Traffic status monitoring is essential to support intelligent transportation, and demands for highly efficient and secure processing and transmission of massive data. Inspired by recent information-theoretic study, the traffic status monitoring is formulated as a privacy-preserving semantic coding problem in this work to characterize the fundamental trade-off among data compression, traffic image reconstruction, segmentation of traffic participants, and preservation of pixel regions of traffic participants. Grounded on the theoretic results, a novel generative semantic coding scheme is further developed for data-driven semantic coding design with the aid of attention mechanisms to capture correlations between sparse information. Experiments on the RCooper dataset demonstrate the effectiveness of the proposed generative semantic coding scheme and its advantages over the benchmark compression methods.

Vehicle-to-everything (V2X) communication plays an essential role in the Internet-of-Vehicles (IoV) systems. However, in practice the diverse decision objectives of different users and the highly dynamic nature of IoV cause great challenges in realizing efficient data transmission. This paper investigates a sequential power control problem in a typical V2X communication network formed by multiple data delivery links, and proposes a novel decision framework from the perspective of dynamic multi-objective optimization (DMO). The framework is developed based on the dynamic multi-objective optimization evolutionary algorithm (DMOEA). When the variation of channel fading state is detected, an innovative environment change response mechanism is implemented to predict the centroid change of the Pareto optimal set (PS) in the new environment. This allows to properly generate the initial population for the subsequent evolutionary search process, and rapidly track the time-varying Pareto optimal front (PF). The effectiveness and advantages of the proposed framework over conventional solutions are validated by simulation results.

The advent of 6G networks necessitates fundamental advancements in Radio Access Network (RAN) slicing to accommodate unprecedented service demands with highly dynamic traffic patterns. Existing AI-driven approaches for RAN slicing face critical limitations: they typically require model retraining when slice configurations change, lack scalability for concurrent slice management, or overlook the significant computational overhead from frequent resource reconfigurations. This paper addresses these significant challenges by introducing a novel RAN Partially Observable Stochastic Game (RAN-POSG) framework that enables parallel decision-making while effectively incorporating global resource constraints. Firstly, we propose an Adaptive Retention Framework (ARF) that strategically balances stability and adaptability through an innovative Conditional Accumulative Reward mechanism, significantly reducing reconfiguration overhead while maintaining high Service Level Agreement (SLA) compliance. Our proposed Statistic-Field Deep Truncated Monte Carlo (SF-DTMC) algorithm leverages multi-agent reinforcement learning where agents make independent yet coordinated decisions, ensuring both flexibility with varying slice counts and scalability under large-scale networks. Lastly, a custom-built high-fidelity RAN slicing simulator based on NS-3 simulator with PettingZoo interfaces is developed for comprehensive evaluations across multiple test scenarios. The simulation results demonstrate the significant advantages of the proposed framework, including superior user satisfaction, resource utilization, and lower resource reconfiguration overhead compared to state-of-the-art methods, representing a crucial advancement toward Native-AI in 6G networks.

Resource allocation in vehicle-to-everything (V2X) communication is an important yet challenging problem. Most existing works target optimizing a specific performance metric in a relatively stable condition, which may not be able to adaptively satisfy the dynamic quality of service (QoS) requirements prevalent in practical vehicular networks. In this paper, we investigate a typical V2X resource allocation problem requiring optimization of both vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication performance. Instead of setting one single system goal, we formulate it as a multi-objective optimization problem and aim to seek the complete trade-off between the objectives, i.e., the Pareto frontier (PF). To this end, the problem is first transformed to a multi-objective Markov decision process (MOMDP) and solved by a multi-objective reinforcement learning resource allocation (MORLRA) algorithm. Simulation results demonstrate the effectiveness and advantages of the proposed method.

Vehicle-Infrastructure cooperative perception (VICP) serves as a promising solution to enhance the environmental sensing capability of autonomous driving systems. However, most existing VICP algorithms assume ideal data exchange between the road-side and on-board sensors. In practice, channel distortion and limited bandwidth in vehicle-to-infrastructure (V2I) communication would cause significant performance degradation. To address this issue, we propose VICP-SC, a solution that integrates VICP with semantic communication (SC) for the LiDAR-based 3D object detection function. Specifically, a task-driven joint source-channel coding (JSCC) module and a multi-scale feature fusion (FF) module are developed to efficiently transmit and effectively fuse the infrastructure’s LiDAR point clouds with those attained by vehicles. Extensive experiments on the real-world DAIR-V2X dataset demonstrate that the proposed VICP-SC method achieves excellent performance in terms of perception accuracy, bandwidth efficiency, and system robustness against channel variations compared with existing baselines, particularly in the low signal-to-noise (SNR) regimes.

With the sixth-generation mobile communication technology (6G) experiencing a shift of spatial expansion, the integration of terrestrial architecture and satellites has become one core feature of 6G. While the scarcity of spectrum resources has become a significant challenge hindering the advancement of integrated satellite-terrestrial networks, it is crucial to push the development of effective strategies to optimize spectrum utilization. This letter introduces a flexible spectrum sensing technique in the nonorthogonal multiple access (NOMA) uplink system for low-earth orbit (LEO) satellite-terrestrial communications, striking an optimal balance between under-sensing and over-sensing. Confronted with diverse complicated scenarios of multisatellite coverage, we conceive the operational principles and derive the sensing thresholds, exploiting spectrum holes and mitigating the fluctuation of false alarms. Notably, numerical outcomes showcase that the proposed technique outperforms the benchmarks, achieving a maximum throughput gain of 61.5% in dynamic communication environments.

