ABSTRACT Directional modulation (DM) has been widely studied as a promising technique for enhancing physical layer security (PLS). In particular, DM combined with frequency diverse arrays (FDA) enables directional transmission on the two‐dimensional plane. However, the inevitable presence of sidelobes in FDA patterns still poses a risk of information leakage to eavesdroppers. To address this limitation, we propose an artificial‐noise‐aided DM scheme based on a multilog frequency diverse array (AN‐DM‐MFDA). Unlike conventiona methods that uniformly transmit artificial noise (AN), the proposed scheme strategically allocates AN to the leakage sidelobes, thereby reducing their vulnerability to interception. Furthermore, we derive the theoretical expressions for the average bit error rate (BER) and the secrecy capacity of the developed system. Simulation results confirm that the proposed scheme achieves stronger secrecy protection while maintaining reliable detection performance for the legitimate user, outperforming existing AN‐aided DM approaches.

Constant envelope orthogonal frequency-division multiplexing (CE-OFDM) has attracted increasing attention as a power-efficient modulation scheme for Internet of Things (IoT) networks, particularly in energy-constrained scenarios such as space–air–ground integrated networks (SAGINs). Despite its inherently low peak-to-average power ratio (PAPR), CE-OFDM suffers from significant bit error rate (BER) degradation due to nonlinear phase wrapping caused by phase modulation. To address this limitation, we propose a selective mapping (SLM)-aided CE-OFDM scheme, in which the transmit sequence with the minimum number of phase jumps is selected from a pre-defined set of candidates. This strategy effectively mitigates the impact of phase wrapping and significantly improves BER performance. Furthermore, to eliminate the spectral overhead caused by conventional side information transmission, we propose an embedded side information (ESI) mechanism that seamlessly incorporates index data into the transmitted signal without requiring additional bandwidth. Simulation results verify that the proposed SLM-aided CE-OFDM scheme, equipped with ESI, achieves substantial BER improvements over conventional CE-OFDM systems, while maintaining the low-PAPR property essential for energy-constrained IoT devices.

The space–air–ground integrated network (SAGIN) has emerged as a promising architecture for future wireless communication systems. However, its open and heterogeneous nature introduces significant physical-layer security risks. To overcome the security concerns in SAGIN, we propose a constant-envelope orthogonal frequency division multiplexing with index modulation (CE-OFDM-IM) based transmission framework, where the constant-envelope property ensures compatibility with hardware-constrained SAGIN environments, while the index modulation mechanism inherently supports covert signaling via implicit subcarrier activation patterns. To enable reliable detection at legitimate receivers, we design both optimal and low-complexity receiver architectures, and further introduce a clipping-based technique to suppress phase wrapping and enhance demodulation robustness. Additionally, the average bit error probability (ABEP) is analytically characterized through performance analysis. Finally, simulation results demonstrate that the proposed CE-OFDM-IM system achieves robust transmission with zero peak-to-average power ratio (PAPR), offering a practical and energy-efficient solution for secure communications in SAGIN environments.

The low-altitude economy (LAE), an emerging economic paradigm encompassing various flight activities in low-altitude airspace, has attracted widespread attention from academia and industry due to its appealing economic and social benefits. In this paper, we investigate the design of integrated sensing and communication (ISAC) waveforms for LAE applications. Specifically, we propose an adaptive ISAC waveform, which integrates the maximum distance separable (MDS) code and index modulation (IM) into the orthogonal frequency division multiplexing (OFDM) waveform, namely A-MDS-OFDM-IM. This design combines the hybrid benefits of MDS code, IM, and OFDM techniques, i.e., the error detection capability of MDS code, the high spectral efficiency (SE) of IM, and the high sensing resolution of OFDM, thereby enabling robust communication and sensing. A comprehensive performance analysis of A-MDS-OFDM-IM is provided, including its bit error rate (BER), peak-to-sidelobe level (PSL), and peak-to-average power ratio (PAPR). Moreover, to address the high PAPR issue of A-MDS-OFDM-IM, we develop an adaptive design criterion based on the alternating direction method of multipliers (ADMM), which is capable of jointly optimizing the communication, sensing, and PAPR performance of the proposed system. Simulation results demonstrate that the proposed waveform achieves better BER performance than conventional OFDM-based waveforms under a non-ideal high power amplifier (HPA), owing to its low-PAPR characteristic. Additionally, the proposed waveform ensures robust sensing with satisfactory PSL performance, making it a promising ISAC waveform for LAE applications.

This contribution focuses on the optimization of bit error rate (BER) performance in fluid antenna (FA)-aided wireless communication systems, which leverage the new degrees of freedom provided by positional flexibility, thus surpassing the limitations of conventional fixed-position antenna (FPA) systems. In this context, a theoretical analysis identifies key metrics for maximum likelihood (ML) and zero-forcing (ZF) detectors, specifically the minimum singular value and effective rank, as determinants of system performance. Then, for single-input single-output (SISO) channels, the optimization problem is formulated as channel gain maximization constrained by the predefined moving region and then solved using a mixed-integer linear programming (MILP) model. Furthermore, for multiple-input multiple-output (MIMO) channels, an alternating optimization (AO) algorithm incorporating the Frank-Wolfe method is proposed to optimize ML and ZF performances, targeting minimum singular value maximization for ML and singular value balancing for ZF, both subject to moving region and antenna spacing constraints. Finally, numerical results exhibit significant performance gains for the FA system over its conventional FPA alternative. In general, these findings highlight the potential of FA systems for addressing ultra-reliable communication challenges in the sixth generation (6G) wireless communications.

Recently, the pinching-antenna system (PASS) has emerged as a promising architecture owing to its ability to reconfigure large-scale path loss and signal phase by activating radiation points along a dielectric waveguide. However, existing studies mainly focus on line-shaped PASS architectures, whose limited spatial flexibility constrains their applicability in multiuser and indoor scenarios. In this paper, we propose a novel two-dimensional (2D) pinching-antenna system (2D-PASS) that extends the conventional line-shaped structure into a continuous dielectric waveguide plane, thereby forming a reconfigurable radiating plane capable of dynamic beam adaptation across a 2D spatial domain. An optimization framework is developed to maximize the minimum received signal-to-noise ratio (SNR) among user equipments (UEs) by adaptively adjusting the spatial configuration of pinching antennas (PAs), serving as an analog beamforming mechanism for dynamic spatial control. For the continuous-position scenario, a particle swarm optimization (PSO)-based algorithm is proposed to efficiently explore the nonconvex search space, while a discrete variant is introduced to accommodate practical hardware constraints with limited PA placement resolution. Simulation results demonstrate that the proposed 2D-PASS substantially improves the minimum SNR compared with conventional line-shaped PASS and fixed-position antenna (FPA) benchmarks, while maintaining robustness under varying user distributions and distances.

Recent advances in reconfigurable antennas have led to the new concept of the fluid antenna system (FAS) for shape and position flexibility, as another degree of freedom for wireless communication enhancement. This paper explores the integration of a transmit FAS array for hybrid beamforming (HBF) into a reconfigurable intelligent surface (RIS)-assisted communication architecture for multiuser communications in the downlink, corresponding to the downlink RIS-assisted multiuser multiple-input single-output (MISO) FAS model (Tx RIS-assisted-MISO-FAS). By considering Rician channel fading, we formulate a sum-rate maximization optimization problem to alternately optimize the HBF matrix, the RIS phase-shift matrix, and the FAS position. Due to the strong coupling of multiple optimization variables, the multi-fractional summation in the sum-rate expression, the modulus-1 limitation of analog phase shifters and RIS, and the antenna position variables appearing in the exponent, this problem is highly non-convex, which is addressed through the block coordinate descent (BCD) framework in conjunction with semidefinite relaxation (SDR) and majorization-minimization (MM) methods. To reduce the computational complexity, we then propose a low-complexity grating-lobe (GL)-based telescopic-FA (TFA) system with multiple delicately deployed RISs under the sub-connected HBF architecture and the line-of-sight (LoS)-dominant channel condition, to allow closed-form solutions for the HBF and TFA position. Our simulation results illustrate that the former optimization scheme significantly enhances the achievable rate of the proposed system, while the GL-based TFA scheme also provides a considerable gain over conventional fixed-position antenna (FPA) systems, requiring statistical channel state information (CSI) only and with low computational complexity.

This paper proposes a framework for applying a novel multi-domain modulation scheme, orthogonal chirp division multiplexing with index modulation (OCDM-IM), in integrated sensing and communication (ISAC) systems by combining OCDM and index modulation (IM). To support simple SISO-ISAC applications, a low-complexity fast Fourier transform (FFT)-based sensing algorithm is developed for the sake of the superior performance of OCDM-IM over traditional OCDM. Building on this, the framework is further extended to more attractive MIMO-ISAC systems in order to achieve improved bit error rate (BER) and a lower peak-to-average power ratio (PAPR) compared to the conventional MIMO-OCDM scheme. For sensing, it enables distance, velocity, and angle estimation by formulating the OCDM-IM waveform within a compressed sensing framework, which is then efficiently solved using the proposed orthogonal matching pursuit (OMP) algorithm. Simulation results confirm that the OCDM-IM waveform enhances communication performance through IM while preserving the promising sensing performance.

Integrated Sensing and Communication (ISAC) systems promise to revolutionize wireless networks by concurrently supporting high-resolution sensing and high-performance communication. This paper presents a novel radio access technology (RAT) selection framework that capitalizes on vision sensing from base station (BS) cameras to optimize both communication and perception capabilities within the ISAC system. Our framework strategically employs two distinct RATs, LTE and millimeter wave (mmWave), to enhance system performance. We propose a vision-based user localization method that employs a 3D detection technique to capture the spatial distribution of users within the surrounding environment. This is followed by geometric calculations to accurately determine the state of mmWave communication links between the BS and individual users. Additionally, we integrate the SlowFast model to recognize user activities, facilitating adaptive transmission rate allocation based on observed behaviors. We develop a Deep Deterministic Policy Gradient (DDPG)-based algorithm, utilizing the joint distribution of users and their activities, designed to maximize the total transmission rate for all users through joint RAT selection and precoding optimization, while adhering to constraints on sensing mutual information and minimum transmission rates. Numerical simulation results demonstrate the effectiveness of the proposed framework in dynamically adjusting resource allocation, ensuring high-quality communication under challenging conditions.

Directional modulation (DM) is a physical layer security (PLS) technique implemented at the transmitter, leveraging antenna arrays to ensure secure communications. Through a process of spatial precoding between transceivers to transmit signals in specific directions, DM is capable of disrupting communications in unintended directions to prevent eavesdropping. In general, recent progress in the development of multiple-input multiple-output (MIMO) systems, including advanced radio frequency (RF), antenna technologies, along with innovative precoding algorithms, has enhanced the capabilities of DM techniques, leading to a multitude of robust DM variants. Hence, this survey aims to offer a comprehensive overview of DM, covering its fundamentals, promising variants, applications, hardware implementations, and future trends. Initially, the basic principle of DM is outlined in a general manner for subsequent comprehension. Subsequently, the large family of DM techniques is categorized into distinct variants based on the types of transmitting arrays. Next, we give a comprehensive survey of DM in common wireless scenarios, including multi-user (MU), relay, Internet of Things (IoT), and non-orthogonal access (NOMA) networks. Furthermore, we provide an illustration of DM system implementations, encompassing foundational architectures and cost-effective hardware realizations. Finally, concerning the unresolved challenges and current research focal points in DM, we present future research directions that merit further exploration and reference.

The challenges associated with channel state information (CSI) acquisition in the frequency division duplexing (FDD) extremely large-scale MIMO (XL-MIMO) system significantly impede its application in 6G communications. In such context, this contribution introduces a CSI feedback framework with pilot precoding capitalizing on the partial FDD reciprocity towards significantly enhanced efficiency. Specifically, by exploiting the near-field channel sparsity in the polar-delay domain, the proposed polar-delay sparsity (PDS) codebook remarkably reduces pilot and CSI feedback overhead while maintaining high spectral efficiency (SE). Furthermore, a class of innovative compression and decompression methods are also developed to enable further reduction of training overhead without compromising system performance. Notably, the proposed design ensures consistently low computational complexity and feedback overhead at the user equipment (UE), making it well-suited for the massive connectivity demands of heterogeneous devices in 6G Internet of Everything (IoE) applications. Finally, simulation results demonstrate the satisfactory SE performance enhancement achieved by the proposed schemes.

In pursuit of real-time sensing within ultra-short duration, conventional sensing algorithms are gradually failing to fulfill the stringent latency demands. Specifically, traditional subspace-based methods such as multiple signal classification (MUSIC) are hindered by their need for an extensive number of snapshots to accumulate the rank of the spatial covariance matrix (SCM), resulting in poor real-time performance. Moreover, advanced techniques like compressed sensing and machine learning are constrained by requirements for high signal sparsity or suffer from limited generality. To handle these challenges, this paper proposes an innovative extension of subspace theory tailored to insufficient-snapshot scenarios, leveraging the concept of spatio-temporal exchangeability. Based on the defined spatio-temporal correlation predicated on the space translation invariance characteristic of uniform linear arrays, we engineer a pseudo SCM that inherently possesses sufficient rank. This methodology not only resolves the rank-deficiency issue but also fully exploits the array aperture and significantly reduces the noise level. Simulation results are presented, substantiating the feasibility and enhanced performance of the proposed algorithms, marking a significant advancement over existing methodologies.

As a promising candidate for high-mobility communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention owing to its robustness to Doppler shifts and efficient hardware implementation. In this paper, motivated by the urgent demand for seamless and reliable communications in high-speed railway (HSR) scenarios, we innovatively integrate OCDM into cell-free massive multiple-input multiple-output (CFmMIMO) systems and establish a novel transmission framework, termed CFmMIMO-OCDM. Within this framework, we conduct a comprehensive analysis of the doubly-dispersive HSR channel model and derive the input-output signal relation in HSR communications. Moreover, to address the challenges posed by high computational complexity and excessive data exchange inherent in centralized signal processing, we first reveal the quasi-sparsity of the Fresnel-domain channel matrix in HSR communications. Then, we develop a distributed baseband processing (DBP) architecture by leveraging the channel sparsity. Aimed at enhancing the signal detection efficiency and accuracy, we further design a distributed message passing (DMP)-based detection algorithm for CFmMIMO-OCDM in HSR communications, which achieves considerably reduced complexity and data exchange compared to the centralized detection. Numerical results confirm the superiority of CFmMIMO-OCDM over conventional orthogonal frequency division multiplexing (OFDM)-assisted CFmMIMO systems in HSR communications. Moreover, theoretical analysis and numerical results are provided to demonstrate that our proposed DMP detection can achieve attractive bit error rate (BER) and complexity performance compared to conventional centralized detection.

