In this paper, we propose a novel orthogonal frequency division multiplexing (OFDM) waveform framework by capitalizing on the benefits of maximum distance separable (MDS) code and the reconfigurable intelligent surface (RIS). The proposed scheme is referred to as MDS-OFDM-RIS. The proposed design scheme consists of (i) an MDS code based amplitude and phase modulation scheme for OFDM transmission, which helps increase the minimum Hamming distance among symbols and improve on the error detection capabilities, (ii) a RIS that is placed near the radio frequency (RF) source, (iii) as well as a reduced-complexity maximum likelihood (RC-ML) detection algorithm at the receiver by utilizing the error detection ability of the MDS codes. We derive an upper bound for the bit error rate (BER) and a closed-form expression of the mutual information. Using the obtained analytical expressions, we formulate two optimization problems and derive the corresponding optimal solutions for RIS phase shifts. It is found that the two optimization problems share the same optimal solution, which indicates that the obtained RIS phase shifts optimize the system BER and channel capacity simultaneously. Simulation results show that compared with conventional OFDM systems, the proposed system can better combat multipath fading and provide higher channel capacity, especially when the RIS phase shifts are optimal. Moreover, the accuracy and low complexity of the proposed RC-ML detection scheme are demonstrated by numerical results.

We study a downlink cooperative non-orthogonal multiple access (NOMA) system in which a base station (BS) serves two paired users on the same frequency band simultaneously, and the near user acts as an energy-constrained relay for the far user since there is no direct link between the BS and the far user due to physical obstacles or heavy shadowing. To replenish energy of the near user for relaying, we enable the near user to harvest energy from BS signals by adopting the simultaneous wireless information and power transfer (SWIPT) technique. Different from the linear energy harvesting (EH) model used in most of the existing literature, we adopt a non-linear model for EH. By considering the non-linear features of the practical circuits, we aim to minimize the BS energy consumption while ensuring the minimum required transmission rate of both users. Since the formulated problem contains coupled power and time resource variables, it is challenging to solve it directly. Thus, we propose an optimal power-time resource allocation algorithm by decoupling the variables properly. Simulation results verify the theoretical analysis and show the performance of the considered system.

Wireless interference identification (WII) is a critical technology for the non-cooperative communication systems, and it is widely applied into military and civilian scenarios. With enormous success in many computer vision and language processing applications, deep learning (DL) has also achieved the remarkable performance for WII. However, existing DL based WII methods can not dynamically allocate proper computing resources conditioned on the inputs, causing serious waste of resources and low computational efficiency, which is unacceptable for edge computing devices, such as unmanned aerial vehicle (UAV)-aided systems. In this paper, the problem of dynamic computation resource allocation is modeled as adaptively learning forward propagation depth of networks on a per-input basis, and this novel method is termed as dynamic computing resource allocation in convolutional neural networks (DCRACNN). In particular, the proposed DCRACNN is composed of two subnetworks: a main network and a policy network. The main network is equipped with multiple classifiers at different depths, allowing test examples to stop early during inference so as to significantly improve computational efficiency. The policy network is optimized with reinforcement learning to address the bottleneck of non-differentiable decisions of optimal forward propagation depth for the main network while preserving recognition accuracy. To address the challenges of high error rates of early classifiers in the main network, novel methods are introduced, which can be seamlessly integrated into DRCACNN. In addition, a novel network pruning process is proposed for the main network to further reduce model sizes and computational complexity. Experiments demonstrate that DCRACNN can significantly reduce the calculation cost with boosting accuracy for WII.