This paper presents a novel, high-performance multi-vehicle detection and tracking (MVDT) framework to extract vehicle trajectories from roadside 3D point clouds. First, we developed a vehicle detector called the deep evidential occupancy grid model. This model uses PointNet to extract features from vertically organized point cloud pillars, generating a bird’s-eye view (BEV) grid feature map, followed by a 2D convolutional neural network to capture both local and global spatial features. The detection head uses a Dirichlet distribution to gather “evidence” from gathered features that indicate grid occupancy or vacancy, quantifying the model’s “confidence” through a probabilistic representation of uncertainty. An adaptive post-processing method is applied to output detection results with associated uncertainty. Second, we integrated uncertainty into the tracker, developing an uncertainty-aware multi-vehicle tracking (UMVT) model. This model manages the trajectory initialization and termination strategies, enhancing tracking robustness in complex scenarios. Comprehensive experiments on two real-world datasets, DAIR-V2X and V2X-Real, demonstrate that the proposed MVDT framework outperforms state-of-the-art methods in roadside perception, achieving improvements in both detection and tracking tasks. The effectiveness has also been validated using point cloud data from the Shanghai-Nanjing Expressway, showcasing its excellent practical applicability.

Full-sample vehicle trajectories collected by roadside sensors are pivotal for advancements in active traffic management and vehicle-highway automation applications. However, sensor inefficiencies and environmental factors often lead to incomplete and noisy data, impeding practical applications. To overcome this hurdle, we propose a data-driven and kinematics-aware method for reconstructing full-sample vehicle trajectories. It employs an encoder-decoder architecture, where the data-driven encoder utilizes graph-based map modeling to capture road network characteristics and models multi-vehicle interactions based on real-world driving attention patterns. The model-driven decoder integrates conditional variational autoencoder with a vehicle kinematic model, to ensure that the generated trajectories adhere closely to vehicle kinematic principles. Experiments on large-scale trajectory datasets demonstrate that our approach exhibits good performance in trajectory reconstruction. Furthermore, case studies on the DAIR-V2X dataset indicate that our approach could improve raw trajectory data quality and downstream prediction accuracy.

Parking scenarios present huge challenges for the prediction of intentions and situational awareness of autonomous vehicles. Unlike urban roads, parking scenarios feature greater freedom of movement, denser interactions, and more diverse intents. To address this issue, we propose the Parking Memory Interaction Transformer (PMI-Transformer) for predicting vehicle intent and trajectory based on cooperative vehicle-infrastructure systems (CVIS). Our model utilizes long-term semantic observation data and short-term historical trajectories to predict a target vehicle’s future intentions and trajectories from roadside perception units in CVIS. Based on global spatiotemporal features, a memory encoding strategy extracts essential information from the vehicle’s long-term history. At the same time, a hypergraph-based multiscale interaction module captures dense, diverse interactions in multi-vehicle environments. We incorporate parking occupancy data and calculate affinities between the vehicle and nearby spots to better characterize vehicle intent within the parking context. Experimental results show that our model outperforms state-of-the-art methods in intent and trajectory prediction, with visualizations confirming that PMI-Transformer effectively captures key historical information and social interactions in parking scenarios.

LiDAR semantic segmentation plays an important role in 3D scene understanding for autonomous driving. However, the performance based on the LiDAR equipped on a vehicle may be limited due to small perception perspective, object occlusion, and sparsity of point clouds in the distance. To address these challenges, we propose a vehicle-infrastructure cooperative semantic segmentation (VICSS) framework to enhance the vehicle-side perception capability. An infrastructure feature extraction (IFE) module is employed to extract features from the roadside LiDAR point cloud. A local feature extraction (LFE) module and a global feature extraction (GFE) module are applied to the vehicle point cloud to capture the local and global features, respectively. The features from both point clouds are fused through a feature aggregation (FA) module, which applies the cross-attention mechanism to learn beneficial information from the infrastructure features. Using a dataset generated by CARLA we show that the proposed VICSS framework achieves good performance in terms of semantic segmentation accuracy.

Reinforcement learning (RL) has been widely employed in communications, in the areas of interference management, resource allocation, signal detection, and power control, etc. Nevertheless, RL is vulnerable under various malicious attacks, such as adversarial examples and privacy intrusions. In this paper, a falsification attack on the agent actions in a scalar linear quadratic regulator (LQR) system is studied. This adversarial problem is formulated as a novel dynamic game by introducing an adversarial belief, and subgame perfect equilibria (SPEs) are characterized under different adversarial constraints. Numerical experiments show the impact of strategic interactions and justify the theoretic results.

Deep reinforcement learning (DRL) can be used to solve decision-making problems in changing and complex environments. It is widely envisioned to have great potential for enabling autonomous driving. However, the large and continuous state and action spaces of autonomous driving tasks in general lead to low exploration efficiency and significantly affect the model training speed. In this paper, we propose a novel autonomous driving framework based on DRL with an intervention module. The module utilizes historical information to predict the status of future states so that evaluation of potential actions can be inferred. By this means, an intrinsic reward is generated to prevent DRL agent from entering poor states with useless exploration. Integrating our DRL model with a perception module which utilizes supervised multi-task learning to extract useful features from raw sensor data, the proposed framework can notably improve the efficiency of DRL training. A case study on the open racing car simulator (TORCS), with inputs from both distance sensors and vision sensors, is carried out to demonstrate the effectiveness of our method.

This paper investigates efficient data delivery methods, through integrating non-orthogonal transmission with network coding techniques, in wireless networks with multiple sources, multiple relays, and multiple destinations. We propose properly scheduling terminals into transmitting clusters to optimize system error performance. Considering communication over general Nakagami-m fading channels, an error probability approximation analysis is first presented. To reduce computation complexity for solving the node scheduling design problem, we further propose transforming the problem into a data classification problem and applying data-driven machine learning tools to reach rapid decision-making. Taking two-user clustering as an example, simulation results show that our method can attain similar performance as the optimal solution, and significantly outperform conventional methods that orthogonalize or randomly schedule terminals. The proposed method and the analytical framework can be extended to larger networks with diverse node clusters and more complex signal propagation environments.