This paper addresses the problem of pilot contamination in single-cell networks, especially in dense-user areas where intra-cell pilot reuse is inevitable. We utilize fluid antennas (FAs) at the base station (BS) to improve the uplink (UL) channel estimation accuracy in terms of normalized mean square error (NMSE). To this end, we invoke channel spatial correlation as a key metric to characterize the similarity among the spatial structures of users’ channels. Inspired by the fact that the users with low spatial correlation experience less interference, we propose to mitigate pilot contamination by optimizing the FA positions with an innovative objective of minimizing the channel spatial correlation among the pilot-sharing users. To handle the resulting fractional programming problem, we recast it into a more tractable form via establishing upper and lower bounds for the objective function. Subsequently, the reformulated problem is effectively solved through an alternating optimization (AO) framework, where the position of each FA is iteratively optimized exploiting the successive convex approximation (SCA) method. Finally, simulation results validate the effectiveness of the proposed algorithm and demonstrate its remarkable performance in channel estimation and data transmission. In particular, the proposed 16-FA scheme achieves up to 71.2% NMSE reduction over its fixed-position antenna (FPA) counterpart, translating to an approximate 11 dB SNR gain in symbol detection.

In the field of constant envelope orthogonal frequency division multiplexing (CE-OFDM), the use of phase modulation heralds a paradigm with an extremely reduced peak-to-average power ratio (PAPR) of zero. However, the non-linear characteristics inherent to phase modulation pose a formidable challenge to the integration of modern channel coding strategies aimed at improved bit error rate (BER) results. To address this issue, we present an efficient low-complexity log-likelihood ratio (LLR) computation strategy based on the traditional phase receiver, anchored in an effective approximation to the phase noise variance. Furthermore, this foundation paves the way for an innovative receiver architecture, namely the near-maximum likelihood (NML) receiver, designed to improve decoding performance without incurring significant computational overhead. Comparative analyses of encoded CE-OFDM across different receiver models not only confirm the accuracy and superiority of the proposed LLR estimation, but also underscore the dual advantages of our proposed NML receiver in optimising BER and detection complexity, positioning CE-OFDM as an optimal candidate for scenarios requiring high-efficiency power amplifiers (PAs).

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.

As mobile networks progress towards a unified framework for integrated sensing and communication (ISAC), it is foreseeable to introduce new privacy concerns, particularly the potential exposure of position information to unintended receivers. In other words, the scope of physical-layer security (PLS) needs to be expanded to encompass both communication and sensing privacy. Therefore, in contrast to conventional PLS schemes that focus predominantly on preventing eavesdropping, this paper proposes a novel physical-layer privacy (PLP) design within ISAC frameworks, in order to guarantee the secrecy of data transmission while obscuring transmitter’s directional information. Specifically, we introduce a metric termed angular-domain peak-to-average ratio (ADPAR) to assess sensing resistance (SR) performance. Subsequently, three fundamental optimization problems are formulated under such ADPAR constraints to enhance communication secrecy, depending upon the integrity of illegitimate channel state information. These problems are then tackled using advanced strategies such as null-space projection and the cooperation with artificial noise. Additionally, closed-form solutions are further derived in a few specific cases by leveraging singular value decomposition (SVD) and generalized SVD. Finally, simulation results affirm the effectiveness of our design in safeguarding the twofold privacy within ISAC networks.

We consider the problem of max-min beamforming (MMB) for cell-free massive multi-input multi-output (MIMO) systems, where the objective is to maximize the minimum achievable rate among all users. Existing MMB methods are mainly based on deterministic optimization methods, which are computationally inefficient when the problem size grows large. To address this issue, we, in this paper, propose a randomized alternating direction method of multiplier (ADMM) algorithm for large-scale MMB problems. We first propose a novel formulation that transforms the highly challenging feasibility-checking problem into a linearly constrained optimization problem. An efficient randomized ADMM is then developed for solving the linearly constrained problem. Unlike standard ADMM, randomized ADMM only needs to solve a small number of subproblems at each iteration to ensure convergence, thus achieving a substantial complexity reduction. Our theoretical analysis reveals that the proposed algorithm exhibits an$O(1/\bar {t})$convergence rate ($\bar {t}$represents the number of iterations), which is on the same order as its deterministic counterpart. Numerical results show that the proposed algorithm offers a significant complexity advantage over existing methods in solving the MMB problem.

We are interested in multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) communication-based vehicle state detection (VSD) (including vehicle location, velocity and pose angle) in multipath interference scenarios, towards 6G integrated sensing and communications. Yet, communication-based VSD is challenging, since its signals undergo multipath interference and random fading, while channel state and reflector locations are even unknown, in addition to vehicle state. To address these challenges, a novel multipath information fusion-assisted VSD scheme is devised to smartly aggregate geometric knowledge from both direct and reflection paths, thus yielding a robust VSD solution against multipath interference. In addition, we propose to divide the complex VSD problem into four subproblems: (i) angle-of-arrival detection, (ii) time-of-flight estimation, (iii) joint reconstruction of angle-of-departure, radial speed and channel state, and (iv) vehicle-and-reflector state detection. An efficient four-step cascaded VSD method is devised by exploiting linearity, quadratic, orthogonality and space-time-domain correlation of MIMO OFDM signals, which finally achieves simultaneous VSD, reflector positioning and channel estimate. It is verified by simulations that our VSD scheme outperforms state-of-the-art baselines due to our specially-tailored problem decoupling and multipath information fusion, which builds a technical foundation for designing environment sensing-assisted communication strategies.

In this paper, we propose a fluid antenna (FA) enabled joint transmit and receive index modulation (FA-JTR-IM) transmission mechanism for reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication systems. By integrating the methodologies of FA and IM, the proposed scheme achieves enhanced spectral efficiency (SE) while requiring only a single radio frequency (RF) chain at both the transmitter and receiver. The proposed scheme offers a low hardware cost and power consumption transmission mechanism for the RIS-aided mmWave communication systems. Specifically, the encoding of information bits encompasses not only the modulated symbol but also the indices of transmit FA positions and receive antennas. To achieve a reliability-complexity trade-off, two types of detectors are introduced for the proposed FA-JTR-IM scheme, including the optimal maximum likelihood (ML) detector and two-step sequential (TSS) detector. Based on the ML detector, we derive the expression for the conditional pair-wise error probability of the proposed FA-JTR-IM scheme. Additionally, we provide the closed-form expressions for the unconditional PEP under the finite-path and infinite-path channel conditions, respectively. Simulation results demonstrate the superiority of the proposed FA-JTR-IM scheme in terms of error performance over its conventional benchmark schemes under the same SE condition.

In addressing the challenges posed by high-mobility communication channels, orthogonal time frequency space (OTFS) emerges as an efficient waveform, heralding considerable performance enhancements. This work delves into the realm of index modulation (IM)-aided OTFS frameworks, with a spotlight on delay-IM with OTFS (DeIM-OTFS) for its exceptional mitigation of inter-symbol interference. Specifically, we introduce a cutting-edge multi-layer message passing detection (MLMPD) algorithm and its streamlined variant for DeIM-OTFS, capitalizing on genuine a priori information to accelerate convergence and enhance bit error rate (BER) performance. Through meticulous simulation, our MLMPD algorithm demonstrates BER performance improvement while substantially reducing complexity, setting a new benchmark for robust, efficient communication in future-oriented scenarios.

In smart healthcare, health monitoring utilizes diverse tools and technologies to analyze patients' real-time biosignal data, enabling immediate actions and interventions. Existing monitoring approaches were designed on the premise that medical devices track several health metrics concurrently, tailored to their designated functional scope. This means that they report all relevant health values within that scope, which can result in excess resource use and the gathering of extraneous data due to monitoring irrelevant health metrics. In this context, we propose a Dynamic Activity-Aware Health Monitoring strategy (DActAHM), as a novel framework based on Deep Reinforcement Learning (DRL) and SlowFast Model, for striking a balance between optimal monitoring performance and cost efficiency while ensuring precise monitoring based on users' activities. Specifically, with the SlowFast Model, DActAHM efficiently identifies individual activities and captures these results for enhanced processing. Subsequently, DActAHM refines health metric monitoring in response to the identified activity by incorporating a DRL framework. Extensive experiments comparing DActAHM against three state-of-the-art approaches demonstrate it achieves 27.3% higher gain than the best-performing baseline that fixes monitoring actions over timeline.

In this paper, a novel design of amplitude phase shift keying-aided space-time block coded differential spatial modu-lation (APSK-STBC-DSM) is proposed. This design conceived not only maintains the diversity benefits of the space-time block coded differential spatial modulation (STBC-DSM) system, but also conveys extra information bits with the aid of amplitude phase shift keying (APSK), so as to enhance the transmission rate. Moreover, an improved low-complexity detector is also proposed, in order to achieve near-optimal detection performance. Ultimately, simulation results exhibit that the proposed APSK-STBC-DSM system is capable of providing significant bit error rate (BER) performance gains over conventional STBC-DSM and other differential spatial modulation (DSM) counterparts.

As one of the recently proposed attractive multi-carrier waveforms towards high-mobility scenarios for 6G and beyond, the chirp-based affine frequency division multiplexing (AFDM) is capable of adapting to cope with large Doppler frequency shifts, which drastically deteriorates the orthogonality between orthogonal frequency division multiplexing (OFDM) subcarriers. Under doubly selective channels with given delay-Doppler profiles, existing works have proved that the classic AFDM requires a minimum number of subcarriers (such as the number of subcarriers is no less than 6, i.e.,$N\geq 6$) to achieve its full diversity. In this paper, we propose a novel optimized waveform design based on AFDM by combing the concept of index modulation (IM) and repetition coding (RC) in order to achieve additional diversity gain for the case that fewer subcarriers are available, and the proposed scheme is termed as index modulation-repetition coding-aided AFDM (IMRC-AFDM), where the main design idea is to perform the RC to complex-valued symbols to achieve diversity gain, while the transmission rate loss resulting from RC is compensated by exploiting the benefits of IM. Simulations results validate the benefits of our IMRC-AFDM scheme and show the performance gain of the proposed scheme over the original plain scheme.

In recent years, the amalgamation of satellite communications and aerial platforms into space-air-ground integrated network (SAGINs) has emerged as an indispensable area of research for future communications due to the global coverage capacity of low Earth orbit (LEO) satellites and the flexible Deployment of aerial platforms. This paper presents a deep reinforcement learning (DRL)-based approach for the joint optimization of offloading and resource allocation in hybrid cloud and multi-access edge computing (MEC) scenarios within SAGINs. The proposed system considers the presence of multiple satellites, clouds and unmanned aerial vehicles (UAVs). The multiple tasks from ground users are modeled as directed acyclic graphs (DAGs). With the goal of reducing energy consumption and latency in MEC, we propose a novel multi-agent algorithm based on DRL that optimizes both the offloading strategy and the allocation of resources in the MEC infrastructure within SAGIN. A hybrid action algorithm is utilized to address the challenge of hybrid continuous and discrete action space in the proposed problems, and a decision-assisted DRL method is adopted to reduce the impact of unavailable actions in the training process of DRL. Through extensive simulations, the results demonstrate the efficacy of the proposed learning-based scheme, the proposed approach consistently outperforms benchmark schemes, highlighting its superior performance and potential for practical applications.

With the expansive deployment of ground base stations, low Earth orbit (LEO) satellites, and aerial platforms such as unmanned aerial vehicles (UAVs) and high altitude platforms (HAPs), the concept of space-air-ground integrated network (SAGIN) has emerged as a promising architecture for future 6G wireless systems. In general, SAGIN aims to amalgamate terrestrial nodes, aerial platforms, and satellites to enhance global coverage and ensure seamless connectivity. Moreover, beyond mere communication functionality, computing capability is increasingly recognized as a critical attribute of sixth generation (6G) networks. To address this, integrated communication and computing have recently been advocated as a viable approach. Additionally, to overcome the technical challenges of complicated systems such as high mobility, unbalanced traffics, limited resources, and various demands in communication and computing among different network segments, various solutions have been introduced recently. Consequently, this paper offers a comprehensive survey of the technological advances in communication and computing within SAGIN for 6G, including system architecture, network characteristics, general communication, and computing technologies. Subsequently, we summarize the pivotal technologies of SAGIN-enabled 6G, including the physical layer, medium access control (MAC) layer, and network layer. Finally, we explore the technical challenges and future trends in this field.

Orthogonal chirp division multiplexing (OCDM) is a new multi-carrier scheme based on chirp spread spectrum (CSS) recently introduced and shown to be more robust to interference. In this paper, we propose a novel OCDM system based on index modulation (IM). In this scheme, information is conveyed not only byM-ary signal constellation in classic OCDM, but also by subchirp indices activated in accordance with the input bitstream. We design a receiver structure based on single-tap frequency domain equalization (FDE) and maximum likelihood (ML) detection. To address the exponential complexity growth caused by ML detection, we also propose a novel reduced-complexity maximum likelihood (RC-ML) detector. The new detector offers a comparable BER performance to the ML one with a substantially reduced complexity. A theoretical peak-to-average power ratio (PAPR) performance analysis of the new scheme is given to illustrate the advantages of combining OCDM with IM. We provide an extensive performance analysis of the new scheme in terms of bit error rate (BER), diversity gain, and minimum Euclidean distance (MED). Simulation results are presented to demonstrate that the PAPR and BER performances of the proposed scheme are significantly better than those of the OCDM scheme due to the information bits carried by the OCDM subchirp indices. Moreover, our numerical results verify the robustness of the system in the presence of carrier frequency offsets (CFO).

Recently, the reconfigurable intelligent surface (RIS), capable of adjusting the phase shifts (PSs) of the reflecting signals through its low-cost elements, has emerged as a promising technology for next-generation wireless communications. However, the RIS may be manipulated by an illegal passive attacker (Wyn) due to the shared nature of wireless channels. In this paper, a Wyn-controlled RIS is considered to attack multiple-input single-output (MISO) communications via passive beamforming based on existing localization and Rician factor estimation techniques. Specifically, we propose an alignment cancellation (AC) scheme to minimize the achievable rate (AR), where the closed-form expressions for location, reflecting element number, and PSs are derived. Furthermore, the computational complexity is quantified to evaluate the low-cost characteristics of this algorithm. Simulation results demonstrate that the proposed AC scheme outperforms other benchmark schemes in degrading the AR with efficient and low-complexity designs.

