source node transmits multiple data streams at the same time, and the relay node receives and forwards these data stream simultaneously. By exploiting the channel state information, we jointly optimize the precoders at the source node and the relay node in terms of the end-to-end achievable rate, subject to fully suppress the self interference at the relay node. However, the optimization is not concave and intractable. Luckily, we found that the optimal solution lies on the boundary of the feasible set, and can be found numerically with gradient search over the boundary. Finally, through computer simulation, it shows that more multiplexing gain can be obtained with increasing number of data streams and the proposed scheme attains achievable rate 1.5 times higher than the half duplex scheme.

In this work, we propose a recursive semiblind channel estimation algorithm to mitigate the ill effects of pilot contamination in massive multi- user multiple-input multiple output (MU-MIMO) systems. In the proposed approach, we first project the received signals onto the subspace with minimal interference, where a low-complexity modified power method is employed to recursively determine the bases of this subspace. Specifically, a few pilot symbols are applied to generate initial channel estimate of the projected channel coefficients. Finally, a recursive algorithm is introduced to alternatively detect the data symbols and update the channel estimates. Notably, the proposed scheme requires neither cell cooperation nor pilot scheduling. Simulation results demonstrate the performance superiority compared with the existing works.

Massive multi-user multiple-input multiple-output (MU-MIMO) systems are a promising solution for achieving high throughput and robust transmission in next generation mobile communications. Achieving the optimal transceiver design in such systems requires an accurate knowledge of the channel state information. However, in massive MU-MIMO systems, the quality of the channel estimates is often degraded by pilot contamination. In this paper, we propose a low-complexity semiblind channel estimation algorithm to mitigate the ill effects of pilot contamination. In the proposed approach, the received signals are first projected onto the subspace with minimal interference, where the bases of this subspace are determined recursively via a low-complexity modified power method. An initial estimate of the projected channel coefficients is then made based on a small number of pilot symbols. Finally, data symbols are detected and the channel estimation is refined alternatively. Compared with existing channel estimation methods, the proposed algorithm has lower complexity due to the subspace projection and innovation process. An asymptotic analysis reveals that the mean square error of the channel estimates is inversely proportional to the length of the data symbols. Simulation results demonstrate that the proposed algorithm outperforms the existing works and alleviates the pilot contamination effects effectively.

This work presents a novel peak-to-average power ratio (PAPR) reduction scheme, namely extended selected mapping (eSLM), in space-frequency block coding (SFBC) MIMO-OFDM systems. By introducing phase rotations and amplitude extensions into so-called extension matrices, the candidate signals with lowest PAPR can be selected and the receiver is able to identify index of the selected candidate without transmission of side information. Thus, the proposed eSLM has higher spectrum efficiency compared to conventional SLM-based methods which demand side information. Furthermore, the extended matrices preserve orthogonality of the space-frequency encoded blocks, which allows low-complexity decoding at the receiver. It shows through simulations that the proposed eSLM outperforms existing blind SLM-based method, and has slight performance gap with costly oSLM scheme.

We propose a semi-blind channel estimation for two-way relay networks (TWRNs) where multiple relays employ amplify-and-forward (AF) protocol and the channel is frequency-selectively faded. The challenges of the channel estimation are resulted from inter-symbol interference (ISI), self-interference in two-way systems and difficulty to estimate each relay link separately. To deal with ISI and to obtain the effective channel impulse response (CIR) of each relay link individually, cyclic prefix is inserted in source messages and we propose a low-complexity and scalable design of precoding matrices at relays. Under the framework, effective CIR of each relay link is estimated through the second-order statistics of the received signals, while ambiguity resulted from the channel matrices of direct links between users are resolved by a small number of training blocks. It shows through simulations that NMSE of the proposed scheme exists error floor at high SNR, which can be mitigated by averaging over more blocks of received signals especially when the channel varies slowly.

Consider a two-relay decode-and-forward (DF) cooperative network where Alamouti coding is adopted among relays to exploit spatial diversity. However, the spatial diversity gain is diminished with the existence of misbehaving relays. Most existing work on detecting malicious relays requires the knowledge of instantaneous channel status, which is usually unavailable if the relays garble retransmitted signals deliberately. With this regard, we propose a noncoherent misbehavior detection using the second-order statistics of channel estimates for relay-destination links. It shows from simulation results that increasing the number of received blocks provides significant improvement even at low SNR regime.

In this paper, we propose a concept of parasitic communication system via relaying. The proposed system is suitable for improving the throughput of some users who are far from the base station or have numerous obstructions in their communication links. In the parasitic communication system, the users communicate to the base station by following the original setting of the standards. Only a slight modification at the retransmission mode is required. Therefore, the proposed approach is backward compatible to several existing systems.

In this work, we consider a canonical three-node DF cooperative network where the relay may misbehave with a certain probability. Without misbehavior detection, diversity combining at the destination may degrade system performance severely if the relay misbehaves. Most existing work of misbehavior detections requires perfect channel state information (CSI), which may not be achievable since the retransmitted symbols are garbled by the malicious relays. With this regard, we propose a two-tier approach to detect misbehaving relay in quasi-static relay channel. By measuring the average received energy and observing the distribution of the phase rotations of the tracing symbols, misbehavior of the relay can be determined without CSI. As shown in simulations, error performance of the proposed scheme is close to the case with perfect misbehavior detection.