Intelligent connected vehicle (ICV) has been widely deemed as the key to reduce road accident rate and improve traffic efficiency. However, ensuring high communication reliability and low transmission delay in vehicular networks is challenging, especially in large-scale dynamic networks with diverse heterogeneous data exchange demands. In this paper, we investigate the potential of applying the deep reinforcement learning (DRL) technique to facilitate efficient transmission design in a class of complex multi-user vehicle-to-vehicle (V2V) networks, where conventional mathematical tools confront difficulties in solving the design optimization problems. The considered network contains several pairs of V2V links sharing the channel resource. Each link desires to communicate two types of delay-sensitive messages to support different safety-related applications with the maximum energy efficiency. We propose transforming the power/rate control problem into a Markov decision process and then solving it using the deep deterministic policy gradient (DDPG) algorithm. Simulation results show that in a two-user network our DRL-aided solution can achieve better performance than that with Lyapunov optimization. Extending the former to work in a larger network is straightforward, but it is not the case for the latter. The advantages of applying DRL to support wireless system design are thus demonstrated.

Vehicular edge computing (VEC) is a new computing paradigm that has great potential to enhance the capability of vehicle terminals (VTs) to support resource-hungry in-vehicle applications with low latency and high energy efficiency. In this article, we investigate an important computation offloading scheduling problem in a typical VEC scenario, where a VT traveling along an expressway intends to schedule its tasks waiting in the queue to minimize the long-term cost in terms of a tradeoff between task latency and energy consumption. Due to diverse task characteristics, dynamic wireless environment, and frequent handover events caused by vehicle movements, an optimal solution should take into account both where to schedule (i.e., local computation or offloading) and when to schedule (i.e., the order and time for execution) each task. To solve such a complicated stochastic optimization problem, we model it by a carefully designed Markov decision process (MDP) and resort to deep reinforcement learning (DRL) to deal with the enormous state space. Our DRL implementation is designed based on the state-of-the-art proximal policy optimization (PPO) algorithm. A parameter-shared network architecture combined with a convolutional neural network (CNN) is utilized to approximate both policy and value function, which can effectively extract representative features. A series of adjustments to the state and reward representations are taken to further improve the training efficiency. Extensive simulation experiments and comprehensive comparisons with six known baseline algorithms and their heuristic combinations clearly demonstrate the advantages of the proposed DRL-based offloading scheduling method.

This letter investigates the performance of energy detector with multiple antennas in cognitive vehicular networks, where the sensing signals of the secondary user are temporally correlated. A novel analytical method, based on new results of confidence interval estimation for auto-correlated matrix in random matrix theory, is proposed to estimate the miss detection probability. The estimate accuracy is verified via simulations. Based on such results, the impacts of signal temporal correlation on sensing performance and decision threshold are analyzed.

This paper investigates efficient transmission design in a class of multi-user cooperation networks, in which multiple information sources intend to distribute their messages to a sufficient proportion of ambient destinations with the assistance of multiple relays. We apply a relaying scheme that combines non-orthogonal transmission with network coding techniques to balance channel consumption and inter-user interference. The sources and relays are divided into clusters, terminals within each of which are allowed to non-orthogonally access the same channel. A class of finite-field network codes are adopted in the relays. We provide the methods to derive the system transmission error probability and diversity-multiplexing tradeoff (DMT), over Nakagami-m fading channels. Through error probability and finite-SNR DMT analysis for certain clustering strategies, and infinite-SNR DMT analysis for general situations, we show that the considered relaying scheme can notably improve system performance over the conventional approach that demands only orthogonal transmission in network-coded cooperation networks.

Vehicular networking is potentially an effective solution to the problems in today's transportation system. However, realizing efficient communications among vehicles with satisfactory reliability and latency requirements is challenging, especially when diverse applications are taken into consideration. In this paper, we investigate a cross-layer energy- efficient transmission design for a class of vehicular communication networks, in which two pairs of vehicle-to-vehicle links non-orthogonally share the available spectrum. Each link desires to deliver two types of messages that can support different delay-limited applications. The periodically- generated heartbeat messages should be transmitted subject to a reliability requirement, and the randomly-appeared sensing messages should be delivered with finite latency. We propose a power control strategy to achieve high energy efficiency, while ensuring the expected quality-of-service requirements, based on both channel state information in the physical layer and queue state information in the media access control layer. Simulation results show that our proposed method notably outperforms conventional methods.

Recently, research on mobile molecular communication (MC) has become a trend and hotspot. The timevarying channel impulse response (IR) resulting from the mobile feature makes signal detection schemes for static MC no longer applicable. In this paper, a low complexity and non-coherent detection scheme based on the energy difference between two adjacent symbols is proposed for blind signal detection in mobile scenario providing high data rates. Specifically, we consider the non-coherent signal detection scheme with respect to both the moving transmitter and the moving receiver for the first time. In contrast to existing methods for low data rates and without considering inter-symbol interference (ISI), the proposed method utilizes the ISI to achieve quality detection results at high data rates without knowing the channel state information (CSI). The influences of the mobility parameters and data rates on the bit error rate (BER) are evaluated by simulations. The simulation results demonstrate that the BER performance of the proposed scheme is much lower at the same signal intensity compared with a non-coherent scheme, and the computational complexity can be significantly reduced as well. Consequently, the proposed scheme can reduce the effect of the mobility of the nanomachines in signal detection and has the potential to implement molecular communications, especially in mobile scenarios.