This paper investigates the issue of resource allocation for unmanned aerial vehicle and reconfigurable intelligent surface (UAV-RIS) assisted vehicular communication systems. To adapt the high dynamics of vehicular networks, we conceive a digital twin-based system over RIS-embedded environment towards environmental-aware communications. Specifically, a digital twin system can leverage data-driven models to predict the large-scale fading of future stages, while RIS is capable of controlling the propagation environments in real time, which can be utilized to mitigate prediction errors imposed by the small-scale fading. Using the capabilities of “prediction" and “reconfiguration", we expect to comprehensively foresee the dynamic changes in vehicular networks. In particular, the above-mentioned issue is formulated as a multi-slot total power consumption minimization problem under the quality of service (QoS) and energy constraints. Considering the finite battery energy of the UAV and the circuit power of the RIS, the transmit power of the UAV and the number of active reflecting elements (REs) are jointly scheduled for a finite time horizon. To tackle this mixed integer non-linear programming (MINLP) problem, we transform the original model into a discrete-time dynamic system. According to whether the dynamics of radio environments are predictable or not, the optimal offline and online policies are derived by using the deterministic and stochastic dynamic programming algorithms, respectively. To reduce the computation complexity, we further propose a novel online policy based on the idea of double-strategy selection. Finally, numerical results demonstrate that the proposed online policy exhibits near-optimal performances and outperforms other benchmarks in terms of transmission failure probability and effective power consumption.

In this paper, we study an unmanned aerial vehicle (UAV) assisted data collection (DC) system and consider the interference caused by malicious nodes. To ensure sustainable communication, a bi-objective optimization problem is proposed to jointly optimize the maximization of the total data throughput and the minimization of the UAV energy consumption. To this end, a proximal policy optimization (PPO) based framework is proposed. Specifically, under a realistic probabilistic line-of-sight (LoS) channel model, we derive the maximum achievable data transmission rate under interference and express its lower bound explicitly, considering coverage and sensing constraints. In order to tackle the non-convex dilemma and optimize communication throughput and energy utilization, the issue at hand is conceptualized as a Markov decision process (MDP) and a multidimensional reward function is set. Extensive numerical simulations demonstrate the effectiveness of the proposed frame-work in handling the data transmission task while reducing overall network energy consumption. The results indicate a total throughput improvement of 19.7% and a reduction of 46.3% in energy consumption compared to the baseline algorithm.

This paper investigates a deep learning-based algorithm to optimize the unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-aided cell-free (CF) hybrid non-orthogonal multiple-access (NOMA)/orthogonal multiple-access (OMA) networks. The practical RIS reflection model and user grouping optimization are considered in the proposed network. A double cascade correlation network (DCCN) is proposed to optimize the RIS reflection coefficients, and based on the results from DCCN, an inverse-variance deep reinforcement learning (IV-DRL) algorithm is introduced to address the UAV trajectory optimization problem. Simulation results show that the proposed algorithms significantly improve the performance in UAV-RIS-assisted CF networks.

Constant envelope orthogonal frequency division multiplexing (CE-OFDM) has been extensively studied due to its ability to achieve low peak-to-average power ratio (PAPR) waveforms. However, CE-OFDM may encounter the phase ambiguity problem when the modulation index is relatively large. To mitigate this issue, we incorporate companding technique into CE-OFDM successfully enhance the overall system performance. Moreover, companding scheme requires a balanced consideration between the degree of signal compression and phase ambiguity problem. Therefore, this paper derives the optimal parameter selection by analysing the signal-to-interference-plus-noise ratio (SINR) of the scheme. Simulation results demonstrate that the proposed approach enhances the performance of CE-OFDM effectively by reducing the phase ambiguity as well as minimizing the interference caused by the companding technique.

Polarization modulation (PM) is a recently developed multiple-input multiple-output (MIMO) toward low-cost implementation by exploiting the polarization degree of freedom in an efficient way. In the context of a PM transmitter, an improved expectation propagation (EP) detector is proposed in this contribution, while the bit-error rate (BER) performance and computational complexity are both evaluated and quantified respectively. We demonstrate that the proposed detector outperforms its original EP-based counterpart and traditional linear minimum mean square error (LMMSE) detector as a promising receiver design for PM systems.

Space-time line code (STLC) constitutes an attractive multiple-input multiple-output (MIMO) transmission scheme conceived for full diversity gain, while reducing the complexity of the receiver substantially. In this paper, we investigate the influence of imperfect channel estimation to STLC when the channel is modeled by Rayleigh and Rician fading. Furthermore, based on the derived the signal-to-interference-plus-noise ratio (SINR), both the theoretical bit-error-rate (BER) bound and the outage probability of STLC are quantified subject to channel estimation errors. The numerical results finally confirm the reliability of the theoretical analysis.

Channel estimation for orthogonal frequency division multiplexing (OFDM) systems stands for an important issue in mobile broadband communications, where a class of methods based on training symbols, also known as pilots, have been widely studied. Aiming at improving the spectrum efficiency and reducing the transmission latency, we tend to approach the minimal pilot overhead for OFDM systems by proposing a two-stage channel estimation method. Specifically, in the first stage, the initial value of the channel frequency response is obtained by the discrete Fourier transform (DFT)-based estimator. Then, in the second stage, soft-decision is performed to select the components with high confidence, and thus the complete channel estimation value is reconstructed by employing the compressed sensing technology. Simulations show that the proposed scheme yields considerable improvements in both bit error rate (BER) performance and data transmission efficiency.

As healthcare becomes increasingly data-driven, integrating hybrid mobile edge-quantum computing (MEQC) into smart healthcare systems emerges as a promising solution for handling growing computational demand, especially for latency-sensitive tasks. Therefore, this paper proposes a deep reinforcement learning (DRL)-based Lyapunov approach for schedule computation offloading, aiming to minimize the total latency in hybrid MEQC-based smart healthcare systems. In this framework, a sustainable computation offloading strategy is obtained while guaranteeing the individual latency constraints and the required success ratio for each computation task. More precisely, the original latency minimization problem is transformed into a stepwise mixed-integer non-convex optimization problem using Lyapunov techniques. Subsequently, a Deep Q-Network (DQN) is adopted for computation offloading mode selection. The effectiveness of the proposed approach and its dependency on various system parameters are validated and assessed through numerical simulations.

In this contribution, the system of quasi-orthogonal space-time block coded spatial modulation (QOSTBC-SM) with minimum decoding complexity (MDC) is proposed toward linear detection complexity by utilizing the corresponding single-symbol decoding algorithm at the receiver. In addition, we compare the performance of the MDC-QOSTBC-SM system with different decoding algorithms in conjunction with comparing this scheme with space-time block coded spatial modulation (STBCSM). Furthermore, we extend it to uplink multi-user multiple-input multiple-output (MIMO) communications and compare the performance with STBC-SM in the context of quasi-static flat Rayleigh fading channels. The simulation results indicate that the proposed scheme achieves a significant reduction in decoding complexity at the cost of a slight performance loss when employing the single-symbol decoding algorithm.

To enable secure and effective downlink multiple-input multiple-output (MIMO) transmission, it has recently been proposed to integrate space-time block code (STBC) with spatial modulation (SM) and artificial noise (AN). However, this technique suffers from high detection complexity at the receiver side. In this contribution, therefore, we present an AN-assisted transmission scheme for the quasi-orthogonal space–time block coded receive spatial modulation (QOSTBC-RSM) system while developing two low-complexity detection algorithms. Our simulation results demonstrate that the proposed scheme and the corresponding detection methods are capable of guaranteeing safe transmission and achieving near-optimal bit error rate (BER) performance while significantly reducing computational overhead.

Orthogonal Time-Frequency-Space (OTFS) modulation has demonstrated superior performances to extend coverage for high-mobility communicatoins. In this paper, we propose a novel OTFS scheme featuring interference self-cancellation achievable with nearly linear complexity. We first apply the repetitive precoding in delay-Doppler domain, and performing negative padding in the time-frequency domain. Additionally, we formulate equivalent coefficients for the TF domain channel to obtain combined gain with point division equalization. Through numerical simulation, compared with the unprocessed OTFS system, the proposed scheme can achieve a significant performance improvement under TF domain point equalization. Finally, through the analysis of computational complexity, our proposed scheme can realize the transceivers design and equalization with approximately linear complexity.

Abstract Offset spatial modulation (OSM) is an attractive variant in the family of spatial modulation technology, which is capable of reducing the frequency of radio frequency chain switching in practical implementations. This paper elaborates on the benefits of adaptive power allocation (PA) conceived for OSM systems. First, a closed‐form solution of the optimal PA algorithm is derived for the case of two transmit antennas, which maximizes the minimum Euclidean distance (ED) between the received signal points. Moreover, based on the above solutions, a low‐complexity iterative PA algorithm is proposed for larger numbers of transmit antennas, by exploiting the EDs of a few dominant error vectors. Furthermore, for striking a flexible trade‐off between the bit error performance and complexity, an iterative convex approximation based PA algorithm is also developed. Finally, simulation results are presented to show that the proposed PA algorithms provide considerable improvements on the original OSM in terms of the bit error rate.

In smart ocean Internet of Things (IoT) systems, autonomous underwater vehicles (AUVs) are responsible for underwater information collection. Due to the nature of the medium, the acoustic communications for AUVs are of low bandwidth and adverse environmental conditions causing severe transmission problems. In order to realize cross-media transmission from AUVs to the offshore platform, unmanned surface vehicles (USVs) have been suggested to forward the collected information in a coordinated manner. Against this backdrop, this contribution develops a cooperative USV-to-USV (U2U) cross-media cooperative communications scheme. Then, we formulate a rate maximization problem with the objective of optimizing the reputation-aware USV selection strategy. Furthermore, to characterize the impact of the mobility of AUVs/USVs, a long-term dynamic process is constructed. Meanwhile, we also develop an efficient algorithm which transforms the reputation-aided dynamic USVs selection problem into the infinite-time horizon average one restricted by time average rate constraints in the collection of penalty processes with the help of the Lyapunov optimization framework and drift-plus-penalty method. Finally, numerical results are presented to validate the convergence behavior and the performance for the designed dynamic USVs selection algorithm.

As a revolutionary technology for future wireless communications, reconfigurable intelligent surface (RIS), characterized by an efficient way of manipulating wireless signals, has been widely investigated in recent years toward enhancing signal quality, energy efficiency, throughput, and so on. However, in RIS-assisted Internet of Things (IoT), a new issue as multipath separation emerges, especially, when deploying multiple RISs to assist communication, since the devices may have limited signal processing capabilities. For alleviating this problem, we conceive a novel RIS-enabled code-division multiple access (CDMA) structure, where each RIS holds a specified time-varying coefficient to tag the channel. Moreover, multipath extraction is further considered, including a practical channel estimation approach along with theoretical derivations in terms of Cramér–Rao lower bound, mean-square error, as well as ergodic channel capacity. Simulation results corroborate the feasibility of the conceived RIS-CDMA structure and the effectiveness of the proposed multipath extraction approach.

In this paper, we investigate an intelligent reflecting surface (IRS)-assisted wireless powered Internet of Things (WP-IoT) network that operates in multiple resource blocks (RBs). Particularly, the IRS helps in both downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT), in a way that it improves energy reflection in WET from a power station (PS) to various IoT devices and boosts information delivery in WIT from the IoT devices to an access point (AP). Those IoT devices are capable of utilizing the collected energy, and adopting the time-division multiple access (TDMA) or non-orthogonal multiple access (NOMA) scheme in the uplink WIT. Aiming to maximize the average throughput as the overall performance indicator of the considered network, we jointly optimize the transmit power allocation of the PS, the time scheduling, and the IRS phase shifts. These coupled variables lead to the non-convexity of this optimization problem, which cannot be solved directly. To address this problem, we first design the optimal PS’s transmit power allocation for each RB. For the TDMA-based scheme, we design the closed-form IRS beam pattern of the uplink WIT. Then, the closed-form downlink and uplink time allocations are derived by the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions. In addition, the quadratic transformation (QT)-based Alternating Direction Method of Multipliers (ADMM) approach is proposed to iteratively derive the sub-optimal IRS beam pattern of the downlink WET in an alternated fashion. For the NOMA-based scheme, we propose to apply an alternating optimization (AO) algorithm to iteratively optimize the IRS phase shifts, where the uplink IRS beam pattern is iteratively designed by the Riemannian Manifold Optimization (RMO) approach, and the QT-based ADMM method is adopted to alternately derive the sub-optimal downlink IRS phase shifts. Finally, numerical results demonstrate the improved performance of the proposed solution approaches compared to the benchmark schemes, also highlight advantages of the application of IRS in multiple RB scenarios.

In this contribution, the concepts of polarization modulation (PM) and spatial modulation (SM) are integrated, to reap their respective advantages toward single-radio frequency (RF) multiple-input multiple-output (MIMO) transmissions. In the so-called polarization and spatial modulation (PSM) system, the information is conveyed by activated antenna indices as well as polarization modulated symbols, while at the receiver, a low-complexity near-optimal detection algorithm based on compressive sensing (CS) is proposed. Furthermore, a closed-form union bound of the average bit-error rate (BER) over fading channels is quantified by theoretical derivation, which is then extended to the case of spatial correlation (SC) as well as channel estimation error (CSE) toward practical use. Finally, our simulation results demonstrate the superiority of the developed PSM system over conventional PM in terms of BER performance over various fading channels.

This paper proposes a multi-cluster wireless powered Internet of Things (WP-IoT) network assisted by multiple intelligent reflecting surfaces (multi-IRS). In this network, a power station (PS) first broadcasts wireless energy to the distributed IoT devices grouped into multiple clusters. The IoT devices then use the harvested energy to convey their information to an access point (AP), based on a hybrid time- and frequency-division multiple access (TDMA-FDMA) protocol. Furthermore, multiple IRSs are deployed to perform anomalous reflection for energy and information transfer, to improve energy harvesting and data transmission capabilities. Under the constraints of the unit-modulus phase shifts, the transmission time shared among clusters and the bandwidth shared by the devices in each cluster, the considered system is optimized by maximizing its sum throughput. The optimization problem is non-convex and with complicatedly coupled variables. To solve this problem, we propose to first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission scheduling and bandwidth allocation, then the quadratic transformation (QT) and the alternating optimization (AO) algorithm are introduced to solve the downlink and uplink IRS phase shifts, whilst the Majorization-Minimization (MM) and Riemannian Manifold Optimization (RMO) methods are applied to iteratively derive their closed-form solutions. Additionally, we provide a benchmark scheme to facilitate the system design, where each IRS can control its “on/off” state to aid the downlink and uplink transmissions in the condition of at most one activated IRS during one certain time duration. Finally, simulation results are presented to verify the optimality of our proposed scheme and highlight the beneficial role of the IRS.

