The emergence of sixth generation communication (6G) wireless networks is set to revolutionize vehicular communication by enabling ultra-reliable, low-latency, and high-capacity connectivity in cellular vehicle-to-everything (C-V2X) environments. This paper presents a theoretical framework on novel cooperative vehicular communication and information perception algorithms for large-scale 6G C-V2X networks while leveraging integrated space-air-ground communication system. Specifically, we address key challenges in real-time information exchange and fusion among multiple vehicles. Utilizing inequality theory and functional mapping theory, we derive an upper bound on channel capacity for a fixed number of relays and propose a low-complexity, multi-class relay selection algorithm. Furthermore, we introduce an optimal mobile edge computing (MEC) based correspondence strategy to improve vehicle-to-vehicle communication, alongside an efficient information estimation algorithm to facilitate real-time data sharing. Our simulation results confirm that the proposed algorithms significantly outperform existing cooperative vehicular schemes in terms of channel capacity, while the developed evaluation theory ensures accurate cooperative perception with reduced computational complexity. The proposed framework and theoretical contributions offer a foundational basis for 6G C-V2X networks.

With the emergence of extremely large-scale antenna arrays (ELAAs), the next generation of wireless communication is likely to occur in the radiating near-field region of base stations (BS). In such regions, beam training needs to search both the angle and distance dimensions, leading to a prolonged training process and coverage hole (dead zone). To cope with those issues, we propose a novel codebook design guideline for the uniform circular array by maximizing the overlapping coverage between the beam coverage (BC) and near-field region, where the energy spread effect in the near-field region is exploited to obtain the optimal focusing point to improve the beam gain inside the dead zone. Based on this guideline, we construct the beam coverage-based codebook structure and two-stage beam training (TSBT) scheme. Numerical simulations show that the TSBT scheme with the proposed codebook can potentially reduce beam training overhead while improving the success rate and patching the dead zone.

This paper introduces a novel cooperative vehicular communication algorithm tailored for future 6G ultra-massive vehicle-to-everything (V2X) networks leveraging integrated space-air-ground communication systems. Specifically, we address the challenge of real-time information exchange among rapidly moving vehicles. We demonstrate the existence of an upper bound on channel capacity given a fixed number of relays, and propose a low-complexity relay selection heuristic algorithm. Simulation results verify that our proposed algorithm achieves superior channel capacities compared to existing cooperative vehicular communication approaches.

The conventional machine learning (ML)–based channel state information (CSI) acquisition has overlooked the potential privacy disclosure and estimation overhead problem caused by transmitting pilot datasets during the estimation stage. In this paper, we propose federated edge learning for CSI acquisition to protect the data privacy in the Internet of vehicle network with massive antenna array. To reduce the channel estimation overhead, the joint model pruning and vector quantization algorithm for network gradient parameters is presented to reduce the amount of exchange information between the centralized server and devices. This scheme allows for local fine‐tuning to adapt the global model to the channel characteristics of each device. In addition, we also provide theoretical guarantees of convergence and quantization error bound in closed form, respectively. Simulation results demonstrate that the proposed FL‐based CSI acquisition with model pruning and vector quantization scheme can efficiently improve the performance of channel estimation while reducing the communication overhead.

Extremely large-scale antenna arrays (ELAAs) introduce a new communication paradigm called near-field communications, where users are likely to operate in the near-field region of the base-stations (BSs). In such a region, beam training needs to search both the angle and distance dimensions, leading to a prolonged training process and a coverage hole (dead zone). To cope with this issue, we developed a beam depth-based codebook and training scheme for near-field ELAA systems. As the performance of codebook design is mainly dictated by the array configurations, we study the codebook design considering uniform linear, circular and planar antenna arrays. Specifically, we first offer an integrated model to characterize the near-field channel for the considered array configurations. Then, we propose a novel codebook design guideline by maximizing the overlap depth between the near-field codeword (beam) coverage and near-field region, where the energy spread effect is exploited to obtain the optimal focusing point to improve the beam gain inside the dead zone. Based on this guideline, we respectively construct the beam depth based on two-stage and hierarchical codebooks as well as the corresponding beam training schemes. Numerical simulations show that the proposed codebook based beam training schemes can potentially reduce beam training overhead while improving the success rate and beam gain inside the dead zone.