Terahertz (THz) communications with a frequency band $0.1-10$ THz are envisioned as a promising solution to future high-speed wireless communication. Although with tens of gigahertz available bandwidth, THz signals suffer from severe free-spreading loss and molecular-absorption loss, which limit the wireless transmission distance. To compensate for the propagation loss, the ultra-massive multiple-input-multiple-output (UM-MIMO) can be applied to generate a high-gain directional beam by beamforming technologies. In this paper, a review of beamforming technologies for THz UM-MIMO systems is provided. Specifically, we first present the system model of THz UM-MIMO and identify its channel parameters and architecture types. Then, we illustrate the basic principles of beamforming via UM-MIMO and discuss the far-field and near-field assumptions in THz UM-MIMO. Moreover, an important beamforming strategy in THz band, i.e., beam training, is introduced wherein the beam training protocol and codebook design approaches are summarized. The intelligent-reflecting-surface (IRS)-assisted joint beamforming and multi-user beamforming in THz UM-MIMO systems are studied, respectively. The spatial-wideband effect and frequency-wideband effect in the THz beamforming are analyzed and the corresponding solutions are provided. Further, we present the corresponding fabrication techniques and illuminate the emerging applications benefiting from THz beamforming. Open challenges and future research directions on THz UM-MIMO systems are finally highlighted.

In this paper, we investigate a novel beam direction-based modulation transmit scheme for millimeter-wave (mmWave) communication systems equipped with distributed antennas, and propose an enhanced space-domain index modulation (ESDIM) scheme, in which the number of beamforming directions (BDs) for transmitting information bits can be one or two. The ESDIM system jointly utilizes the combination of the index of BDs and signal constellations to transmit information bits, in which one or two beam directions are considered. In addition, we propose power allocation (PA) aided schemes for ESDIM to further improve the system reliability by reducing the system symbol error rate (SER), where three constraints to the transmit power for beamforming are investigated. To be specific, a total transmit power constraint (TTPC) and two per-beamforming power constraints (PBPC) are considered. We first propose a suboptimal PA algorithm for the TTPC-PA aided ESDIM scheme using a powerful solver based on advanced algebra theories, which only considers the PA aspects for a given selected beamforming pair. Then, we extend the TTPC-PA aided scheme to the average per-beamforming power constraint (APBPC)-PA aided scheme and the strict per-beamforming power constraint (SPBPC)-PA aided scheme. Simulation results show that the proposed schemes improve the system SER performance compared to other existing counterparts.

The dual-functional radar-communication (DFRC) system operating at terahertz (THz) band is increasingly investigated as promising, which is expected to significantly improve spectrum efficiency, reduce equipment cost and size, and achieve ultra-high speed wireless communication as well as high range resolution radar sensing. Nevertheless, the beamwidth is usually very narrow in THz DFRC system that beam misalignment may happen, which greatly degrades both communication and radar performances. To address this, we develop an adaptive power allocation and beamwidth design framework in the context of THz DFRC system. Firstly, to accurately capture the impact of beamwidth on radar and communication performances, we consider a statistical radar cross section (RCS) fluctuation model, i.e., Swerling I model, then based on this model we derive beam alignment probability, successful ranging probability and successful detection probability that work as the radar performance metrics, as well as data rate that works as the communication performance metric. Secondly, we formulate a sum data rate maximization problem, subject to total transmit power constraint and a minimum successful detection probability by optimizing transmit power and beamwidth. Thirdly, we solve the original non-convex problem by decomposing it into two sub-problems and iteratively optimizing these two sub-problems. Numerical results verify the effectiveness of our work.

Next-generation wireless networks are expected to support delay-sensitive applications such as augmented reality (AR) and virtual reality (VR). In AR/VR applications, computational jobs are offloaded to the edge computing server. Such offloading process requires transmissions of large data packets with low latency. Terahertz (THz) band can provide hundreds of GHz bandwidth and thus is promising in enabling ultra-high speed data transmissions. However, THz transmission distance is limited due to its inherently severe propagation loss. This paper considers to improve THz transmission distance with the help of mobile computing. The key idea is that, if mobile devices can handle partial computational jobs, the data transmission rate requirement is lowered, which in turn helps to improve THz transmission distance. Specifically, we aim to maximize THz transmission distance by jointly optimizing users' job offloading decisions and transmission carrier frequencies. To that end, we first introduce a delay-violation probability to characterize the delay constraints for delay-sensitive services. Based on the derived delay-violation probability, we formulate a THz transmission distance maximization (THz-TDM) problem. This THz-TDM problem is non-convex. To solve this issue, we derive in closed-form the expression of the achievable transmission distance with respect to carrier frequency and data rate. Subsequently, we propose a Hungarian-ADMM based iterative algorithm (HAB) and a low-complexity Rearrangement Inequality-like algorithm (RIL). The proposed RIL involves no iterations but only applies to the case where the edge computing server queue stability constraint is relaxed. Numerical results show that the developed schemes can effectively improve THz transmission distance.