Recently, communications with the aid of reconfigurable intelligent surface (RIS), which operates with the aim of enhancing the system communication performance, have aroused extensive researches. Furthermore, the use of RIS for positioning has been considered. Therefore, we focus on a practical structure of indoor positioning assisted by distributed RISs through utilizing their ability to manipulate multipath signals, through the developed quasi-static and dynamic modes. Specifically, in the quasi-static mode, for reducing the implementation cost, the reflection coefficients for each RIS are preset and remain constant. In the dynamic mode, the reflection coefficients can be timely updated with a two-step positioning approach toward more accurate positioning performance. Furthermore, the Cramér-Rao lower bound of the developed positioning scheme is quantified through theoretical analysis. Both theoretical analysis and simulation results demonstrate that RIS has the potential to realize accurate positioning even with a single access point, due to its ability to mark the channel and replace traditional active positioning anchors. Meanwhile, we also show that the developed two-step positioning scheme can achieve considerable performance gain in accurate positioning.

This work poses a distributed multi-resource allocation scheme for minimizing the weighted sum of latency and energy consumption in the on-device distributed federated learning (FL) system. Each mobile device in the system engages the model training process within the specified area and allocates its computation and communication resources for deriving and uploading parameters, respectively, to minimize the objective of system subject to the computation/communication budget and a target latency requirement. In particular, mobile devices are connect via wireless TCP/IP architectures. Exploiting the optimization problem structure, the problem can be decomposed to two convex sub-problems. Drawing on the Lagrangian dual and harmony search techniques, we characterize the global optimal solution by the closed-form solutions to all sub-problems, which give qualitative insights to multi-resource tradeoff. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm.

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.

Recently, reconfigurable intelligent surface (RIS) has arisen as an excellent technology for assisting wireless communications. In order to handle the intractable non-convex problem for jointly optimizing beamforming and PSs in multiple-input multiple-output (MIMO) transmission, we propose a novel alternating direction (AD) method by maximizing the achievable rate (AR) at the receiver. Specifically, the initial problem is divided into the following two processes: i) optimizing the beamforming vector with fixed PSs, ii) determining a specific PS based on a closed-form solution when the other PSs and beamforming are fixed. Simulation results corroborate that the proposed AD method provides robust attainable performance with reduced computational complexity compared to its traditional counterparts.

Millimeter wave (mmWave) band communication has attracted extensive attention for high-data-rate transmission at the cost of increasing distortion from the degraded power efficiency of high power amplifier (HPA). Constant envelope orthogonal frequency division multiplexing OFDM (CE-OFDM) technique is capable of obtaining a 0dB peak-to-average-power-ratio (PAPR) signal and better resistance to phase noise in a multipath channel. However, current CE-OFDM suffers from the tradeoff between bit error rate (BER) performance and receiving complexity. This paper proposes an offset CE-OFDM (OCE-OFDM) scheme, which improves the carrier to noise ratio of subcarriers at higher frequency by adjusting the real component of the signal. Both theoretical and computer simulations make a comparison between the proposed OCEOFDM and conventional CE-OFDM, and demonstrate that the proposed scheme realizes a better BER performance using the low-complexity receiver in both AWGN and 60GHz mmWave channels with nonlinear HPA.

Constant envelope orthogonal frequency division multiplexing (CE-OFDM) has been proposed to overcome the primary drawback as high peak-to-average power ratio (PAPR) in original OFDM at the cost of considerable out-of-band emission. The novelty of this contribution is that the concept of N-continuous signaling is integrated with CE-OFDM in order to suppress the sidelobe. More specifically, the scheme advocated is capable of enhancing the continuity between adjacent CE-OFDM symbols and their higher order derivatives. Moreover, the related signal-to-interference-plus-noise ratio (SINR) of the proposed scheme is analyzed in this paper. The performance results show that the sidelobe suppression can be achieved with slight cost of bit-error rate (BER).

This contribution studies adaptive power allocation of low-density parity check-code (LDPC) coded modulation on fading channels, where in this case, the symbols within a codeword suffer from different signal-to-noise ratios (SNRs). We consider adjusting the transmission power for each symbol within the codeword so as to improve the transmission performance, through an optimization based on Gaussian approximation. Specially, we first give the expression of the optimization problem, and further propose the solution based on Lagrangian method. Numerical results demonstrate the performance gain brought by the proposed power allocation method.

In this paper, we investigate the stability and effectiveness of intelligent reflecting surface (IRS) aided systems in the context of mobile multi-users and time-varying channel status. Different from the previous researches in the IRS-aided communication mostly based on one or more independent channel realization, we consider dynamic channel status varying with the mobility of users. Specifically, a dynamic problem as maximizing the time-average rate of all users is formulated. A fractional programming method based on Lagrangian dual theory is proposed as a solution. Simulation results demonstrate that the IRS can be more efficient than amplified forward (AF) relay in adapting the dynamically changing channels stably.

Offset spatial modulation orthogonal frequency division multiplexing (OSM-OFDM) is demonstrated to offer a simple radio frequency (RF) switching structure with comparable bit error rate (BER) performance to conventional SM-OFDM. However, the OSM-OFDM waveform inherits the large peak-to-average power ratio (PAPR) of original OFDM, and suffers performance loss in terms of BER due to the nonlinear noise from high power amplifier (HPA). As a class of efficient PAPR reduction techniques, the BER performance of clipping-aided OSM-OFDM is first evaluated through theoretical analysis. Furthermore, in this contribution, we conceive a new antenna offset structure as grouped OSM-OFDM toward low PAPR, while its upper BER bound is quantified through theoretical analysis. Through simulation results, we demonstrate that compared to the original OSM-OFDM, both grouped OSM-OFDM and clipped OSM-OFDM are capable of striking a better balance between PAPR and BER performance, while the former one is without extra computational complexity and the later one needs distortion recovery at the receiver side.

In this contribution, we consider the construction of the communication link between two terminals working on different media as underwater acoustics and traditional microwave, with the aid of a cross-media bidirectional relay. Our goal is to develop an optimal power allocation algorithm in order to minimize the outage probability. Through theoretical analysis and simulation results, we demonstrate the effectiveness of the above-mentioned cross-media structure and power optimization scheme, so as to adapt the scenario of hybrid underwater acoustic communication (UAC) and radio frequency (RF) transmission.

Artificial noise elimination (ANE) has arisen as a possible countermeasure for mitigating the influence of artificial noise (AN) at the eavesdropper (Eve). However, conventional ANE schemes require the attainable channel state information (CSI) between the transmitter (Alice) and legitimate receiver (Bob), which reduces the feasibility of this proposal. In this paper, we investigate the issue of ANE without the CSI of Alice-Bob link by minimizing the artificial-noise-to-signal ratio (ANSR). Moreover, the detailed minor component analysis (MCA) algorithm is presented, and the computational complexity is quantified. Simulation results demonstrate that MCA can effectively degrade the influence of AN without the knowledge of CSI.

The concept of unsourced multiple access (UMAC) has been developed toward the massive access scenario in future wireless communications. Recently, a basic framework of UMAC has been proposed where every message is encoded by polar codes. In this contribution, we extend the utilization of polarization-adjusted convolutional (PAC) codes into the aforementioned framework, in order to reap its advantage in excellent error performance in the context of short block length. The performance comparisons of the above mentioned schemes demonstrate that the new structure of UMAC with PAC codes is capable of exhibiting improved error performance with reducing complexity when compared to that with polar codes.

With the evolution of mobile terminals and the development of user demands, the existing mobile system is facing with serious challenges, such as the nearly saturated spectrum, limited processing capability of mobile terminals, and the high complexity of emerging technologies. To deal with these challenges, 6G is attracting extensive attentions. In this paper, we firstly investigate the potential technologies for 6G from the view of network design and resource management, then highlight the potential challenges for resource allocation, imposed for satisfying multi-demands in future hyper-heterogeneous networks with massive different devices and applications. In addition, the existing criteria for resource allocation are summarized for their common forms and limitations when dealing with multi-demands. Considering the limitations of the existing criteria, a novel quality-of-experience (QoE)-aware criterion on demand side is introduced toward efficient resource allocation in 6G, including its modeling, the potential application forms and typical scenarios, the future directions and challenges.

Orthogonal time frequency space (OTFS) is a new multi-carrier modulation technology emerging in recent years. Like orthogonal frequency division multiplexing (OFDM), OTFS also has the problem of high peak to average power ratio (PAPR). Because of the high PAPR, OTFS signals are easy to enter the nonlinear region of the power amplifier (PA), and result in nonlinear distortion. In this paper, we study the PAPR problem for OTFS system and propose an improved algorithm by jointly exploiting the traditional selective mapping (SLM) scheme and the imperialist competition algorithm (ICA), namely ICA-SLM. The simulation results shown that the proposed novel PAPR reduction method is capable of achieving better performance compared to conventional SLM for OTFS systems.

This paper explores the application of spatial modulation to a cross-media communication system. Specifically, based on the theoretical expression of the mutual information of spatial modulation, an optimization model to maximize the minimum mutual information in the uplink and downlink of the relay is proposed, along with a developed power allocation (PA) scheme. In addition, the simulation is operated under two cross-media communication scenarios to confirm the superiority and robustness of the proposed PA scheme. Finally, the simulation results confirm that the proposed PA scheme has better mutual information performance than that of the average PA scheme.

In this contribution, for enhancing the secure transmission of space-time block coded spatial modulation (STBCSM) systems, we conceive a minimum Euclidean distance (ED) transmit antenna selection (MED-TAS) method in conjunction with a new artificial noise (AN) design based on the optimized ED criterion (ED-AN). Specifically, we select the antennas by taking both legitimate receiver and eavesdropper into consideration, while proposing optimized ED-AN by minimizing the ED between the two specified signals, in order to enhance the performance at the legitimate receiver while degrading that at the eavesdropper. Simulation study demonstrates that the proposed technique is capable of achieving better security performance compared to its counterpart as conventional AN-aided STBC-SM design.

Orthogonal time frequency space (OTFS) is a two-dimensional modulation scheme designed in the delay-Doppler domain, for combating the high mobility environment toward future wireless communications. In this paper, we propose a novel generalized spatial modulation combined OTFS (GSMOTFS) system, aiming at achieving better system performance compared to the conventional multiple-input multiple-output OTFS (MIMO-OTFS) and GSM orthogonal frequency division multiplexing (GSM-OFDM) systems. Furthermore, a decision feedback detector based on the minimum mean square error (MMSE) criterion is also developed toward efficient signal detection for the proposed GSM-OTFS system.

Abstract Recently, the physical limitation of range‐domain security guarantee for directional modulation with frequency diverse array was disclosed in Ding et al. ( IEEE Access 8, 63302–63309 (2020)). Therefore, to recreate this significant secrecy realisation in both direction and range domain, the authors conceive an artificial noise aided directional modulation scheme via reconfigurable intelligent surface. Specifically, the angle of departure from the reconfigurable intelligent surface to co‐direction receivers varies with distance, which provides the freedom to distinguish users in the range domain. With locations of the active eavesdroppers, an optimisation problem is further formulated to maximise the secrecy rate, which is then solved by applying genetic algorithm and alternating optimisation. On the other hand, when the eavesdroppers are silent, we employ maximum‐ratio transmission precoding and artificial noise to guarantee information security. Finally, simulation results demonstrate that, due to the aid of reconfigurable intelligent surface, the developed directional modulation structure can robustly guarantee the information security in the range domain, whether the information of eavesdroppers is achieved.

Abstract The hybrid visible light communication (VLC)/radio frequency (RF) communications are investigated with the aid of reconfigurable intelligent surfaces (RISs) in dynamic wireless networks, where the RIS access selection processes of VLC/RF users are updated depending on the channel quality dynamically. Specifically, a dynamic optimization problem due to the mobility of users and time‐varying selection strategy is formulated. Under the constraints of the average minimum rate for VLC/RF users and the maximum transmit power constraints for VLC/RF access points (APs), the target is to minimize the average long‐term power consumption, by jointly considering the active beamforming at APs and the passive beamforming at RISs. Based on the Lyapunov optimization framework and the drift‐plus‐penalty (DPP) algorithm, the original optimization problem is transformed into corresponding short‐term problems at each frame. Furthermore, the closed form solutions with active‐passive beamforming are derived using the fractional programming method based on the Lagrangian dual theory. Finally, numerical results demonstrate the convergence and effectiveness of the proposed optimization algorithm.

Abstract The design of dynamic wireless networks assisted by reconfigurable intelligent surfaces (RIS) mounted on aerial platforms (RIS‐APs) is conceived, where the connection status among users and RIS‐APs are selected according to the average channel quality dynamically and timely. Taking into account the time‐varying selection status and the mobility of users, we construct a long‐term dynamic process. The goal is to minimize the time‐averaged power consumption under the requirements of the time‐averaged minimum rate for users as well as the constraint of the maximum transmit power for the base station (BS), via jointly optimizing the active beamforming at the BS and passive beamforming at RIS‐APs. With the aid of Lyapunov concept‐based drift‐plus‐penalty (DPP) algorithm, the long‐term optimization problem is transformed into short‐term sub‐problems related to each other at each frame. Subsequently, the fractional programming method based on Lagrangian dual theory is applied to derive the solutions for active‐passive beamforming in a closed form. Finally, simulation results validate the convergence and effectiveness of the proposed algorithm.

Artificial noise (AN), aiming to disturb the eavesdropper while avoiding the influence on the legitimate receiver, has arisen as an excellent technology for improving the physical-layer security of wireless communications. In order to challenge AN, zero-forcing elimination (ZFE) has been introduced as a possible countermeasure to mitigate the AN for the eavesdropper at the cost of more available receive antennas. In this contribution, from the perspective of eavesdroppers, we further conceive a class of efficient null-space elimination (NSE) schemes in order to reduce the number of receive antennas while enhancing the detection quality compared to original ZFE. Furthermore, the performance of secrecy rate as well as bit-error rate (BER) is quantified for both ZFE and NSE schemes through theoretical derivation, while the influence of imperfect channel state information (CSI) is also evaluated. The performance comparison of the above-mentioned schemes illustrates that NSE can provide more robust performance for eavesdroppers over ZFE, with lower hardware requirements as well as moderate complexity increase.