We examine a cooperative uplink CDMA network where multiple sources simultaneously access the cooperative channel using different spreading codes and compete for the resources at the relays. To efficiently utilize the limited energy and bandwidth resources at the relays, we propose in this work a decentralized reduced-rank multiuser relaying (RR-MUR) where the data received at each relay is compressed into a limited number of dimensions and forwarded with optimal power allocation among the different dimensions. The proposed scheme follows upon our previous work where a centralized strategy has been proposed. Specifically, we propose the minimum mean square error principle component analysis (MMSE-PCA) approach that can be used to design the relay precoders in a decentralized manner. We show that the MMSE-PCA scheme is the optimal relay precoder design when only one relay exists in the network. Through numerical simulations, we show that the proposed scheme outperforms the Q-selection scheme, where only a selected group of sources are served by each relay.

Cooperative relaying has been studied extensively in the literature to exploit spatial diversity gains by having each source transmit its messages through multiple independently fading relay paths. In multiuser systems where multiple sources may access the same set of relays simultaneously, CDMA spreading techniques along with multiuser detection schemes have been proposed in the literature to eliminate multiple access interference (MAI). In order for each relay to forward messages from all sources, a tremendous increase in dimensions (or spreading gain) is used to accommodate the relay transmissions. To reduce the required bandwidth or dimensions, we propose a reduced-rank multiuser relaying (RR-MUR) scheme where the data received from multiple users are first compressed into lower dimensions before being retransmitted. More specifically, linear compression precoders at the relays and decoder at the destination are found by imposing a recursive joint optimization procedure with the objective of minimizing the mean square error (MMSE) of the estimate at the destination. We show through numerical simulations that the RR-MUR scheme outperforms the often adopted Q-selection scheme in terms of increased spectral efficiency.

In this paper, we examine the uplink of a cooperative CDMA network, where users cooperate by relaying each other's messages to the base station. When spreading waveforms are not orthogonal, multiple access interference (MAI) exists at the relays and the destination, causing cooperative diversity gains to diminish. To address this issue, we adopt the multiuser detection (MUD) technique to mitigate MAI in achieving the full advantages of cooperation. Specifically, the relay-assisted decorrelating multiuser detector (RAD-MUD) is proposed to separate interfering signals at the destination with the help of preceding at the relays along with pre-whitening at the destination. Unlike the conventional zero-forcing (ZF) precoder or the decorrelating MUD, the proposed RAD-MUD experiences neither power expansion at the relays nor noise amplification at the destination. Three cooperative transmission strategies are considered on top of RAD-MUD; namely, transmit beamforming, selective relaying and distributed space-time coding. Since the reliability of each source-relay and/or relay-destination links are different, relay transmissions are weighted accordingly in our schemes to further combat MAI. The advantages of RAD-MUD over ZF precoding and other existing cooperative MUD schemes are shown through computer simulations.

The use of multi-user detection (MUD) in a cooperative CDMA network is investigated for the uplink in synchronous CDMA systems. Suppose that, at any instant in time, part of the users serve as sources while the others serve as relays. The proposed MUD scheme decorrelates the sources' messages at the destination with the help of precoding at the relays. Three cooperation methods are considered: (1) transmit beamforming, (2) selective relaying and (3) distributed space- time coding. The optimal weighting factors of each method are determined by taking the quality of the source-to-relay and/or the relay-to-destination links into account. We show that significant improvements in terms of the spatial diversity and multiple-access interference (MAI) mitigation can be attained when precoding is employed at the relays to aid the decorrelation at the destination. The advantages are even more pronounced when selective relaying is combined with the other two schemes.

Three power control schemes for the space-time coded amplify-and-forward (AF) relaying scheme targeting at wireless sensor network applications are examined and compared. The opportunistic scheme performs the best by considering the signal-to-noise-ratio (SNR) of the received signal. However, if the power for the relay is limited, the performance of the opportunistic scheme degrades due to the loss of active relay nodes that have better channel conditions. Since the battery lifetime of nodes for wireless sensor networks is limited and the loss of relay nodes is critical to system performance, we propose an SNR-constrained power reduction scheme to prolong the relay lifetime for the opportunistic scheme. It is demonstrated by computer simulation that the opportunistic scheme with SNR-constrained power reduction is power efficient and the relay lifetime of dense relay networks can be significantly prolonged.

Discrete power allocation strategies for amplify- and-forward cooperative networks are proposed based on selective relaying methods. The goal of power allocation is to maximize the network lifetime, which is defined as the duration of time for which the outage probability at the destination can be maintained above a certain level. The discrete power levels enable a low cost implementation and a close integration with high speed digital circuits. We propose three power allocation strategies that take into consideration both the channel state information (CSI) and the residual energy information (REI) at each node. By modeling the residual energy of each node as the states of a Markov Chain, we are able to derive the network lifetime analytically by computing the expected number of transitions to the absorbing states, i.e., the energy states for which the outage probability is no longer achievable. The performance of the three strategies are compared through numerical simulations and a significant improvement in network lifetime is shown, when compared with the case considering only the local CSI.

Power allocation strategies are devised to maximize the network lifetime of amplify-and-forward (AF) cooperative networks. The paper considers the scenario where one source and multiple partners cooperate to transmit messages to the destination. The powers emitted by the users are subject to the SNR requirement at the destination. First, the power allocation strategy that demands the minimum instantaneous aggregate transmit power of all cooperating partners is described and analyzed. The optimal solution results in a form of selective relaying; namely, the user with the best channel condition is selected to help in relaying the message. However, this instantaneous power minimization strategy does not necessarily maximize the lifetime of battery-limited systems. Then, three AF cooperative schemes were proposed to exploit the channel state information (CSI), the residual battery energy and the QoS requirement. It is shown that the network lifetime can be extended considerably by taking all these three factors into account.