Automatic modulation recognition (AMR) has been considered as an efficient technique for non-cooperative communication and intelligent communication. In this work, we propose a modified transformer-based method for AMR, called frame-wise embedding aided transformer (FEA-T), aiming to extract the global correlation feature of the signal to obtain higher classification accuracy as well as lower time cost. To enhance the global modeling capability of the transformer, we design a frame-wise embedding module (FEM) to aggregate more samples into a token in the embedding stage to generate a more efficient token sequence. We also present the optimal frame length by analyzing the representation ability of each transformer layer for a better trade-off between the speed and the performance. Moreover, we design a novel dual-branch gate linear unit (DB-GLU) scheme for the feed-forward network of the transformer to reduce the model size and enhance the performance. Experimental results on RadioML2018.01A datasets demonstrate that the proposed method outperforms state-of-the-art works in terms of recognition accuracy and running speed.

In wireless communication systems, wireless interference classification (WIC) is considered as one of the most effective technologies to address the challenges brought by electromagnetic interference in military and civilian scenarios. Recently, deep learning (DL) based methods have dominated progress in the field of WIC. However, the most existing methods do not consider the redundancy of input samples, nor have the ability to adaptively allocate computational resources conditioned on the inputs. To this end, we propose time-frequency component-aware convolutional neural network (TFCCNN), and it allows the convolution calculation to be performed only at the locations where time-frequency components or important parts exist in the time-frequency image of interference signals, leading to reduce the superfluous computation. Furthermore, to further reduce the computational complexity, we introduce a novel adaptive forward propagation (AFP) algorithm, and the network can determine the depth of forward propagation according to the difficulty of the sample during inference. Experimental results demonstrate that the proposed method reduces the computational complexity by about 75% when the recognition accuracy is slightly improved compared to the traditional CNNs.

This paper investigates the reliability problem of airborne free-space optical (FSO) communications, and a hybrid FSO/radio frequency (RF) communication system with parallel transmission is proposed, where the data stream is transmitted over both FSO and RF links simultaneously. Further, to combat channel fading, maximal ratio combining is utilized at the receiver for combining received signals from both links. The performances of the proposed system are analytically derived in terms of the outage probability and the average bite-error rate (BER). Numerical results show that the proposed hybrid FSO/RF system with parallel transmission outperforms a single airborne FSO or a single RF link, which provides technical guidance for designing reliable high-speed airborne communication systems.

Wireless interference recognition (WIR) is one of the most indispensable technologies in non-cooperative communication systems. Recently, compared with convolutional networks, Transformers enjoy the ability of extracting global features and have achieved striking performance for WIR. However, the self-attention module in Transformers brings huge computational overhead that hinders deployment on resource-constrained devices. In this letter, we aim to alleviate the problem and propose WIR-Transformer. Specifically, WIR-Transformer introduces the division of regions and independently calculates self-attention in each region, which effectively reduces the complexity. To overcome the bottleneck of information blocking between regions, we propose an information exchange module (IEM). To further reduce the complexity, we introduce a novel patch aggregation module (PAM) to reduce the number of patches and fuse the local information. Experiments demonstrate that the proposed WIR-Transformer achieves higher accuracy as compared to conventional methods for WIR.