A novel reconfigurable intelligent surface (RIS)-aided hybrid reflection/transmitter design is proposed for achieving information exchange in cross-media communications. In pursuit of the balance between energy efficiency and low-cost implementations, the cloud-management transmission protocol is adopted in the integrated multi-media system. Specifically, the messages of devices using heterogeneous propagation media, are firstly transmitted to the medium-matched AP, with the aid of the RIS-based dual-hop transmission. After the operation of intermediate frequency conversion, the access point (AP) uploads the received signals to the cloud for further demodulating and decoding process. Based on time division multiple access (TDMA), the cloud is able to distinguish the downlink data transmitted to different devices and transforms them into the input of the RIS controller via the dedicated control channel. Thereby, the RIS can passively reflect the incident carrier back into the original receiver with the exchanged information during the preallocated slots, following the idea of an index modulation-based transmitter. Moreover, the iterative optimization algorithm is utilized for optimizing the RIS phase, transmit rate and time allocation jointly in the delay-constrained cross-media communication model. Our simulation results demonstrate that the proposed RIS-based scheme can improve the end-to-end throughput than that of the AP-based transmission, the equal time allocation, the random and the discrete phase adjustment benchmarks.

The introduction of space-time block code (STBC) can efficiently increase the diversity of original spatial modulation (SM). However, the spectral efficiency of STBC will be reduced gradually with the increase of the transmit antennas. In this contribution, we conceive a novel quasi-orthogonal space-time block coded spatial modulation (QOSTBC-SM) design for multiple-input and multiple-out (MIMO) transmission by reaping their respective benefits, while improving the spectral efficiency compared to conventional space-time block coded spatial modulation (STBC-SM). More specifically, in the proposed QOSTBC-SM structure, the information bits are conveyed via the active antenna index, as well as the QOSTBC blocks at the transmitter, while at the receiver, a low-complexity detection scheme is proposed. Furthermore, a closed-form union bound of the bit error rate (BER) is also quantified by theoretical derivation. Finally, our simulation results demonstrate that QOSTBC-SM is capable of outperforming the conventional counterpart as STBC-SM with higher spectral efficiency.

In this paper, we conceive the design of dynamic wireless networks assisted by multiple intelligent reflecting surfaces (IRSs), where the connection states between users and IRSs are capable of being updated timely. Taking into account the time-varying states of the system, we further construct a long-term dynamic process. Our goal is to maximize the time average sum-rate of the dynamic system under the time average rate and power constraints of users, via jointly optimizing the power allocation at users and the reflecting coefficients at IRSs. With the aid of Lyapunov concept-based drift-plus-penalty (DPP) algorithm, the long-term optimization problem is formulated as an infinite-horizon time-average one. Subsequently, the fractional programming method based on Lagrangian dual transform is applied to optimize power allocation and reflecting coefficients in an iterative manner, and the closed-form solutions of power and reflecting coefficients can be obtained at each iteration. Finally, simulation results demonstrate the convergence and effectiveness of the proposed algorithm. Further performance comparisons indicate that the proposed algorithm can maintain a balance between supply and demand for resource allocation and improve the fairness of users.

Orthogonal time frequency space (OTFS) system constitutes an effective structure conceived for efficiently utilizing the channel information, which is capable of achieving a promising transmission performance in high-mobility environment. To extract enough channel diversity, a two-dimensional Fourier transformation combined with a pulse shape is designed at the OTFS transmitter. Consequently, the amplitude of OTFS signals may fluctuate drastically, owing to the combined dependency of the OTFS transformation and the pulse shape. To quantify the amplitude fluctuation, we investigate the peak-to-average power ratio (PAPR) of OTFS signals, for a large amount of data in the delay-Doppler domain. We first reveal that when the number of data points approaches to infinity, based on central limit theorems for dependent variables, the complex-valued OTFS signals weakly converge to a Gaussian distribution. Then, according to the extremal theory of the Chi-squared process for stationary OTFS signals, an accurate expression of the PAPR distribution is derived, depending on the transmit pulse and the number of data points. It is also demonstrated that upon modifying the exponential factor, the analytical PAPR expression is applicable for the non-stationary Gaussian distribution caused by the bandlimited pulse with a large roll-off factor. Simulation results confirm the accuracy of the analytical PAPR probability for practical conditions.

This paper investigates the long-term effectiveness and stability of an integrated unmanned aerial vehicles (UAV)-intelligent reflecting surface (IRS) relaying dynamic system in the context of time-varying system states. Consequently, a dynamic optimization problem is constructed to minimize the frame-average transmit power by joint active beamforming at the base station (BS) and passive beamforming at the IRS under frame-average rate constraints. The original problem as an infinite-horizon time-average one can be solved by introducing the drift-plus-penalty (DPP) algorithm and then the optimal active beamforming and passive beamforming can be obtained in an iterative manner. Simulation results demonstrate the theoretical analysis and assess the performance of the dynamic system.

In this paper, we propose a novel intelligent successive interference cancellation (SIC) detection algorithm, namely I-SIC, for the uplink non-orthogonal multiple access (NOMA) system. Compared with some traditional SIC detection algorithms based on channel state information (CSI) and quality of service (QoS), the proposed I-SIC can learn the implied characteristics in the received signal, channel state information and power information through deep neural network (DNN), so as to more intelligently provide sorting scheme for SIC detection algorithm and further improve the detection performance of the system. Experimental results show that compared with the traditional SIC detection algorithm based on CSI (CSI-SIC), this algorithm can significantly improve the detection performance of the system(up to 6 dB for three-user scenario with QPSK modulation).

In the vision of the next generation communications, the wireless devices may work on different transmission media, such as microwave and visible light. In this case, how to bridge these different devices remains an open challenge. Following the framework of [1], we consider a cross-media base station (BS) for supporting devices working on different media, as visible light and radio frequency (RF). Specifically, we conceive two criteria for power allocation toward enhanced performance, by maximizing the sum and minimum rates. Furthermore, we also analyze the impact of the position of BS to the system performance. Finally, the theoretical results are verified by simulations for supporting the effectiveness of the developed power allocation schemes in cross-media communications.

Reconfigurable intelligent surface (RIS)-assisted wireless communications have been widely investigated toward enhanced signal quality, energy efficiency as well as throughput. The issue of multipath separation and identification via multiple RISs, which, however, remains to be further investigated, is of paramount importance. For alleviating this problem, we conceive a novel RIS-assisted spreading and code division multiple access (CDMA) structure, where each RIS holds a time-varying reflection coefficient sequence for spreading, in order to tag the channel and distinguish each other with the idea of CDMA. Moreover, practical channel estimation and multipath identification are considered, and the ergodic channel capacity is also derived when the receiver employs maximum ratio combining. Simulation results corroborate the feasibility of the proposed RIS-CDMA structure and the effectiveness of the developed channel estimation and multipath combination approaches.

In this paper, a pair of detectors based on the local search (LS) and${M}$algorithms are proposed for the multiple-input multiple-output subcarrier-index modulation orthogonal frequency division multiplexing (MIMO-SIM-OFDM) systems. Specifically, the${M}$algorithm is employed in the LS algorithm to avoid the local optimal solution in the first proposed detector, which is named as enhanced LS (E-LS) detector. Furthermore, a threshold is employed to the E-LS detector to reduce the complexity in the second proposed detector, which is named as low-complexity E-LS (LC-E-LC) detector. Simulation results show that the proposed detectors can obtain a flexible tradeoff between performance and complexity, compared to the traditional maximum likelihood (ML) and log-likelihood ratio (LLR) detectors.

In this paper, the idea of offset spatial modulation (OSM) is integrated with orthogonal frequency division multiplexing (OFDM), toward an efficient design to bridge their advantages. Compared to its conventional counterpart as spatial modulation (SM)-OFDM, the proposed OSM-OFDM scheme aims at providing a simplified implementation structure with less number of radio frequency (RF) chains, by introducing an offset between the RF chain and the index of the activated transmit antenna on each subcarrier. Specifically, three types of offset antenna selection (OAS) methods are developed to meet different scene requirements for different number of available RF chains. Furthermore, through theoretical analysis, we quantify the bit error rate upper bounds of OSM-OFDM with different types of OAS methods. Finally, extensive computer simulations demonstrate that OSM-OFDM provides a flexible tradeoff among implementation cost, computation complexity and error performance.

In this paper, the adoption of an intelligent reflecting surface (IRS) for multiple user pairs in two-hop networks is investigated. Different from the existing studies on IRS that mainly focused on tuning the reflection coefficients of all elements, we consider the implementation oftruereflection resource management (RRM) through the identification of the best triggered module subset. More precisely, the implementation oftrueRRM builds on the premise of our proposed modular IRS structure consisting of multiple independent and controllable modules. In the context of modular IRS structure, we investigate the signal-to-interference-plus-noise ratio (SINR)-based max-min problem subject to per source terminals (STs) power budgets and module size constraint, via joint triggered module subset identification, transmit power allocation, and the corresponding passive beamforming. Whereas this problem is NP-hard due to the module size constraint, which can be addressed by the convex sparsity-inducing approximation to the hard module size constraint using mixed$\ell _{1,F}\text {-norm}$, where it yields a suitable semidefinite relaxation. Using techniques from separable convex programming, we provide a two-block alternating direction method of multipliers (ADMM) algorithm for the approximated problem. Numerical simulations are used to validate the analysis and assess the performance of the proposed algorithm as a function of the system parameters. Further energy efficiency (EE) performance comparison demonstrates the necessity and meaningfulness of the introduced modular IRS structure. Specifically, for a given network setting, there is an optimal value of the number of triggered modules for system, when the EE is considered.

This paper proposes a precoded optical space-domain index modulation scheme for indoor visible light communications, which is based on the optimization of the minimum Euclidean distance of optical spatial modulation (OSM) with real-valued modulation constellations. We find that the precoding matrix design can be formulated as a non-convex quadratically constrained quadratic program (QCQP), whose solution is generally intractable. To tackle this problem, we first consider the case of two optical transmit antennas ( Nt= 2) in the precoded OSM and derive a closed-form solution for arbitrary M-order pulse amplitude modulation (PAM). Based on the derived solutions and the error vector reduction method, we then propose a low-complexity iterative (LCI) algorithm to identify the precoding matrix for the setup Nt> 2. To strike a flexible complexity-BER (bit error rate) tradeoff, we propose a successive convex approximation (SCA)-assisted matrix-based optimization method to transform the non-convex QCQP problem into a series of linear convex subproblems, which can be solved by low-complexity solvers. Simulation results show that these proposed algorithms are capable of substantially improving the system error performance compared with conventional OSM systems. Besides, a symbol-based SCA algorithm is introduced and it is shown to outperform the matrix-based SCA and the suboptimal LCI algorithm in terms of the BER.

In this paper, we investigate the issue of security on the remote state estimation in cyber-physical systems (CPSs), where a wireless sensor utilizes the channel hopping scheme to transmit the data to the remote estimator over multiple channels in the presence of periodic denial-of-service attacks. Assume that the jammer can interfere with a subset of channels at each attack time in active period. For an energy-constraint jammer, the problem of how to select the number of channels at each attack time to maximally deteriorate the CPS performance is investigated. Based on the index of average estimation error, we introduce two different attack strategies, which include selecting identical number of channels and unequal number of channels at each attack time, and further show theoretically that the attack effect by selecting unequal number of channels is better than that of selecting identical number of channels. By formulating the problem of selecting the number of channels as integer programming problems, we present the corresponding algorithm to approximate the optimal attack schedule for both cases. The numerical results are presented to validate the theoretical results and the effectiveness of the proposed algorithms.

It is known that the capacity of the intelligent reflecting surface (IRS) aided cellular network can be effectively improved by reflecting the incident signals from the transmitter in a low-cost passive reflecting way. Nevertheless, in the actual network operation, the base station (BS) and IRS may belong to different operators, consequently, the IRS is reluctant to help the BS without any payment. Therefore, this paper investigates price-based reflection resource (elements) allocation strategies for an IRS-aided multiuser multiple-input and single-output (MISO) downlink communication systems, in which all transmissions over the same frequency band. Assuming that the IRS is composed with multiple modules, each of which is attached with a smart controller, thus, the states (active/idle) of module can be operated by its controller, and all controllers can be communicated with each other via fiber links. A Stackelberg game-based alternating direction method of multipliers (ADMM) is proposed to jointly optimize the transmit beamforming at the BS and the passive beamforming of the active modules. Numerical examples are presented to verify the proposed algorithm. It is shown that the proposed scheme is effective in the utilities of both the BS and IRS.

This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio-frequency wave towards destination terminals (DTs). As the number of reflecting elements increases, the reflection resource allocation (RRA) will become urgently needed in this context, which is due to the non-ignorable energy consumption. The goal of this paper, therefore, is to realize the RRA besides the active-passive beamforming design, where RRA is based on the introduced modular IRS architecture. The modular IRS consists with multiple modules, each of which has multiple reflecting elements and is equipped with a smart controller, all the controllers can communicate with each other in a point-to-point fashion via fiber links. Consequently, an optimization problem is formulated to maximize the minimum SINR at DTs, subject to the module size constraint and both individual source terminal (ST) transmit power and the reflecting coefficients constraints. Whereas this problem is NP-hard due to the module size constraint, we develop an approximate solution by introducing the mixed row block l1,F-norm to transform it into a suitable semidefinite relaxation. Finally, numerical results demonstrate the meaningfulness of the introduced modular IRS architecture.

This paper investigates the spectral efficiency (SE) in reconfigurable intelligent surface (RIS)-aided multiuser multiple-input single-output (MISO) systems, where RIS can reconFigure the propagation environment via a large number of controllable and intelligent phase shifters. In order to explore the SE performance with user proportional fairness for such a system, an optimization problem is formulated to maximize the SE by jointly considering the power allocation at the base station (BS) and phase shift at the RIS, under nonlinear proportional rate fairness constraints. To solve the non-convex optimization problem, an effective solution is developed, which capitalizes on an iterative algorithm with closed-form expressions, i.e., alternatively optimizing the transmit power at the BS and the reflecting phase shift at the RIS. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed alternative algorithm.

Non-terrestrial network (NTN) based on orthogonal frequency-division multiplexing (OFDM) can provide robust high-speed data transition over geographically wide-range radio links between terrestrial ends and the satellite platforms. In this paper, we firstly investigate the performance of OFDM signal over a nonlinear channel caused by group delay and nonlinear distortion from the electronic processing and high power amplifier (HPA), respectively. By comparing with the conventional single-carrier waveform, simulation results show that the OFDM system can achieve a comparable BER performance with higher frequency efficiency in group delay channel, but suffers BER degradation from nonlinear distortion by HPA. To improve the BER performance, we propose a pre-distortion-based index- modulated OFDM (IM-OFDM) for the terrestrial-satellite radio link in the non-terrestrial network. The proposed scheme, taking the advantage of sparse allocation of the partially active subcarriers in IM-OFDM waveform, remaps the nonlinear distortion noise into an extended constellation space independently among the active and non-active subcarriers, thus to mitigate the effect of distortion noise to block-wise symbol detection. Simulations show that the proposed pre-distortion-based IM-OFDM signal is capable of achieving a better tradeoff among BER performance and HPA efficiency, compared with both the conventional OFDM and the IM-OFDM system.