This paper investigates the communication problem in a class of multi-user dual-hop networks in which multiple source terminals desire to distribute their independent messages to multiple destinations through the assistance of multiple relay terminals. We consider an efficient transmission strategy that combines network coding and non-orthogonal transmission techniques to balance the achievability of spatial diversity and channel utilization. Specifically, in addition to applying a class of finite-field network codes in the relays, we divide the sources and the relays into clusters such that terminals within each cluster can access the same channel resource. The achievable error performance under Nakagami-m fading environment is derived and is shown to significantly outperform the conventional transmission methods.

This paper studies the resource allocation (RA) problem when the in- band device-to-device (D2D) technology is applied to support vehicle-to-vehicle (V2V) communications. Conventional D2D RA normally demands a sufficient level of channel knowledge to reach the optimal performance. But in vehicular communication environments, this would require tremendous signalling overhead and the acquired channel knowledge is easily outdated. Therefore, RA relying only on geographic information is more feasible. To this end, we propose a novel location-partition-based RA scheme. We first divide the cell coverage area and road into small zones, the geographic information of which is stored in a database. Satisfying the requirement that the interference generated by all V2V links to the reused cellular user (CU) is below a certain threshold, an interference matrix that reflects the interference from nodes in cell zones to road zones is established. Three types of power control methods are adopted to maximize the minimum achievable rate of the V2V links. A series of simulations are conducted to verify the performance of our proposed RA solution. The results show that our method can improve the minimum achievable rate compared with conventional location-based RA methods. The impact of different system parameters are also carefully analyzed.

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this record

We investigate the achievable sum degrees of freedom (DoF) in a class of single-antenna multi-user multi-hop relay networks. The networks consist of multiple information sources and destinations, without direct signal propagation link between them, so that multiple layers of relays are deployed to assist in information delivery. We consider the situation that relays are unable to shield their receptions from the harmful self-interference and from the interference generated by other relays. Hence, ideal full-duplex relaying is not applicable. Utilizing half-duplex decode-and-forward relays, a cluster successive relaying (CSR) transmission scheme is adopted to conduct message transmission. The CSR scheme divides each layer of relays into two successively activated relay clusters to compensate the extra channel consumption demanded by the half-duplex operation. We propose two interference alignment strategies to deal with the interference issues. By properly clustering the relays in each layer, we find the asymptotically achievable sum DoF, subject to time-varying and frequency-selective fading, respectively. These results can lead to new lower bounds for the available DoF in the considered class of multi-user multi-hop networks.

We investigate the information transmission in a typical communication scenario in the intelligent transportation systems (ITS), that is, infrastructures providing ITS services that intend to broadcast independent messages to the vehicles within their coverage areas. Due to the dynamic nature of wireless signal propagation links and interuser interference, it is hard to guarantee satisfactory performance by direct infrastructure to vehicle (I2V) transmissions. To solve this problem, we propose allowing certain vehicles to serve as relays to assist in the information distribution process and applying a class of finite-field network codes to efficiently use the available spectrum resources. Considering that the infrastructure information sources and all vehicles are randomly distributed following Poisson point processes, we model a general urban ITS communication network and based on the stochastic geometry we derive the probability that a vehicle can successfully recover all the desired messages from its serving infrastructure. The analytical and numerical results clearly demonstrate that our proposed network coding based relay-assisted I2V transmission can significantly improve the communication performance of the conventional direct I2V transmission strategy.

The significant increase in energy consumption and the rapid development of renewable energy, such as solar power and wind power, have brought huge challenges to energy security and the environment, which, in the meantime, stimulate the development of energy networks toward a more intelligent direction. Smart meters are the most fundamental components in the intelligent energy networks (IENs). In addition to measuring energy flows, smart energy meters can exchange the information on energy consumption and the status of energy networks between utility companies and consumers. Furthermore, smart energy meters can also be used to monitor and control home appliances and other devices according to the individual consumer's instruction. This paper systematically reviews the development and deployment of smart energy meters, including smart electricity meters, smart heat meters, and smart gas meters. By examining various functions and applications of smart energy meters, as well as associated benefits and costs, this paper provides insights and guidelines regarding the future development of smart meters.

This paper studies how to efficiently reduce the mutual interference between primary and secondary networks. Different to conventional cognitive system, in which primary and secondary networks have no information exchange, the media access control mechanism we propose allow an operating PU to share the length of its remaining occupation time to SUs. With the help of such information exchange, the primary network can be better protected and the secondary network has the potential to reduce the energy consumed by spectrum sensing. To realize such a dynamic information sharing, we propose a novel spectrum-sculpting-aided scheme in this paper. Specifically, the PU deliberately inserts one zero-subcarrier into its data carriers, and uses the zero-subcarrier's position to claim its status. Through two-step sensing, each SU can attain the PU's remaining operation time, and then adjust its sensing strategy to avoid interference to the PU. Analysis of interference probability, together with simulation results, clearly exhibit the advantages of the proposed scheme in terms of the mutual interference and the amount of SU's sensing times.

This paper applies device-to-device technique to vehicular-to-vehicular communications (D2D-V) in an underlay fashion. Different from conventional D2D systems focusing on smart phones, whose positions are usually assumed to be statical, the D2D-V system suffers more mobility and uncertainties. These dynamic features indeed complicate the design of D2D-V system, but also, give more potential to accomplish some geographic based algorithms. Armed with the geographic knowledge of highway and the dynamic GPS information in each vehicle, we propose a geographic based reuse cellular user selection scheme and two different distributed power control schemes to serve various applications in vehicular networks. We model the problem firstly, then formulate two metrics: the sum rate and the minimum-achievable rate to evaluate the performance of the proposed schemes. Simulations are proposed to validate our schemes and analysis. Influences of different system settings are also discussed here.