In the field of military communications, electromagnetic interference has posed a serious threat to wireless communication systems, and wireless interference recognition (WIR) is considered as one of the most indispensable steps to defend against adversarial attacks for anti-interference communication. Following the success in advancing many disciplines, deep learning (DL) brings revolutionary changes to WIR. However, most conventional DL-based methods only use single domain information of interference signals, such as time domain or frequency domain, resulting in low recognition performance under low interference-to-noise ratio (INR). To this end, we propose to exploit multi-domain information of interference signals to take full advantage of the complementarity between domains for WIR. We propose multi-domain networks (MDN) that consist of convolutional layers and transformers to simultaneously strengthen locality and establish long-range dependencies for extracting the features of each domain information. Additionally, to fuse the extracted features from multiple transformation domains together, we propose novel fusion mechanisms, which can be seamlessly incorporated into the MDN. The experimental results demonstrate that the proposed methods notably boost recognition performance as compared to conventional methods for WIR.

In this paper, we propose a novel maximum distance separable (MDS) code based and reconfigurable intelligent surface (RIS) assisted wireless communication system with orthogonal frequency division multiplexing (OFDM). Specifically, input bits are firstly divided into groups and their MDS codes are utilized to decide the amplitudes and phases of subcarriers. The introduction of the MDS code helps to increase the minimum Hamming distance between symbols and improve on the capability of error detection. Besides, the RIS is adopted to create additional paths between the radio frequency (RF) and the receiver as well as alter the signal phases with derived optimal solution. Benefiting from the strength of the RIS, the proposed system can better overcome multipath fading compared with conventional systems. Simulation results are presented to demonstrate the efficacy of the proposed system in terms of reducing bit error rate (BER) through multipath channels.

Wireless interference identification (WII) is critical for non-cooperative communication systems in both civilian and military scenarios. Recently, deep learning (DL) based WII methods have been proposed with impressive performances. However, these methods did not consider the quantization problem for DL based methods of WII, which is an indispensable process when deploying deep neural networks into hardware units. This paper addresses the problem of training quantized convolutional neural networks (CNNs), with low-precision weights as well as activations for WII. Optimizing a low-bit width network is very challenging due to the non-differentiable quantization function. To mitigate the difficulty of training, we propose three effective approaches. Firstly, we propose to train the quantized network with the guidance of the full-precision counterpart. The quantized network can learn from the full-precision counterpart. Unfortunately, training an extra full-precision network to assist a quantized model is cumbersome and computationally expensive. To this end, we further propose a training mechanism which makes use of the manually designed probability distributions to provide a virtual full-precision counterpart for guiding the training of the quantization network without extra computational cost. Thirdly, to make the gradient back-propagates more easily, we propose novel auxiliary output modules, which can be seamlessly incorporated into the proposed quantization networks. Experimental results validate the effectiveness of the proposed methods. Furthermore, it is shown that training 3-bit and 4-bit precision networks with the proposed methods leads to performance improvement as compared to their full precision counterparts with standard network architectures.

Terahertz (THz) communication is promising as it can enable ultra-wide-band and ultra-high-rate for various emerging communication services. In this letter, we propose to exploit the extreme learning machine (ELM) network based regressor for simple and low-complexity joint channel estimation (CE) and signal detection (SD) for THz-band spatial modulation (THz-SM) communications impaired by hardware imperfections. Computer simulations show the performance superiority of the proposed joint CE/SD scheme when compared with the state-of-the-art schemes, and other machine learning-based ones, including the support vector machine (SVM), deep neural network (DNN) and some variants of ELM. Specifically, we show that its bit error rate (BER) performance approaches to that of the recently derived maximal likelihood (ML) SD. In addition, the robustness of the proposed scheme is validated by considering two types of background impulsive noises.