Hybrid analog and digital precoding is one of the key techniques for reducing the number of radio frequency (RF) chains in massive multiple-input multiple-output (MIMO) systems. However, the security of transmission, as well as the interference among multiple users should be both considered for such a system. In this paper, we introduce the idea of spare beams to design the analog precoder for eliminating the multi-user interference. Meanwhile, for enhancing the security, the digital precoder is designed with the aid of artificial noise (AN). Simulation results demonstrate that the proposed scheme has the advantage of achieving better sum rate in comparison to the traditional scheme, while improving the secrecy rate with the identical number of sparse RF chains.

This paper introduces artificial noise (AN) into the spatial and directional (SDM) system, which combines the benefits of spatial modulation (SM), directional modulation (DM) and AN aided transmission. We first describe the SDM system and introduce AN into the null space of its channel matrix, and then, the bit error rate (BER) upper bounds of both the distributed legitimate user and eavesdropper are respectively derived based on the union bound theory, while the secrecy rate is studied. Simulated results demonstate that the SDM system with the aid of AN, compared with its conventional without AN counterpart, is capable of improving the security over the line-of-sight (LoS) channel encountered. Furthermore, in comparison with the DM-AN counterpart, we show that the spectral efficiency is effectively enhanced.

Index modulation (IM) has been developed as an energy-efficient transmission scheme for applications where high data rate is required with a limited energy budget. Meanwhile, multiple input multiple output (MIMO) and orthogonal frequency division multiplexing (OFDM) have been proved to be promising for the physical layer of wireless systems. Therefore, the concept of combining these three schemes to take all the advantages is emerging and the resulted systems are umbrellaed under the name of space-frequency index modulation (SFIM) systems. However, peak-to-average power ratio (PAPR) poses a challenge for SFIM due to the use of OFDM structure. Clipping is a low-complexity and effective measure to reduce the PAPR, however, it introduces distortions that degrade the system error performance. To address this issue, a hard-decision based cancellation method is proposed for the clipped SFIM system in this paper, which shows significant performance gains by effectively estimating the clipping noise and canceling it from the received signals.

A novel detection algorithm is proposed for multiuser detection (MUD) in uplink orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) systems employing the expectation propagation (EP) algorithm. Compared to the conventional elementary signal estimator (ESE) detector, the proposed detector is capable of reducing the complexity with a favorable performance imposed with the accurate estimate of the posterior distribution. Then, the proposed detector can also obtain a considerable bit error rate (BER) performance in high order modulation with high transmission rate. Finally, we design a new scheme suitable for OFDM-IDMA in the presence of multiple carrier frequency offsets (CFOs). Our simulation results illustrate that the proposed scheme can effectively mitigate the influence of multiple CFOs.

In this paper, the security issue of cache-enabled wireless networks is considered, where data transmission from the small base station (SBS) to the user is interfered by denial-of-service (DoS) attacks. To deal with this type of DoS attacks, we firstly investigate the energy dispatch problem from the perspective of the SBS. We further establish an optimization model by using the average number of subfiles requested from the macro base station (MBS) to measure the system performance, and then present the corresponding algorithm to derive the optimal scheduler. Additionally, numerical simulations are carried out to validate the effectiveness of the proposed algorithm.

Novel communication technology based on large intelligent surface (LIS) [1] has arisen recently, with the aim to enhance the signal quality at the receiver. In this paper, a practical structure of LIS-based spatial modulation (LIS-SM) is proposed, in order to utilize both transmit and receive antenna indices. Meanwhile, the theoretical average bit error rate (ABER) performance bound of the developed LIS-SM scheme is investigated. For the sake of achieving further spatial diversity gain, we extend its employment to the antenna selection (AS) scenario, and a low-complexity selection algorithm is designed on the basis of minimum squared Euclidian distance and signal-to-leakage-and-noise ratio as well as the idea of greedy elimination algorithm. Performance analysis shows that AS-aided LIS-SM is more robust in terms of ABER compared with conventional LIS-SM. Moreover, complexity analysis also depicts that the proposed fast selection algorithm achieves much lower complexity yet a comparable ABER performance, compared to the traditional exhaustive search.

Artificial noise (AN) is considered as a new physical layer technology to improve the security of wireless systems. In this paper, we investigate secure transmission of AN-aided generalized spatial modulation (GSM), which maintains the same hardware requirements at the transmitter as the conventional GSM. In order to further improve the jamming intensity of conventional AN scheme, we propose an Euclidean distance optimized AN (ED-AN) scheme by minimizing the Euclidean distance between the transmit signal and the jamming signal, which also avoids the power waste of conventional AN scheme. The secrecy capacities of both the AN-GSM and EDAN-GSM schemes are analyzed, and the optimal power allocation of AN-GSM is further investigated by maximizing the secrecy capacity. Furthermore, the upper bounds of the theoretical bit error rates (BERs) of both the legitimate receiver and the illegal eavesdropper over the Rayleigh fading channel are derived. Simulation results validate our derived analysis and demonstrate that the ED-AN scheme offers better secrecy and BER performance.

An N-continuous generalized frequency division multiplexing (GFDM) transceiver architecture is studied with the objective of striking a balanced trade-off between the bit error rate (BER) and the sidelobe suppression performance. More specifically, in the proposed N-continuous GFDM signaling, the basis signals constructed allow one to make the GFDM signal N-continuous and attain a compact spectrum as an explicit benefit of sidelobe suppression. We further reveal that compared to conventional N-continuous orthogonal frequency division multiplexing (NC-OFDM), N-continuous GFDM introduces relatively low interference through evaluating the signal-to-interference ratio (SIR). Secondly, a signal recovery algorithm is presented by constructing a recovery matrix to eliminate the interference. Finally, it is demonstrated that the proposed N-continuous GFDM scheme outperforms its N-continuous OFDM counterpart in terms of sidelobe suppression, while achieving moderate BER performance degradation as opposed to original OFDM.

A novel social-tie motivated relay selection scheme is proposed for dynamic device-to-device (D2D) communications overlaying cellular networks. Using the non-edge cellular users to forward data, the proposed relay selection scheme can improve the transmission performance of the cell-edge users as an explicit benefit of D2D relays. Meanwhile, the effects of both the physical layer and social layer on the relay selection are jointly considered, where social ties are regarded as not only the motivation of relay services, but also the metric of security performance. Moreover, a generalized satisfaction index is introduced for designing a uniform quality of experience (QoE) criterion that can map different quality of service (QoS) metrics such as rate, throughput, delay, into a unified metric, and hence, is beneficial for the tradeoff between QoE and resource efficiency of relay selection. Furthermore, a dynamic optimization process is constructed for analyzing the effects of both the mobility of users and the randomness of channel on the relay selection, with the aid of the Lyapunov framework and drift-plus-penalty (DPP) algorithm. Finally, numerical results validate the effects of the proposed relay selection scheme.

A simplified rectangular differential spatial modulation (S-RDSM) scheme is conceived for massive multiple-input multiple-output (MIMO) systems dispensing with the channel state information (CSI). In the proposed S-RDSM scheme, the information bits are first mapped to a conventional SM symbol and then rectangular differential encoding is invoked between a pair of SM symbols. Then a non-coherent detector relying on a forgetting factor is developed, which requires no CSI at the receiver. Explicitly, a low-complexity hard limited maximum likelihood (HL-ML) detector is conceived for our generalized S-RDSM scheme, which is characterized by our theoretical analysis. Furthermore, we derive the optimal forgetting factor in closed form, which is capable of significantly reducing the complexity of the associated optimization. Finally, the upper bounds of the average bit error probability (ABEP) are derived using the moment generating function (MGF), and are validated by our simulation results. Both the theoretical and simulation results have shown that the proposed S-RDSM system outperforms the existing non-coherent schemes, despite operating at 10% of the benchmarker's complexity, whilst approaching the performance of its coherent SM counterpart at a comparable complexity.

In this contribution, we propose and investigate the containment control issue for general linear multiagent systems (MASs) under the asynchronous setting, where the network topology is not subjected to any structural restrictions and the roles of the leaders and the followers are entirely determined by the network topology. It is assumed that the interaction time instants of each agent, at which this agent interacts with its neighbors, are independent of the other agents' and can be unevenly distributed. An asynchronous distributed algorithm is proposed to implement the control strategy of linear MASs. The non-negative matrix theory and the composition of binary relations are utilized to handle the asynchronous containment control issue. It is shown that the leaders in each closed and strongly connected component of the network topology will reach a common state and the followers will gradually enter the dynamic convex hull constructed by the leaders. Moreover, it is also proved that the system matrix can be strictly unstable, and the upper bound of the system matrix's spectral radius is explicitly stated. Finally, two simulation examples are also provided to verify the efficacy of our theoretical results.

This article investigates the security issue based on the proactive channel hopping scheme in wireless networked control systems (WNCS), where the sensor sends the measurement data to the controller through multiple channels attacked by a periodic denial-of-service jammer. For a jammer with the limited energy, the number of attacks, which denotes the amount of time expended in launching the attack, increases with the decline in the channel number attacked at each time, which has an effect on the success of receiving the data. On the basis of this, the problem of making an optimal tradeoff between the channel number attacked at each time and the attack number to degrade the WNCS performance maximally is investigated. We formulate this problem as an integer programming problem based on the linear quadratic Gaussian control cost function. Then, a necessary condition of the optimal solution without integrality constraints for this optimization problem is given for the scenario where the sensor utilizes one channel at each time to perform the data transmission. We further investigate this problem for the case where the sensor can select several channels at each time to transmit the measurement data. Moreover, for both cases the corresponding algorithms are presented to approximate optimal schedules. Finally, the theoretical results and the validity of the algorithm proposed are verified via the numerical results.

In this paper, we consider the secure generalised precoding aided spatial modulation (GPSM) scheme, which is combined with artificial noise (AN) to resist unknown malicious eavesdropping. Given a strict power constraint for the transmit signal and a wiretap Rayleigh fading channel, the BER performances of both the desired receiver and malicious eavesdropper are derived. Simulation results validate the accuracy of the theoretical analysis and demonstrate the secrecy performance of the secure GPSM system.

In this paper, the problem of caching at small base stations (SBSs) is investigated, where the SBSs are allowed to communicate with each other through a fixed communication topology. Assume that the central controller can only serve a subset of SBSs due to its limited capacity, when the cellular users request files. A problem rises in selecting which SBSs to cache the files requested in order to minimize the delay of file transmission from the SBS to the user. Moreover, the delay associated with all the user in the wireless network depends on the number of requirements. Motivated by this, the problem of selecting which SBSs to cache which file for minimizing the delay experienced by the cellular users is studied. We formulate the problem of minimizing the latency experienced by the cellular users as an integer programming problem and then propose a corresponding algorithm to obtain the optimal caching strategy. The numerical simulations show the validity of the proposed algorithm.es, or Math in Paper Title or Abstract.

In this paper, we propose a blockchain (BC)-enabled relay selection method in distributed cooperative communication networks, where non-cell-edge users (NCEUs) consume transmit power to relay cell edge users (CEUs) for uplink transmission in exchange for payments from CEUs. The proposed BC-enabled relay selection method aims at eliminating the failure of cooperative device to device (D2D) communication while maintaining privacy protection. By exploiting BC in the probe-reply phase, both CEU request and NCEU reply messages can be recorded in a verifiable manner. Once the feedback messages are received, the next step is decision making, which can be implemented by a two-sided matching game, in which the players include the CEUs party and the NCEUs one. In addition, the information recorded on the BC contains not only the probe-reply messages but also the optimal matching profile (e.g., transmission power sequence of NCEUs and the corresponding payment sequence of CEUs) in the second phase. The simulation results show that the proposed method is improved compared with the traditional matching scheme.

Caching contents in the small base station (SBS) is expected as a class of promising techniques in an effort to mitigate the congestion problem for future cellular networks. In this paper, we consider a heterogeneous cellular network with storage capable SBSs, and focus our attention on the SBS selection problem, in order to achieve balanced cost including incurred serving cost and users' experienced delay. The numerical results show that the location of SBS has an impact on the delay and servicing cost, which should be carefully decided in system design.

In this paper, an efficient coordinate descent method (CDM)-based turbo-type iterative detection algorithm is proposed for uplink interleave division multiple access (IDMA) systems. The simulation results and analysis reveal that the proposed CDM-based detector provides improved bit error rate (BER) performance compared to the conventional elementary signal estimator (ESE) conceived for IDMA systems. Meanwhile, by taking into account both the convergence performance and the computational complexity, we show that the proposed CDM-based detector is also superior to conventional ESE.

Orthogonal Frequency Division Multiplexing (OFDM) with Index Modulation (OFDM-IM), which conveyed information bits via the activated indices and constellation symbols is a promising technique in the next wireless communications. In the OFDM-IM scheme, only part of subcarriers are activated to transmit information, the inactive subcarriers transmit zero symbols, so that the conventional differential coding is not suitable for the adjacent subcarriers. In order to address this issue, in this paper, a novel Rectangular Differential OFDM-IM (RD-OFDM-IM) scheme is proposed to exploit the benefits of OFDM-IM dispensing with Channel State Information (CSI). In the proposed RD-OFDM-IM scheme, N subcarriers are partitioned into G subblocks and index modulation is employed in each subblock first. Then rectangular differential coding is invoked during two adjacent subblocks, so that non-coherent detection can be employed for the proposed RD-OFDM-IM scheme. Simulation results are shown that the proposed RD-OFDM-IM scheme is capable of providing considerable performance gain over conventional Differential OFDM (D-OFDM) scheme with lower Peak Average Power Ratio (PAPR).

As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both the technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of the IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by the existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks.