This paper investigates transmission schemes for fixed-rate communications over a Rayleigh block-fading interference channel. There are two source-destination pairs where each source, in the presence of a short-term power constraint, intends to communicate with its dedicated destination at a fixed data rate. It encodes its messages using a point-to-point Gaussian codebook. The two users' transmissions can be conducted orthogonally or non-orthogonally. In the latter case, each destination performs either direct decoding by treating the interference as noise, or successive interference cancellation (SIC) to recover its desired message. For each scheme, we seek solutions of a power control problem to efficiently assign power to the sources such that the codewords can be successfully decoded at destinations. However, because of the random nature of fading, the power control problem for some channel realizations may not have any feasible solution and the transmission will be in outage. Thus, for each transmission scheme, we first compute a lower bound and an upper bound on the outage probability. Next, we use these results to find an outer bound and an inner bound on the ϵ -outage achievable rate region, i.e., the rate region in which the outage probability is below a certain value ϵ.

This paper considers distributed transceiver design and power control for K-user multiple-input-multiple-output interference networks. Each source intends to send multiple independent data streams to its corresponding destination where the number of data streams coincides with the degrees of freedom of the network. Each data stream is encoded at a fixed data rate, whereas different streams can be encoded at possibly different rates. We assume that only local channel side information (i.e., knowledge related to channels directly connected to a terminal) can be acquired by each terminal. We propose iterative algorithms to perform both power control and transceiver design. Transmitter beamforming matrices and receiver filtering matrices are designed to maximize signal-to-interference-plus-noise ratio corresponding to each stream, and a power control scheme is performed to assign the minimum power to each encoded data stream such that successful communication can be guaranteed. The proposed algorithms exhibit a substantial performance improvement compared with the conventional orthogonal transmission schemes.

Considering the time-varying K-user single-antenna interference channel (IC), it has been shown that, when terminals have perfect global channel state information (CSI) and they can tolerate asymptotically long delay, applying an ergodic interference alignment (EIA) scheme can achieve half of the interference-free achievable rate. However, in practice, obtaining such CSI is challenging, and only a limited delay is acceptable. This paper addresses data transmission over the IC by taking these concerns into account. Specifically, we consider the case that each transmitter attains only quantized CSI via limited feedback signals. This causes imperfect interference alignment and a degraded performance. We propose adaptive schemes to compensate the impact of the CSI uncertainties. We first study a power control problem which is concerned with communicating at fixed rates using minimum transmit powers. A power control algorithm is used to reach the solution. Next, we address a throughput maximization problem when the transmit powers are fixed. Through the analysis of system outage probability, we propose a rate adaptation scheme to maximize throughput. Finally, we quantify the throughput loss in delay-limited systems. Our results show that, even with limited feedback, performing the EIA scheme with proper power control or rate adaptation strategies can still outperform conventional orthogonal transmission approaches.

We study the achievable sum degrees of freedom (DoF) in wireless single-antenna multi-user multi-hop relay networks. In most existing works targeting this problem, it is assumed that relays operate in perfect full-duplex fashion and can also shield their receptions from the transmissions of other relays in the same and rear layers. In practice, such ideal assumptions are normally hard to realize. And a naive adoption of half-duplex operation in relays may result in significantly inefficient use of available radio resources. We propose spectrally-efficient transmission schemes to address this issue. A lower-bound to the networks' available DoF (i.e., optimally achievable sum DoF) hence can be attained. Our results indicate that besides providing information delivery paths, relays can also bring DoF gain over single-hop networks, even when relay interference issues are taken into consideration. This lower-bound would approach the upper-bound of the available DoF, if each relay layer deploys more nodes. When the number of relays in every layer is infinitely large, the exact available DoF is identified, which shows that handling interference issues through distributed signal processing and half-duplex operation across multiple layers of terminals may not negatively affect DoF performance, compared with ideal full-duplex and multi-antenna networks.

Visible light communication (VLC) has recently emerged to become a promising wireless communication technology. Vehicle lights and traffic lights have started to utilize LEDs and due to their shorter response time, they can be easily modified to become VLC transmitters. In addition, cameras embedded in smartphones can be used as VLC receivers. As a result, Vehicular VLC (V2LC) between vehicle lighting and smartphone cameras has the potential to enable a great number of applications with low cost. In this paper, a prototype V2LC system that utilizes undersampled frequency shift ON-OFF keying (UFSOOK) modulation is proposed. The system utilizes rolling shutter cameras as the receiver and takes advantages of its characteristics to improve the receiving performance. An off- the-shelf vehicle LED taillight is used as the transmitter. Information is transmitted in the continuous state (ON-OFF) changes of LEDs which are invisible to human eyes. The performance evaluation results demonstrate that the communication prototype is robust and can resist common optical interferences and noises within the image.

Many researchers have recently proposed using LTE to support traffic safety and transportation efficiency applications in vehicular ad hoc networks (VANETs). The deviceto- device (D2D) communication technique underlay LTE network has been considered as an effective way to potentially conduct vehicle-to- vehicle (V2V) communications. Since the vehicular communication environment has different characteristics compared with conventional cellular systems, efficient resource allocation strategies for D2D communications in VANETs are demanded. In this paper, we propose two such algorithms aiming at minimizing the consumption of cellular radio resources. We divide the vehicles on a road into multiple clusters, each of which utilizes a single cellular user's resource. One of the proposed algorithms is constructed based on the dynamic programming technique to search the optimal clustering solution. The other seeks to solve the resource allocation problem with low computing complexity. The performances of the algorithms are exhibited through simulations under practical traffic environments.