Extremely large-scale multiple-input multiple-output (XL-MIMO) wideband systems may exhibit the severe delay spread, due to its spatial- and frequency-wideband (dual-wideband) effects. The typical orthogonal frequency division multiplexing (OFDM) technology have to insert a larger number of cyclic prefix (CP) to overcome the inter-symbol interference (ISI) induced by delay spread. The additional CP overhead will counteract the improvement of spectral efficiency by the large antenna array. To address the issue, this paper proposes a structured OFDM (SOFDM) modulation approach to reduce the CP overhead for wideband XL-MIMO systems with dual-wideband effects. As the ability to perform SOFDM is affected by the antenna architecture, we study the modulation technique considering different antenna structures, including fully-digital, phase shifter-based hybrid array, and dynamic metasurface antenna (DMA) architectures. Specifically, we first provide a mathematical model to represent a near-field channel with dual wideband effects. Based on the channel model, we develop the SOFDM modulation and then propose a joint spatial precoding and frequency domain equalization scheme to maximize the system spectral efficiency, where the solutions of precoding/combining and equalization matrices are derived for the three types of antenna array architectures. Numerical simulations indicate that the proposed scheme can effectively deal with the dual-wideband effects and significantly improve the spectral efficiency with low CP overhead.

The forthcoming wireless network promises revolutionary advancements with significantly higher peak data rates, reduced latency, and vastly improved reliability. Among pivotal technologies, the design of novel multiple access schemes, particularly rate-splitting multiple access (RSMA), holds significant importance. In this article, we focus on the joint optimization of user scheduling, rate allocation, and beamforming for downlink multiple-input single-output communication networks under RSMA finite blocklength (FBL) transmission. The difficulty of the formulated optimization problem lies on the achievable rate function with FBL transmission and the joint design of user scheduling and beamforming. In order to solve the formulated problem, we first analyze the convexity and feasibility of the achievable rate function and further provide an efficient algorithm by cooperatively using strong Lagrangian duality, the difference of convex functions programming, the big-M method, and the alternating optimization algorithm for the joint optimization process. Numerical simulations validate the effectiveness of the proposed approach, offering promising insights for the future of 6G wireless networks.

Extremely large-scale multiple-input multiple-output (XL-MIMO) communication system with extremely large-scale antenna arrays can achieve ultra-high spectral efficiency. However, the conventional far-field beam codebooks may be mismatched with the near-field spherical-wavefront channel caused by large array aperture, which results in severe performance loss. To address this problem, we develop a criterion of code book design to maximize the worst-case beam gain within the beam coverage. Then, a closed-form expression of the near-field full dimensional (FD) codebook with non-orthogonal structure is derived, which can realize the spatial oversampling regardless of the number of antennas at the transceiver. Simulation results show that our proposed non-orthogonal codebook can potentially improve the accuracy of near-field beam training, compared with existing codebooks.

Massive multiple-input multiple-output (mMIMO) channels in wideband systems may exhibit the severe delay spread, due to its spatial- and frequency-wideband (dual-wideband) effects. The typical orthogonal frequency division multiplexing (OFDM) technology have to insert a larger number of cyclic prefix (CP) to overcome the inter-symbol interference (ISI) induced by delay spread. The additional CP overhead will counteract the improvement of spectral efficiency by the large antenna array. To address the issue, this paper proposes a structure OFDM modulation approach to reduce the CP overhead for wideband mMIMO systems with dual-wideband effects. Specifically, we propose a joint spatial precoding and frequency domain equalization scheme to maximize the system spectrum efficiency, where the closed-form solutions of precoding vectors and equalization matrices are derived by exploiting the unique characteristic of composite channel matrix. Numerical simulations indicate that the proposed scheme can effectively deal with the dual-wideband effects and significantly improve the spectral efficiency with low CP overhead.

Dynamic metasurface antennas (DMAs) provide a new paradigm to realize large-scale antenna arrays for future wireless systems. In this paper, we study the downlink millimeter wave (mmWave) DMA systems with limited number of radio frequency (RF) chains. By using the specific DMA structure, an equivalent mmWave channel model with hybrid beamforming is first explicitly characterized. Based on that, we propose an effective joint microstrip selection and beamforming scheme to accommodate for the limited number of RF chains. A low-complexity digital beamforming solution with channel gain-based microstrip selection is developed, while the analog beamformer is obtained via a coordinate ascent method. The proposed scheme is numerically shown to approach the performance of DMAs without RF chain reduction, verifying the effectiveness of the proposed schemes.

Finite blocklength (FBL) transmission is a promising technique to meet the stringent delay and reliability requirements. In this paper, based on the FBL transmission mode, we formulate a joint user scheduling and beamforming (USBF) optimization problem with maximizing the system utility related to the long-term average rate, which considers variable user numbers and statistical wireless channels. To address the long-term optimization problem, by utilizing the Lyapunov optimization method, it can be transformed into the admission control subproblem and the joint USBF subproblem with the instantaneous channel state information (CSI) of each user. Then, an iteration method with successive convex approximation (SCA) is presented to solve the transformed instantaneous optimization problem. The simulation results confirm that the proposed scheme can achieve a significant performance gain on the premise of meeting the requirements of uRLLC.