As one of the key enabling technologies of emerging smart societies and industries (i.e., industry 4.0), the Internet of Things (IoT) has evolved significantly in both technologies and applications. It is estimated that more than 25 billion devices will be connected by wireless IoT networks by 2020. In addition to ubiquitous connectivity, many envisioned applications of IoT, such as industrial automation, vehicle-to-everything (V2X) networks, smart grids, and remote surgery, will have stringent transmission latency and reliability requirements, which may not be supported by existing systems. Thus, there is an urgent need for rethinking the entire communication protocol stack for wireless IoT networks. In this tutorial paper, we review the various application scenarios, fundamental performance limits, and potential technical solutions for high-reliability and low-latency (HRLL) wireless IoT networks. We discuss physical, MAC (medium access control), and network layers of wireless IoT networks, which all have significant impacts on latency and reliability. For the physical layer, we discuss the fundamental information-theoretic limits for HRLL communications, and then we also introduce a frame structure and preamble design for HRLL communications. Then practical channel codes with finite block length are reviewed. For the MAC layer, we first discuss optimized spectrum and power resource management schemes and then recently proposed grant-free schemes are discussed. For the network layer, we discuss the optimized network structure (traffic dispersion and network densification), the optimal traffic allocation schemes and network coding schemes to minimize latency.

In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multiple-input multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the K -nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS and PA parameter evaluation. Furthermore, we investigate the design of feature vectors for the SLC and DNN approaches and propose a novel feature vector generator to match the specific transmission mode of SM. As a further advance, our proposed approaches are extended to other adaptive index modulation (IM) schemes, e.g., adaptive modulation (AM) aided orthogonal frequency division multiplexing with IM (OFDM-IM). Our simulation results show that the SLC and DNN-based adaptive SM-MIMO systems outperform many conventional optimization-driven designs and are capable of achieving a near-optimal performance with a significantly lower complexity.

Spatial modulation (SM) and space shift keying (SSK) techniques have the unique advantages of their single-radio-frequency (RF) structures compared with conventional multiple-input-multiple-output (MIMO) techniques. However, the transmission rates of these techniques are decided by the maximal switching frequency or by the minimal switching time between the RF chain and transmit antennas, which has been a bottleneck for their applications in future broadband wireless communications. To alleviate this problem, we propose a class of novel offset SM (OSM) and offset SSK (OSSK) schemes, with the aid of channel state information (CSI) at the transmitter. Compared with conventional SM and SSK, the proposed OSM and OSSK schemes can reduce the switching frequency of the RF chain, by introducing an offset between the connected RF chain and the index of the spatial modulated antenna. In extreme conditions, the proposed OSM and OSSK can work without RF switching while maintaining the single-RF advantage of conventional SM and SSK schemes. Through theoretical analysis, we also develop the bit-error rate (BER) performance bounds for the proposed two schemes. Finally, our simulation results demonstrate that the proposed OSM and OSSK outperform their counterparts, including conventional SM, SSK, CSI-aided SM, and CSI-aided SSK, while having a simplified RF-switching structure.

In this paper, we investigate the optimal dynamic social-aware peer selection with spectrum-power trading to maximize the average sum energy efficiency (EE) of cellular users (CUs) for uplink transmission for an orthogonal frequency division multiple access cellular network with device-to-device (D2D) communications. Different from the previous studies, which mostly focus on how to exploit social ties in human social networks to construct the permutation of all the feasible peers, we consider dynamic peer selection with social awareness-aided spectrum-power trading in D2D overlaying communications. Specifically, the amount of transmit power from the D2D transmitters to relay the CUs for uplink transmission is determined by their social trust levels. Likewise, the D2D transmitters can gain the corresponding amount of spectrum from the CUs for D2D pair link communications, which can be regarded as the compensation of the power consumption for relaying CUs. We formulate the dynamic peer selection problems with social awareness-aided spectrum-power trading in cooperative D2D communications into the infinite-horizon time-average renewal-reward problems subject to time average constraints on a collection of penalty processes. And the Lyapunov optimization concepts-based drift-plus-penalty algorithms are proposed to solve them. The simulation results demonstrate the effectiveness of the proposed dynamic peer selection algorithms. And further performance comparison indicates that the proposed dynamic peer selection algorithms not only maximize the average EE of CUs but also guarantee higher privacy protection.

In this paper, a novel scheme dubbed space-time block coded rectangular differential spatial modulation (STBC-RDSM) is proposed for multiple-input and multiple-out (MIMO) systems, which combines space-time block coding (STBC) and rectangular differential spatial modulation (RDSM) to reap their respective benefits while avoiding the drawbacks of conventional differential spatial modulation (DSM) systems. More specifically, in the proposed STBC-RDSM scheme, information bits are conveyed via the rectangular differentially encoded antenna index matrices, as well as the STBC blocks. Furthermore, a low-complexity detection scheme is proposed. Our simulation results demonstrate that STBC-RDSM outperforms its conventional DSM counterparts in various spectral efficiencies. Finally, a closed-form union bound on the bit error rate (BER) is derived and validated by our simulation results.

A novel multiple-input and multiple-output (MIMO) transmission scheme termed as generalized quadrature spatial modulation (G-QSM) is proposed. It amalgamates the concept of quadrature spatial modulation (QSM) and spatial multiplexing for the sake of achieving a high throughput, despite relying on a low number of radio frequency (RF) chains. In the proposed G-QSM scheme, the conventional constellation points of the spatial multiplexing structure are replaced by the QSM symbols, hence the information bits are conveyed both by the antenna indices as well as by the classic amplitude/phase modulated (APM) constellation points. The upper bounds of the average bit error probability (ABEP) of the proposed G-QSM system in high throughput massive MIMO configurations are derived. Furthermore, an efficient multipath orthogonal matching pursuit (EM-OMP)-based compressive sensing (CS) detector is developed for our proposed G-QSM system. Both our analytical and simulation results demonstrated that the proposed scheme is capable of providing considerable performance gains over the existing schemes in massive MIMO configurations.

Single-carrier frequency-domain turbo equalization (SC-FDTE) has gained widespread adoption in the emerging broadband spatial modulation (SM) systems operating in frequency-selective channels, where the channel model considered is a quasi-static Rayleigh fading channel. In this paper, a new class of robust FDTE designs based on the minimum mean-square error (MMSE) criterion is conceived for broadband single-carrier SM (SC-SM) systems relying on realistic imperfect channel knowledge. First, a robust time-domain soft-decision feedback (TDSDF)-aided FDTE is proposed to cope with channel estimation errors at the receiver. Furthermore, its robust frequency-domain soft-decision feedback (FDSDF)-aided counterpart is derived to offer a low-complexity approximate solution. Finally, by exploiting the carefully selected reliable soft-decision output of the channel decoder as pilots, we refine the resultant decision-directed channel estimation. As a benefit, the performance of the two robust FDTEs can be further improved. Both our simulation results and our extrinsic information transfer (EXIT) chart analysis demonstrate that the proposed robust FDTEs achieve significant performance improvements over the conventional FDTEs.

We investigate the energy-efficient power allocation for downlink transmission in orthogonal frequency division multiple access-based long term evolution systems. Aiming at realizing on-demand power allocation in cellular networks, we explore the available coordination between the base station and multiple users, and propose a new performance merit, namely, the demand side coordination energy efficiency (DSC-EE), which captures the system normalized EE and the demand side information. The proposed DSC-EE is designed to exploit individual disparities from both the entire system and the individual own expected utility perspectives. Our goal is to maximize the DSC-EE of the system via power allocation with a constraint on the maximum transmit power. Specifically, the objective function of DSC-EE maximization problem in a fractional form can be transformed into a subtractive form that is more tractable based on the fractional programming theory. The convergence property of the proposed algorithms and the meaningfulness of the proposed performance merit related to the EE are demonstrated by simulations. The comparison of four EE metrics, the EE and the rate fairness, global-EE, Sum-EE, and DSC-EE, shows that the DSC-EE maximization tends to achieve high implementation level of on-demand power allocation while ensuring the EE of the system. In addition, when the minimum rate replaces the expected rate in the DSC-EE, further performance comparison indicates the necessity and impact of the expected rate in the tolerable quality of service bias function.

Spatial Modulation (SM) has the unique advantage of its single-radio frequency (RF) structure compared to conventional multiple-input multiple-output (MIMO) techniques. However, the transmission rate of SM-MIMO is decided by the maximal switching frequency when it switches between the RF chain and transmit antennas, which has been a bottleneck for its applications in future broadband wireless communications. For alleviating this problem, in this paper, a class of novel offset spatial modulation (OSM) schemes are proposed with the aid of channel state information (CSI) at the transmitter. Compared to conventional SM, the proposed OSM scheme is capable of reducing the switching frequency of the RF chain, by introducing an offset between the connected RF chain and the index of the activated antenna. Furthermore, in some extreme conditions, OSM can even work without RF switching while maintaining the single-RF advantage of conventional SM-MIMO. Through theoretical analysis, we also develop a bit-error rate (BER) performance bound for the proposed OSM system. Finally, our simulation results demonstrate that OSM outperforms its counterparts including conventional SM and CSI-aided SM, while offering a simplified RF- switching structure.

In this paper, a novel space-time block coded rectangular differential spatial modulation (STBC-RDSM) is proposed for multiple-input and multiple-out (MIMO) system, which combines the space-time block coding (STBC) and rectangular differential spatial modulation (RDSM) to take the advantages of RDSM and STBC systems, while avoiding the drawbacks of conventional differential spatial modulation (DSM) systems. More specifically, in the proposed STBC-RDSM scheme, the information bits are conveyed via the rectangular differential encoded antenna indices matrices, as well as the STBC blocks. Our simulation results demonstrate that STBC-RDSM outperforms the existing DSM systems for various spectral efficiencies.

N-continuous (NC) precoder is a class of efficient sidelobe suppression techniques by smoothing the consecutive multicarrier symbols at the cost of additional interference to the transmit signals. In this paper, we firstly investigate the N-continuous precoder in recently developed Subcarrier-Index Modulation Orthogonal Frequency Division Multiplexing (SIM-OFDM) systems, when the channel is modeled by frequency-selective fading. On the one hand, the Signal to Interference Plus Noise Ratio (SINR) is derived through theoretical analysis. Furthermore, with the quantified SINR, the achievable rate is first acquired for evaluating the performance of NC precoded SIM-OFDM. We also show that the above-mentioned performance analysis matches well with the simulation results.

Hybrid analog-digital precoding is a class of efficient techniques for improving the performance of massive multiple-input multiple-output (MIMO) systems with reduced number of radio frequency (RF) chains. Meanwhile, for combating the frequency selective fading, orthogonal frequency-division multiplexing (OFDM) should be also combined in current wireless standards such as 5G. Therefore, for massive MIMO-OFDM, it is difficult to decide the best precoding matrix in the context of frequency selective fading. In this paper, for alleviating this problem, a novel hybrid analog-digital precoding algorithm is proposed for multi-user large-scale MIMO-OFDM systems when the frequency selective fading channel is considered. We show that the proposed algorithm offers a flexible precoding design as well as reducing the requirement of the number of RF chains. Finally, the performance results demonstrate that the proposed algorithm is capable of approaching the performance of optimal full-digital precoding with a low implemention complexity.

In this paper, we investigate performance of partial transmit sequence (PTS) for peak-to-average-power (PAPR) reduction in orthogonal frequency division multiplexing with index modulation (OFDM-IM), where three patterns of subcarrier grouping are considered. Firstly, the PAPR performance in terms of the independence of candidate sequences generated by three subcarrier grouping patterns are analyzed, respectively. Both theoretical analysis and numeric simulation show that PTS can achieve somewhat better PAPR performance in OFDM-IM than that in OFDM system. Moreover, the issue of imperfect side information(SI) at the receiver is investigated for exposing the relationship between SI transmitting scheme and BER performance. In the context, the comparison of OFDM and OFDM-IM signal shows that the BER performance of PTS aided OFDM-IM is more robust than that of OFDM signal.

To supporting the systematic requirements of higher spectrum efficiency and user deployment density in future Industrial Internet of Things (IIoT), traditional orthogonal multiple access schemes, such as orthogonal frequency-division multiplex, are quite limiting. Nonorthogonal multiple access (NoMA) has been recognized as one of the enabling technologies for IIoT in 5G network. In this paper, we have proposed an alternative NoMA scheme called interleave-grid multiple access (IGMA). Depending on interleaving and grid-mapping process, IGMA is capable to provide reliable block error rate performance, marvelous user multiplexing capability, as well as robustness against intercell interference. IGMA can apply various detection and decoding techniques at receiver sides to improve the detection performance with acceptable complexity. Both of link-level and system-level results show the promising benefits of IGMA, in particular that nearly seven times user multiplexing gain compared with orthogonal frequency-division multiple access has been observed in the system-level simulation. In addition, a hardware test bed has been implemented to verify the IGMA performance and the testing results proved the strong competitiveness of IGMA over orthogonal frequency-division multiple access in terms of block error rate performance in user overloading scenarios.

In frequency hopping (FH) wireless communications, finding an effective transmission strategy to properly mitigate jamming has been recently considered as a critical issue, due to the inherent broadcast nature of wireless communications. Recently, game theory has been proposed as a powerful tool for dealing with the jamming problem, which can be considered as a player (jammer) playing against a user (transmitter). Different from existing results, in this paper, a bimatrix game framework is developed for modeling the interaction process between the transmitter and the jammer, and the sufficient and necessary conditions for Nash equilibrium (NE) strategy of the game are obtained under the linear constraints. Furthermore, the relationship between the NE solution and the global optimal solution of the corresponding quadratic programming is presented. In addition, a special analysis case is developed based on the continuous game framework in which each player has a continuum of strategies. Finally, we show that the performance can be improved based on our game theoretic framework, which is verified by numerical investigations.

Spatial-multiplexing aided spatial modulation (SMx-SM) is proposed, which intrinsically amalgamates the concept of vertical bell labs space-time (V-BLAST) and SM to attain a high transmission rate, despite its low number of radio frequency (RF) chains at the transmitter. Specifically, in the SMx-SM scheme, the transmit antennas are partitioned into groups and the SM technique is applied individually to each group. Furthermore, low-complexity threshold-aided compressive sensing-based and message passing-based detectors are derived for our SMx-SM system. Our simulation results show that the proposed SMx-SM system exhibits a better performance despite its lower complexity than the conventional generalized spatial modulation system. More importantly, the proposed SMx-SM system is capable of providing considerable performance gains over the V-BLAST system at the same number of RF chains and throughput. Finally, an upper bound is derived for the average bit error probability, which is confirmed by our simulation results.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM), which uses the indices of activated subcarriers to carry additional information bits, is a novel multicarrier transmission technique. In this paper, a profile-based power management relying on maximal minimum Euclidean distance (MED) criterion is proposed. Moreover, a low-complexity subset searching method based on the normalized maximal MED criterion and corresponding reception scheme are proposed. Simulation results show that, without any forward side information or additional complexity for reception, the proposed profile-based power allocation scheme is capable of providing bit error rate (BER) performance improvement over conventional OFDM-IM system.