This paper characterizes achievable rate regions for the Gaussian partially cognitive interference channel with multiple cognitive pairs (K-PCIFC), where the cognitive transmitters know one part of the primary transmitter's message. We explore a novel methodology using the deterministic discrete superposition model (DSM). We find codes and their achievable rate regions in the DSM, and we show that they yield achievable rate regions in the Gaussian model. Our coding scheme in the DSM can be applied in general scenarios. We devise an achievable rate region for the K-PCIFC, which is within a constant gap to known outer bounds for K = 2 in the weak interference regime. From our expressions we derive guidelines for designing the cognitive link between the primary transmitter and the cognitive transmitters. Furthermore, we show that the gain from cognition diminishes for increasing K.

A downlink two-hop MISO broadcast network is considered, with a two-antenna source communicating to 2 single-antenna destinations, via multiple single-antenna relays in between. The sum degrees of freedom (DOF) of the network with mixed channel state information at the transmitter (CSIT) is investigated. The mixed CSIT consists of accurate delayed CSIT and inaccurate instantaneous CSIT, and its availability is limited within each hop, i.e., the source is oblivious to the channels of the second hop. Given a transmission power P and a real value α ∈ [0, 1], if the variance of the error for instantaneous CSIT decreases as O(P-α), it is shown that the sum optimal DOF of the considered network is d = 4+2α/3 when there exist at least 3 intermediate relays. The result can be extended to the MIMO and multiple-hop cases. The proposed achievable schemes essentially combine the concept of retrospective interference alignment based on delayed CSIT and linear beamforming based on inaccurate instantaneous CSIT into an integrated form. Our results show that, in multi-hop MISO broadcast networks, delayed CSIT and inaccurate instantaneous CSIT can be exploited simultaneously to benefit network DOF.

We consider a downlink two-hop MISO broadcast network with a 2-antenna source communicating to 2 single-antenna destinations, via 2 single-antenna relays. We investigate spectrally efficient transmission and the associated achievable sum degrees of freedom (DoF) with mixed channel state information at the transmitter (CSIT), which consists of perfect delayed CSIT and imperfect instantaneous CSIT. When the variance of the estimation error of the instantaneous CSIT lies on level of O(P-α) for the transmission power P and some α ∈ [0, 1], we show that the sum DoF 4-2α/3-2α ∈ [4/3, 2] can be achieved by a novel interference alignment (IA) scheme. The result shows that rather than exploiting only delayed or imperfect instantaneous CSIT, the transmission design taking advantages of both can achieve higher sum DoF.

We investigate the time-varying Gaussian interference channel (IC) in which each source desires to communicate to an intended destination. For the ergodic time-varying IC with global perfect CSI at all terminals, it has been known that with an interference alignment technique each source-destination pair can communicate at half of the interference-free achievable rate. In practice, the channel gains are estimated by transmitting known pilot symbols from the sources, and the channel estimation procedure is hence prone to errors. In this paper, we model the channel estimation error at the destinations by an independent additive Gaussian noise and study the behavior of the ergodic interference alignment scheme with the global noisy CSI at all terminals. Toward this end, we present a closed-form inner bound on the achievable rate region by which we conclude that the achievable degrees of freedom with global perfect CSI can be preserved, if the variance of channel estimation error is proportional to the inverse of the transmitted power.

This paper considers joint transceiver design and power control for K-user multiple-input multiple-output (MIMO) interference networks. Each source intends to communicate with its corresponding destination at a fixed data rate. Only local channel side information (i.e. knowledge related to the channels directly connected to a terminal) is available at each terminal. We propose iterative algorithms to perform power control to guarantee successful communication while designing transmitter beamforming matrices and receiver filtering matrices according to the interference alignment concept. The proposed algorithms can exhibit a substantial performance improvement compared to the conventional orthogonal transmission schemes.

We study the achievable sum degrees of freedom (DoF) in a class of wireless single-antenna multi-hop half-duplex relay networks. The networks contain Msinformation sources, Md information destinations, and arbitrary layers of relays, each with 2K (K ≥ max{Ms,Md}) half-duplex relays, in between. A cluster successive relaying transmission scheme is applied to conduct the communication: We divide the relays in each layer into two equal-size clusters and activate them successively to efficiently use the available channel. It is shown that in a time-varying fading environment this scheme asymptotically achieves the sum DoF min { MsK/Ms+K, MdK/Md+K-1}, which is irrelevant to the number of hops the source messages have to pass through. This result also implies that, when the number of relays in each layer is infinitely large, the available DoF (i.e. the optimally achievable sum DoF) of the considered networks is min{Ms,Md}. Neither distributed signal processing nor multiple layers of half-duplex relay operation negatively affects the system DoF performance.

We consider a downlink two-hop MISO broadcast network with a 2-antenna source communicating to 2 single-antenna destinations, assisted by 2 single-antenna intermediate relays. We investigate spectrally efficient transmission schemes and their achieved sum degrees of freedom (DoF), with quantized delayed channel state information (CSI) feedback. Assuming Grassmannian vector quantization, we study two feedback scenarios according to the feedback range limit, namely global-range feedback, i.e., the source can receive the feedback signals from both the relays and the destinations, and one-hop-range feedback, i.e., each node can only attain the feedback information of its upcoming hop. We establish a sum DoF lower bound for each case. Our results reveal that when the quantization rate at relays BR= α1log2(SNR) and at destinations BD= α2log2(SNR) for min{α1, α2} ≥ 1, the optimal sum DoF 4 over 3 can be achieved with finite-rate delayed feedback.

The interference channel (IC) is the basic underlying channel model for many practical multi-terminal communication scenarios. Here, we consider a modified three-user IC with dual-antenna transmitters and receivers where we allow one receiver to have a finite-rate unidirectional link to another receiver. This kind of receiver-to-receiver link may exist in cellular networks with base-station backhaul. For this scenario we propose a scheme that combines the interference-cancellation capability of Alamouti codes with the subspace-overlapping property of interference alignment to achieve a sum-rate of three symbols/channel use. The scheme does also simultaneously provide a diversity gain of two for every symbol which significantly improves the bit-error rate performance compared to zero-forcing techniques.