In multiuser communication systems, user scheduling and beamforming (US-BF) design are two fundamental problems that are usually studied separately in the existing literature. In this work, we focus on the joint US-BF design with the goal of maximizing the set cardinality of scheduled users, which is computationally challenging due to the non-convex objective function and the coupled constraints with discrete-continuous variables. To tackle these difficulties, a successive convex approximation based US-BF (SCA-USBF) optimization algorithm is firstly proposed. Then, inspired by wireless intelligent communication, a graph neural network based joint US-BF (J-USBF) learning algorithm is developed by combining the joint US and power allocation network model with the BF analytical solution. The effectiveness of SCA-USBF and J-USBF is verified by various numerical results, the latter achieves close performance and higher computational efficiency. Furthermore, the proposed J-USBF also enjoys the generalizability in dynamic wireless network scenarios.

The intelligent reflecting surface (IRS) technology is emerged as an enabling technology for beyond fifth-generation systems and Internet of Things networks in which the signal propagation is reconfigured to enhance wireless system performance. IRS consists of many passive elements and each reflecting the incident signal with a certain phase shift to collectively achieve the required beamforming. The IRS is to be a low profile and lightweight setting with a conformal geometry; hence, its position can be easily engineered to achieve certain performance enhancements. In the current literature, however, the flexibility in the IRS position is often overlooked since it is considered as a given fixture. We argue that optimizing the IRS position provides a new degree of freedom in the network design and enables extra performance gain. In this article, we analytically characterize the optimal IRS’s position to maximize the achievable system rate. We then obtain the optimal IRS positions for different IRS settings with fixed height and variable height and consider both cost-efficient equal phase shift IRS, and nonequal phase shift IRS that enables sophisticated beamforming. We further incorporate antenna directivity in our analysis and investigate its effect on the optimal IRS position in each case. Simulation results show that the provided optimal position yields higher performance than settings with random IRS locations. Our results provide significant practical insights on the network coverage design using the IRS.

The fundamental intelligent reflecting surface (IRS) deployment problem is studied for IRS-aided downlink multi-user communication system, where IRSs are arranged to be deployed in a specific area for enhancing the desired signal and suppressing interference. Specifically, we aim to maximize the minimum achievable rate over all locations in a specific area by jointly optimizing the transmit beamforming at the access point (AP) as well as the placement and reflective beamforming at the IRS. The formulated problem is non-convex and thus difficult to be solved directly. To draw essential insights, we first consider the single-user case and the optimal solution is derived in closed-form. The result shows that the optimal locations are in the connecting line between the AP and user, and the IRSs can be optimally deployed along the connecting line. Besides, a hybrid offline and online design scheme is developed for the multi-user case, where an area discretization strategy and deep neural network (DNN)-based curve fitting technique are proposed for optimizing the IRS locations in the offline manner. Then, an online iterative algorithm is presented to solve the transmit and received beamformig vectors, respectively. Numerical results show that the performance gain is increased by optimizing the IRS locations.

The potential application of intelligent reflecting surfaces (IRSs) for future wireless cellular communication systems has motivated the study of metasurface for achieving additional space degree of freedom, where IRS is used to enhance the desired signal strength and suppress the interference. In this paper, by using the additional design degree of freedom provided by the IRS, we jointly optimize the transmit beamforming vector at the BS and the reflective beamforming vector at the IRS to maximize the minimum rate in the IRS-aided multi-user multiple-input-single-output broadcast channel (MISO-BC), subject to the unit modulus constraints of the reflective beamforming vector. In order to solve the non-convex optimization problem, we propose an efficient algorithm based on alternating optimization. In particular, we optimize the transmit beamforming vectors via the second-order cone problem (SOCP) and reflective beamforming vector by using the semi-definite relaxation (SDR). Numerical results show that the use of IRS leads to significant higher SINR values than benchmark schemes without IRS.

The in-band full-duplex (IBFD) wireless communication has been spotlighted as one of the promising technologies to enhance throughput performance in the future WLANs. One way to leverage full-duplex capability in practical scenario is to enable three-node transmission, where a full-duplex access point (AP) transmits date to one half-duplex user while receives date from another half-duplex user. Such full-duplex communication mode, however, introduces extra uplink-downlink interference, which may degrade the full-duplex gain. In this paper, with the target of fully achieving the performance improvement brought by the full-duplex transmission, we investigate the joint optimization of scheduling and power control in three-node full-duplex WLANs. Specifically, the problem formulation is to maximize the aggregate utility of downlink users under specific date rate constraints of uplink users. In particular, the optimization is conducted by jointly considering the transmit powers of the AP and uplink users, the access-intensity of uplink users, and the uplink-downlink user paring. Such an optimization problem is a classical mixed integer nonlinear programming problem (MINLP) and generally NP-hard. To solve it, we develop an efficient iterative algorithm based on alternating optimization and successive convex approximation (SCA). Numerical results verify that the proposed scheme achieves higher utility compared to other existing schemes.