In this letter, a hybrid multi-domain index modulation scheme based on spreading codes domain and beam domain is proposed. In our design, the information bit stream is divided into two parts: one for covert transmission using the index of spreading code and the other using the directional modulation to improve bit error ratio (BER) performance in the desired direction and to prevent eavesdropping. Then, based on joint boundaries and statistical theory, the average error probabilities of legitimate users and eavesdroppers are derived. Moreover, we analyze the average BER and validate through simulation results that the proposed hybrid multi-domain scheme is capable of achieving better BER performance compared to conventional coded wireless communication systems.

We study a downlink non-orthogonal multi-ple access(NOMA)system,in which a base station(BS)serves a near user and a far user on the same frequency band simultaneously.Due to physical obstacles or heavy shadowing,there is no direct link from the BS to the far user and the near user acts as a cooperative relay for the far user by adopting the simultaneous wireless information and power transfer(SWIPT)technique.In particular,we first derive the outage probabilities of the SWIPT-assisted cooperative NOMA system by considering both full-duplex and half-duplex relaying modes.Then,we analyze the approximated closed-form expression of exact outage probability by applying the Gaussian-Chebyshev quadrature formulas.Simulation results validate the correctness of the theoretical analysis and demonstrate the advantages of the SWIPT-assisted cooperative NOMA system over orthogonal multiple access(OMA)benchmarks.

通信的本质是把各种资源(频谱、时间、空间等)转换为能力(容量、延迟、连接数、可靠性等),因此,为了提升通信能力,我们不仅要利用更多的资源,还要实现这些资源的高效利用.本期专题为"多频段协同通信",旨在探讨频谱资源的挖掘与利用技术.当前单一的频谱资源已经无法满足5G时代的速率需求,因此我们一方面需要寻找新的频谱资源,另一方面应充分挖掘可用频谱的潜力,实现多个频谱的协同通信.国际移动通信(IMT) 2020和IMT 2030推进组均将多频段接入和协同通信技术列为5G、6G的重要核心技术.

Terahertz (THz) communication is considered as an attractive way to overcome the bandwidth bottleneck and satisfy the ever-increasing capacity demand in the future. Due to the high directivity and propagation loss of THz waves, a massive MIMO system using beamforming is envisioned as a promising technology in THz communication to realize high-gain and directional transmission. However, pilots, which are the fundamentals for many beamforming schemes, are challenging to be accurately detected in the THz band owing to the severe propagation loss. In this paper, a unified 3D beam training and tracking procedure is proposed to effectively realize the beamforming in THz communications, by considering the line-of-sight (LoS) propagation. In particular, a novel quadruple-uniform planar array (QUPA) architecture is analyzed to enlarge the signal coverage, increase the beam gain, and reduce the beam squint loss. Then, a new 3D grid-based (GB) beam training is developed with low complexity, including the design of the 3D codebook and training protocol. Finally, a simple yet effective grid-based hybrid (GBH) beam tracking is investigated to support THz beamforming in an efficient manner. The communication framework based on this procedure can dynamically trigger beam training/tracking depending on the real-time quality of service. Numerical results are presented to demonstrate the superiority of our proposed beam training and tracking over the benchmark methods.

Future networks are envisioned to create digital twin representations of the physical world, motivated by the integrated sensing and communication (ISAC). To meet the needs, millimetre wave (mmWave) and terahertz (THz) signals shine with outstanding performance in high-speed transmission and high-accuracy perception, but requiring narrow beamwidth to compensate for high path-loss. It in turn causes severe beam misalignment that induces link failures, leaving reliable inter-cell handovers and intra-cell beam switches crucial challenges for THz/mmWave networks. Exploiting the Synchronization Signal Block (SSB) specified for beam management, we propose a system-level beam alignment scheme utilizing SSB-based sensing to assist beam switches. To fully utilize limited sensing resource, we provide a closed-form optimal SSB time-frequency pattern design that complies with current configuration. Results show that proposed scheme reduces up to 70% beam misalignment probability in highway and urban cases. Thus, guaranteeing the same beam alignment quality, sensing-aided networks can use narrower beams to enlarge network coverage. Also, the optimal resource allocation for SSB pattern design is stable against changing node density and mobility.