A multi-antenna cognitive radio network, with a single pair of primary users and a secondary broadcast channel, is considered. Under perfect channel state information (CSI), the rate-optimal strategy for the primary link is waterfilling, resulting in possibly unused dimensions. The secondary base station, supplied with perfect and global CSI, employs block diagonalization for interference-free message precoding and opportunistic interference alignment to avoid disturbing primary communication. In the absence of sufficient resources to serve all secondary users, a low complexity adaptive antenna selection strategy is proposed. This scheme constructs, in each time-slot, the set of active antennas by opportunistically reusing information and resources from the preceding selection round. Numerical results indicate a complexity reduction at a small rateloss penalty, in comparison with existing selection methods.

We investigate the achievable degrees of freedom (DoF) in K-user X networks (K×K X networks) with delayed channel state information at transmitters (CSIT), where partial cooperation (i.e. message sharing) is potentially allowed among transmitters. We consider two possible cooperation scenarios. In the first scenario one of the transmitters serves as a super node which can obtain the messages of the other transmitters. By proper interference alignment (IA) design, we prove that a DoF 2K/K+1 can be achieved almost surely. In the second scenario, there is no super node but each transmitter shares its message to its left-side neighbor. We show that when K = 3, DoF 7/5 is achievable. In both cases, the achieved DoF are shown to be improved compared with non-cooperative X networks. Moreover, we use a simple example to show that sharing a subset of messages may also improve DoF.

We study the error and diversity-multiplexing tradeoff (DMT) performance of a (secondary) multi-user relay network, where a class of finite field network codes are applied in the relays to efficiently provide spatial diversity. To eliminate spectral efficiency loss induced by half-duplex limitation we adopt the cognitive radio overlay spectrum sharing concept and consider aligning the relays' operation with that of a primary system. To compensate the interference introduced by the secondary relaying, the secondary destination also transmits the primary signals to boost the signal power of the primary system. We also consider exploiting Automatic Repeat Request (ARQ) feedback signals from the secondary destination to minimize the energy consumption of the secondary system. In addition, by allowing multiple secondary sources to transmit non-orthogonally, the performance can be further enhanced.

We study the available degrees of freedom (DOF) of a class of wireless single-antenna multi-user relay networks. In these networks the communications between M unconnected source-destination pairs are provided by a large number of half-duplex relays. To conduct the communications we propose a cluster successive relaying protocol that divides the relays into two equal-size clusters. Unlike the conventional orthogonal relaying protocol that demands all the relays to simultaneously assist the sources, we require the two relay clusters to take turns forwarding the source messages to more efficiently use the channel. In a time-varying fading environment, through appropriate interference alignment the negative impact of inter-user interference can be effectively minimized. Thus the two clusters of half-duplex relays can mimic a cluster of full-duplex relays. When the number of relays is infinitely large, we show that the M-user half-duplex relay networks have M DOF, i.e. their sum capacity can be characterized as CΣ(SNR) = M log (SNR) + o(log (SNR)). This result implies that allowing only distributed processing and half-duplex operation is able to provide the same DOF performance as permitting joint processing and full-duplex operation in wireless relay networks.

We study the impact of multiple access protocols on the diversity-multiplexing tradeoff (DMT) performance in wireless multi-user relay networks. In the networks K half-duplex decode-and-forward (DF) relays employ a class of finite field network codes to assist in the communication between M independent sources and a common destination. The sources and relays are divided into individual clusters. The nodes within one cluster transmit non-orthogonally while the transmissions of different clusters span orthogonal channels. We provide the method to calculate the achievable DMT for each clustering strategy. The network DMT performance can thus be optimized by properly clustering the sources and the relays.

Considering a single-antenna M-user interference channel with symmetrically distributed channel gains, when the channel state information (CSI) is globally available, applying the ergodic interference alignment scheme, each transmitter-receiver pair achieves a rate proportional to ½ of a single user's interference-free achievable rate. This is substantially higher than the achievable rate of the conventional orthogonal transmission schemes such as TDMA. Since the rigid requirement on the CSI may be difficult to realize in practice, in this paper we investigate the performance of applying the ergodic interference alignment scheme when the estimation of each channel gain is made globally known through exploiting only a limited feedback signal from the associated receiver of that channel. Under a block fading environment, we provide a lower bound on the achievable average throughput of the network. Our results imply that the better performance of interference alignment over TDMA may still exist even without the assumption of perfect CSI. Also, the trade off between allocating feedback rate of each receiver to the desired channel or the interference channels at deferent SNR region investigated.

We study the impact of multiple access strategies on the diversity-multiplexing tradeoff (DMT) performance in wireless multi-user relay networks. The networks contain multiple independent sources, multiple half-duplex decode-and-forward (DF) relays, and one common destination. Instead of separately retransmitting each source message, the relays employ a class of spectrally efficient finite field network codes to assist the sources. It is shown that fully orthogonal or fully non-orthogonal transmission among sources/relays does not necessarily provide optimized DMT performance. We propose a novel transmission protocol that divides the sources and relays into individual clusters. The nodes within one cluster transmit non-orthogonally while the transmissions of different clusters span orthogonal channels. We provide the method to calculate the achievable DMT for each clustering strategy. The network DMT performance can thus be optimized by properly clustering the multiple sources and relays.

We study the degrees of freedom (DOF) of a single- antenna M-user time-varying parallel relay network, where the communications between M pairs of unconnected sources and destinations are provided by a large number of half-duplex decode-and-forward (DF) relays. Unlike the conventional relaying strategy which demands all the relays to simultaneously assist the sources, we divide the relays into two clusters and permit them to take turns forwarding the source messages. With appropriate interference alignment design, it is proved that the M-user time-varying relay network has M DOF, provided that the number of relays is infinitely large.