For the high density of users and accompanying network service requirements in the cellular system, Device-to-Device (D2D) communication is a promising technology to cope with the increasing wireless traffic demands by reusing spectrum resources. In practice, the wireless signal is easy to be eavesdropped in D2D communications underlaying cellular networks, hence, ensuring a secure communication for cellular user equipments (CUEs) is an urgent and meaningful problem. In this paper, we propose a joint subcarrier and power allocation scheme for maximizing the sum data rate of D2D pairs, meanwhile protecting the CUEs against eavesdropping. Specifically, in the proposed scheme, we first quantify the security performance with the secrecy data rate, and obtain the closed-form expression for the optimal power allocation of CUEs and D2D pairs by tightening the quality of service (QoS) and secrecy rate requirement constraints of CUEs. Based on the obtained power allocation solution, by searching the optimal mapping relationship between CUEs and D2D pairs, we develop a subcarrier assignment strategy with the Hungarian algorithm to solve it, which can further enhance the sum data rate of D2D pairs. Simulation results demonstrate that the proposed scheme can significantly yield better performance than other schemes.

In recent years, network densification and edge caching become effective approaches to reduce the burden on the fronthaul links and the content delivery latency for wireless communication systems. However, maximizing system spectral efficiency cannot directly provide any insight on their energy requirements/efficiency for cache-enabled millimeter-wave (mmWave) radio access networks (RANs). In this paper, we study the design of energy-efficient transceiver, consisting of analog and digital precoder/combiner, for the delivery phase of the downlink of cache-enabled mmWave RANs. Due to the non-convexity of the delivery rate and objective, the coupling between the digital and analog precoders/combiners, and the constant module constraint on the elements of analog precoders/combiners, the problem of interest is non-convex and hard to obtain the global optimal solution, even the local optimal solution. To this end, we first overcome these challenges one-by-one and then transform the original problem into tractable one. Finally, an algorithmic framework that converges to the Karush-Kuhn-Tucker solution with provable is developed to achieve the design of energy-efficient transceiver. Numerical results are provided to evaluate the performance of the proposed algorithm, where fully digital precoding is used as benchmark.

For millimeter wave (mm-wave) communication systems, a lens antenna array with single-carrier transmission and path delay compensation is a promising technique for realizing cost-effective large multiple-input multiple-output communications with limited number of radio frequency chains. In this paper, we study the multi-user mm-wave downlink lens antenna array system for the general frequency-selective channels. By leveraging the angle-dependent energy focusing property of the lens antenna array and the angular sparsity of mm-wave channels, we investigate the low-complexity single-carrier transmission scheme with path delay pre-compensation applied at the base station (BS). The resulting signal-to-interference-plus-noise ratio (SINR) is derived by taking into account both the residual inter-symbol interference and inter-user interference. Based on the derived SINR expression, we propose an effective joint antenna selection and beamforming scheme by utilizing the group sparse optimization to accommodate for the limited number of RF chains at the BS. Thus, the proposed scheme can obtain the approximate performance with the fully digital case and has a better performance than the conventional orthogonal frequency-division multiplexing mode for the frequency-selectivity channels. Numerical results are provided to verify the effectiveness of the proposed schemes.

To fully exploit the spatial multiplexing gains and array gains of massive multiple-input-multiple-output (MIMO), the channel state information must be obtained accurately at the transmitter side (CSIT). However, conventional channel estimation solutions are not suitable for Frequency-Division Duplexing (FDD) multi-user massive MIMO systems, due to overwhelming pilot and feedback overhead. In this paper, We find that part of the user channels tend to exhibit an approximate beam-blocked sparsity. To exploit this property, we propose a novel blocked compressive channel estimation scheme based on user grouping to reduce the pilot and feedback overhead. More specifically, we adopt user grouping by making the users in one group have similar channel covariance, which makes the channels in one group exhibit beam block sparsity. Then users feed the compressed measurements back to BS and the BS performs the CSIT recovery. Using the beam block sparsity, an optimal block orthogonal matching pursuit algorithm (OBOMP) is developed which effectively recovers the channel parameters. Numerous simulation results demonstrate our proposed scheme outperforms conventional solutions.

In this paper, we propose a load-aware and QoS- aware user association strategy that jointly considers the load of each BS and user's achievable rate instead of only utilizing the latter, and formulate it as a network-wide weighted utility maximization problem to reveal how a heterogeneous cellular network should self-organize. This is a nonlinear mixed-integer optimization problem, and its optimum solutions are very difficult to be obtained when it is large scale one. To solve the proposed problem, we relax association indicator variables and adopt a gradient descent method to find optimum solutions. Then, each user is associated with some BS with a maximum association indicator taken from solutions of the relaxed optimization problem. Experimental results show that, compared with the best power association and range expansion association, our strategy has a lower call blocking probability and a higher load balancing level.