In this paper, a novel cooperative diversity transmission protocol is proposed for a four-node network where a single-antenna source communicates with its intended N-antenna destination with the help of two K-antenna decode-and-forward (DF) relays. Without requiring complex coding strategies at the relays, a sufficiently strong or weak inter-relay channel, or destination-source feedback, the proposed shifted successive DF relaying (SSDFR) protocol asymptotically achieves the optimal diversity-multiplexing tradeoff performance the four-node network can provide.

We study the impact of efficient network coding and multiple access techniques in a two-source two-relay one-destination wireless network through the diversity-multiplexing tradeoff (DMT) analysis. We compare the standard relaying protocol without network coding and the protocols using either a binary network coding (BNC) or an appropriately designed non-binary finite-field network coding (FFNC) at the relays. It is shown that the use of the non-binary FFNC strictly outperforms the other two protocols in terms of DMT. In addition, we propose a new transmission strategy based on the non-binary FFNC design and the non-orthogonal multiple access technique to further improve the DMT performance. Our results highlight the advantages of applying appropriate network coding in multi-source multi-relay networks.

A novel cooperative diversity transmission protocol using two K-antenna decode-and-forward relays to take turns assisting in the communication between a single-antenna source and its intended N-antenna destination is studied. A (1+2⌈logK⌉)-bit destination-relay feedback signal is exploited to perform relay/antenna selection. Two different simple forwarding strategies are applied at the relays: one relay uses a superposition-coding strategy while the other relay uses a repetition-coding strategy. When the source's frame length is sufficiently large, the optimal diversity- multiplexing tradeoff of the four-node network can be achieved asymptotically if K ≥ 3.

A novel cooperative diversity transmission protocol is proposed for a four-node network, in which a single-antenna source communicates with an N-antenna destination with the help of two K-antenna half-duplex decode-and-forward (DF) relays. The proposed shifted successive DF relaying (SSDFR) protocol asymptotically achieves the optimal diversity-multiplexing tradeoff (DMT) the system can provide. The resulting DMT performance does not require complex coding strategies at the relays, extremely strong or weak inter-relay channel conditions, or feedback from the destination to the source, which highlights the advantages of the proposed protocol over state of the art techniques.

Previous work on cooperative communications has concentrated primarily on the diversity benefits of such techniques. This paper, instead, considers the multiplexing benefits of cooperative communications. First, a new interpretation on the fundamental tradeoff between the transmission rate and outage probability in multiple-antenna relay networks is given. It follows that multiplexing gains can be obtained at any finite signal-to-noise ratio (SNR), in full-duplex multiple-antenna relay networks. Thus, relaying can offer not only stronger link reliability, but also higher spectral efficiency. Specifically, the decode-and-forward protocol is applied and networks that have one source, one destination, and multiple relays are considered. A receive power gain at the relays, which captures the network large-scale fading characteristics, is also considered. It is shown that this power gain can significantly affect the system diversity-multiplexing tradeoff for any finite SNR value. Several relaying protocols are proposed and are shown to offer nearly the same outage probability as if the transmit antennas at the source and the relay(s) were colocated, given certain SNR and receive power gains at the relays. Thus, a higher multiplexing gain than that of the direct link can be obtained if the destination has more antennas than the source. Much of the analysis in the paper is valid for arbitrary channel fading statistics. These results point to a view of relay networks as a means for providing higher spectral efficiency, rather than only link reliability.

The diversity-multiplexing tradeoff (DMT) performances of novel decode-and-forward (DF) relaying protocols for multi-source multi-relay cooperative networks are studied in this paper. For a strong-interference scenario and an isolated-relay scenario, the proposed protocols significantly improve diversity performance over direct source-destination transmissions by using a simple repetition coding strategy in relays. This is in addition to enhancing the multiplexing performance over standard DF protocols, which usually suffer from the half-duplex limitation at relays. Although the DMT performance of DF based relaying protocols is limited by the quality of source relay links in principle, adaptive forms of the proposed protocols at the relays can transmit/not transmit according to source relay transmissions and still provide link reliability and spectral efficiency advantages in general source-relay channel conditions.

In this paper, a superposition-coded concurrent decode-and-forward (DF) relaying protocol is presented. A specific scenario, where the inter-relay channel is sufficiently strong, is considered. Assuming perfect source-relay transmissions, the proposed scheme further improves the diversity performance of previously proposed repetition-coded concurrent DF relaying, in which the advantage of the inter-relay interference is not fully extracted.

In this paper, the diversity-multiplexing tradeoff (DMT) behavior of a novel concurrent decode-and-forward (DF) relaying cooperative diversity transmission protocol is analyzed. A two-source two-relay one-destination scenario is considered in which concurrent transmission among the network nodes is used by combining the two sources' two classic DF relaying steps. Through the DMT analysis, it is shown that the proposed protocol can effectively recover the multiplexing loss induced by the classic DF relaying protocol, while still obtaining diversity gain. The system model is further extended to a generalized M-source network.

In this paper, a transmission protocol is studied for a two relay wireless network in which simple repetition coding is applied at the relays. Information-theoretic achievable rates for this transmission scheme are given, and a space-time V-BLAST signalling and detection method that can approach them is developed. It is shown through the diversity multiplexing tradeoff analysis that this transmission scheme can recover the multiplexing loss of the half-duplex relay network, while retaining some diversity gain. This scheme is also compared with conventional transmission protocols that exploit only the diversity of the network at the cost of a multiplexing loss. It is shown that the new transmission protocol offers significant performance advantages over conventional protocols, especially when the interference between the two relays is sufficiently strong.