Differential spatial modulation (DSM) is a novel attractive alternative technique for coherent spatial modulation (CSM) without channel state information (CSI) at the receiver. In this paper, iterative detection is firstly employed to improve the performance of DSM schemes. With the Hamming distance of two matrices reduced to the sum of simple elements, a lowcomplexity iterative detection scheme for recursive systematic convolutional (RSC) coded DSM scheme is proposed. Simulation results show that the proposed detector is capable of approaching a near- capacity performance with a great complexity reduction compared to the maximum a posteriori (MAP) detector.

Orthogonal frequency division multiplexing with index modulation (OFDM-IM) is a newly proposed technique, which achieves significantly improved performance in comparison with classical OFDM by using the indices of active subcarriers to carry additional information bits. In this paper, we propose two power allocation (PA) algorithms for the sake of further improving the bit error rate (BER) performance of conventional OFDM- IM system. The optimal PA algorithm is proposed by minimizing the pairwise error probability (PEP). Moreover, a suboptimal PA (SPA) algorithm is developed by optimizing the normalized minimum Euclidean distance (MED) instead of the PEP metric, in order to reduce the complexity of calculation. Simulation results show that with the aid of PA algorithms, significant performance gains can be achieved over conventional OFDM-IM systems.

In this paper, a novel multiple-input multiple-output (MIMO) transmission scheme, termed as receive antenna selection (RAS)-aided spatial modulation MIMO (SM-MIMO), is proposed for millimeter-wave (mmWave) communications. It employs the spatial modulation (SM) concept and the RAS technique to tackle the costs of the multiple radio frequency (RF) chains at both link ends. Moreover, we develop a pair of RAS algorithms for the proposed mmWave RAS-SM scheme based on the capacity maximization (max-capacity) and the bit-error rate (BER) minimization criteria, which are formulated as two combinatorial optimization problems. The theoretical gradients of the capacity and the BER with respect to RAS variables are derived and the convexities of these problems are discussed. Furthermore, a novel iterative algorithm through jointly designing the log-barrier algorithm (LbA) and the simplified conjugate gradient method is proposed for RAS optimization. Our simulation results show that the proposed RAS-SM schemes are capable of achieving considerable performance gains over conventional norm-based and eigenvalue-based schemes in mmWave MIMO channels, while avoiding an overwhelming complexity imposed by exhaustive search.

In this paper, low-complexity time-domain turbo equalization (TDTE) detectors based upon soft-interference-cancellation (SIC)-aided minimum mean-square error (MMSE) criterion are proposed for single carrier generalized spatial modulation (SC-GSM) systems. First, a symbol-by-symbol-aided TDTE detector for application to the small-scale GSM systems is proposed, where the zero symbols are considered as constellation points when performing SIC. Then, vector-by-vector-aided TDTE (VV-TDTE) detectors for application to larger-scale antenna systems are introduced, where the GSM symbol is treated as an entire vector when performing SIC. As for the proposed VV-TDTE detectors, in addition, different time-varying filter coefficients are designed, in order to strike a flexible tradeoff between complexity and performance. By relying upon extrinsic information transfer chart analysis, we show that the proposed TDTE detectors are capable of providing considerable bit error rate performance gains over existing TDTE detectors and over the classic frequency-domain equalization-based MMSE detector, especially for the unbalanced antenna configurations.

In this paper, we design the low-complexity tree search-based detectors for generalized spatial modulation (GSM) aided single carrier (SC) systems over dispersive channels. Specifically, we commence with a brief review of the existing detection algorithms and then extend the sphere decoding-aided (SD) tree search algorithms designed for flat fading channels to GSM-aided SC systems. Moreover, a pair of reduced-complexity tree search algorithms are proposed by employing a hybrid concept of SD and M-algorithm to balance a tradeoff between the performance and complexity. Our proposed detectors are capable of recovering the transmit signal for both the overdetermine and underdetermine antenna configurations. Simulation results show that: 1) the extended SD-aided tree search algorithms are capable of providing the same performance as the maximum likelihood (ML) algorithm with reduced complexity; 2) the proposed novel algorithms exhibit lower complexity with negligible performance loss, compared with other tree search algorithms.

Conventional energy harvesting (EH) communications are limited by energy unsteadiness and causality. In this paper, a novel technique namely energy borrowing (EB) is specifically designed as an efficient way to bridge energy harvesting and power grid in wireless communications. The EH nodes are capable of adaptively borrowing and returning energy from the power grid, at the cost of paying extra energy interest. In order to maximize the transmission throughput, the packets scheduling problem in the proposed EB-aided EH systems is investigated and adaptive solutions are developed. Simulation results show that the proposed EB-aided EH system provides throughput improvement over the conventional one without EB, while achieving extra energy benefits for the power grid.

In this paper, N-continuous (NC) precoding is first combined with spatial modulation (SM) - orthogonal frequency division multiplexing (OFDM) system for sidelobe suppression, and the bit-error rate (BER) performance of the considered system is analyzed over fading channels. On the one hand, the introduced interference of the NC precoder is modeled and analyzed for SM-OFDM, so as to achieve the theoretical BER performance. On the other hand, we show that the simulation results support the theoretical analysis in different parameter configurations of the considered NC precoded SM-OFDM system.

Generalized spatial modulation (GSM) is a recently proposed appealing multi-input multi-output (MIMO) transmission technique, which is capable of striking a tradeoff between the achievable transmission rate and the cost of radio frequency (RF) chains. In this letter, a novel low-complexity near-optimal soft decision (SoD)-aided detector is proposed for GSM, which considerably reduces the search space by employing a block minimum mean-squared error (B-MMSE) algorithm. Our simulation results show that the proposed detector is capable of achieving a better tradeoff between bit-error-rate (BER) performance and computational complexity compared with the existing matched filter (MF)-based SoD algorithms.

Generalized frequency division multiplexing (GFDM) is a recent multicarrier 5G waveform candidate with the flexibility of pulse shaping filters. However, the flexibility of choosing a pulse shaping filter may result in intercarrier interference (ICI) and intersymbol interference (ISI), which becomes more severe in a broadband channel. In order to eliminate the ISI and ICI, based on discrete Gabor transform (DGT), in this paper, a transmit GFDM signal is first treated as an inverse DGT, and then a frequency-domain DGT is formulated to recover (as a receiver) the GFDM signal. Furthermore, to reduce the complexity, a suboptimal frequency-domain DGT called local DGT is developed. Some analyses are also given for the proposed DGT-based receivers.

The benefits of transmit antenna selection (TAS) invoked for spatial modulation (SM) aided multiple-input multiple-output (MIMO) systems are investigated. Specifically, we commence with a brief review of the existing TAS algorithms and focus on the recently proposed Euclidean distance-based TAS (ED-TAS) schemes due to their high diversity gain. Then, a pair of novel ED-TAS algorithms, termed as the improved QR decomposition (QRD)-based TAS (QRD-TAS) and the error-vector magnitude-based TAS (EVM-TAS) are proposed, which exhibit an attractive system performance at low complexity. Moreover, the proposed ED-TAS algorithms are amalgamated with the low-complexity yet efficient power allocation (PA) technique, termed as TAS-PA, for the sake of further improving the system's performance. Our simulation results show that the proposed TAS-PA algorithms achieve signal-to-noise ratio (SNR) gains of up to 9 dB over the conventional TAS algorithms and up to 6 dB over the TAS-PA algorithm designed for spatial multiplexing systems.

In this paper, we investigate a pair of transmit precoding (TPC) algorithms conceived for spatial modulation (SM) systems communicating over flat-fading multiple-input multiple-output (MIMO) channels. In order to retain all the benefits of conventional SM, we design the TPC matrix to be diagonal and introduce two design criteria for optimizing the elements of the TPC matrix. Specifically, we first investigate a TPC design based on maximizing the minimum Euclidean distance dmin(max-dmin) between the SM signal points at the receiver side. A closed-form solution of the optimal max-dmin-based TPC matrix is derived. Then, another TPC design algorithm is proposed for directly minimizing the bit error ratio (BER) upper bound of SM, which is capable of jointly optimizing the overall Euclidean distance between all received signal points. In the minimum BER (min-BER)-based TPC algorithm, the theoretical gradient of the BER with respect to the diagonal TPC matrix is derived and a simplified iterative conjugate gradient (SCG) algorithm is invoked for TPC optimization. Our simulation results demonstrate that the proposed max-dmin-based TPC algorithm is optimal in terms of the minimum distance. However, increasing dmindoes not achieve a further BER improvement. We also confirm that the min-BER-based TPC outperforms the max-dmin-based TPC schemes in terms of the achievable BER performance.

Transform domain communication system (TDCS) is a multi- carrier cognitive radio (CR) technique which uses cyclic code shift keying (CCSK) for overlay opportunistic spectrum access. Specifically, at any given time, a TDCS system cyclically shifts a fundamental modulation waveform (i.e., a CR sequence satisfying a dynamic spectrum hole constraint) according to specific input data symbol. In practical TDCS system, an interesting research problem is how to design CR sequence with inter-carrier interference (ICI) resilience, where ICI is caused by carrier frequency offset (CFO) or Doppler spread. In this paper, we present a novel family of CR sequences which are able to achieve ICI self-cancellation in TDCS. Analysis and simulations validate that our proposed CR sequence is effective for ICI suppression in TDCS. A future work of this research is to optimize the peak-to-mean power ratio of such CR sequences.

Energy efficiency is of high attention these days due to the development of green communications. Previous resource allocation algorithms in orthogonal frequency division multiple access (OFDMA) mainly focused on maximizing the energy efficiency (EE) of all the users and cannot ensure fairness in advance. To strike a balance between EE and user fairness, this paper proposes an energy-efficient proportional resource allocation algorithm. During subcarriers allocation, the priority of users is adaptively determined by both overall EE and the ratio of users' EE to proportionality. Then a low-complexity power allocation algorithm is extended to uplink OFDMA by searching the water level with constraints on maximum power and minimum rate requirements of each user. Simulation results show that the proposed schemes achieve a substantially proportional fair on EE among users as well as a relatively high over EE.

In this study, the authors investigate the benefits of phase‐rotation‐assisted precoding (PRP) technique in spatial modulation (SM) systems, which are based on maximum free distance d min . First, a closed‐form solution of the maximum‐ d min PRP matrix is derived for the scenario of having two transmit antennas ( N t = 2). Moreover, two numerical methods are proposed for dealing with the case of N t > 2. The complexity of the proposed algorithms is presented. The authors simulation results show that the proposed PRP algorithms provide beneficial bit error ratio performance improvements compared with both the conventional SM and with the existing adaptive SM.

N-continuous orthogonal frequency division multiplexing (OFDM) is a promising sidelobe suppression technique by enhancing the continuity between adjacent symbols with higher-order derivatives. However, it has high computational complexity due to the large-scale matrix operations in frequency domain. In this paper, a low-complexity time-domain signal processing (TDSP) algorithm for N-continuous OFDM is proposed. Based on linear combination of basis vectors, it utilizes small-scale matrix operations to generate the coordinates to construct N-continuous OFDM signal. Simulation results approve that the proposed algorithm achieves dramatic complexity reduction with identical sidelobe suppression and BER performance compared to conventional frequency-domain processed N-continuous OFDM.

As a cognitive radio (CR) modulation technique, transform domain communication system (TDCS) has been proposed by utilizing white space spectrum for the network access and achieving low probability of interception (LPI). In the previously reported TDCS- based cognitive radio networks (CRN), however, the non-zero periodic correlation function between any pair of users results in multiuser interference (MUI). In this paper, a novel framework under the CR constraints is presented by employing a two- dimension spreading scheme (i.e., time and frequency domains). We first obtain a class of spreading sequences with almost perfect periodic correlation function for arbitrary spectrum utilization pattern. Then, an universal criteria is presented for synchronous MUI-free CRNs when employing TDCS. Simulation results demonstrate that the proposed TDCS-based CRN architecture is a preferable candidate for distribution wireless networks, such as CR Ad-hoc wireless sensor networks.

Energy efficiency is becoming more and more important for mobile devices in future green radio network. This paper addresses the tradeoff between energy-efficiency (EE) and spectral efficiency (SE) in uplink multiuser orthogonal frequency division multiple access (OFDMA) systems. In this paper, EE is measured by the sum of instantaneous bits-per-Joule of each user, and it is optimized with the restrictions on both the minimum rate requirements and available transmit power over frequencyselective channels. Based on this optimization model, a novel low-complexity energy-efficient resource allocation algorithm is proposed. Simulation results demonstrate that the proposed scheme provides a better tradeoff between SE and EE than the conventional schemes with reduced complexity.

A novel detect-and-forward (DeF) relaying aided cooperative SM scheme is proposed, which is capable of striking a flexible tradeoff in terms of the achievable bit error ratio (BER), complexity and unequal error protection (UEP). More specifically, SM is invoked at the source node (SN) and the information bit stream is divided into two different sets: the antenna index-bits (AI-bits) as well as the amplitude and phase modulation-bits (APM-bits). By exploiting the different importance of the AI-bits and the APM-bits in SM detection, we propose three low-complexity, yet powerful relay protocols, namely the partial, the hybrid and the hierarchical modulation (HM) based DeF relaying schemes. These schemes determine the most appropriate number of bits to be re-modulated by carefully considering their potential benefits and then assigning a specific modulation scheme for relaying the message. As a further benefit, the employment of multiple radio frequency (RF) chains and the requirement of tight inter-relay synchronization (IRS) can be avoided. Moreover, by exploiting the benefits of our low-complexity relaying protocols and our inter-element interference (IEI) model, a low-complexity maximum-likelihood (ML) detector is proposed for jointly detecting the signal received both via the source-destination (SD) and relay-destination (RD) links. Additionally, an upper bound of the BER is derived for our DeF-SM scheme. Our numerical results show that the bound is asymptotically tight in the high-SNR region and the proposed schemes provide beneficial system performance improvements compared to the conventional MIMO schemes in an identical cooperative scenario.

In this letter, an extension of the near-optimal matched-filter (NMF) detector that provides improvement of the performance with negligible extra complexity is proposed for space-time shift keying (STSK) systems. In contrast to the NMF which only utilizes the index of the most probable active dispersion-matrix (DM) for detection, the proposed method sorts the DM index set and achieves the final results from theKmost probable indices, so as to alleviate the error propagation caused by DM misdetection. Simulation results show that the proposed algorithm provides considerable performance improvement for both hard and soft output detection as compared to NMF. Furthermore, it is also shown that the proposed detector is capable of achieving the same performance as that of the exhaustive-search MF (EMF) detector at reduced complexity, especially for high data rates.

As a bridge of connecting orthogonal frequency division multiplexing (OFDM) with single-carrier frequency domain equalization (SC-FDE) techniques, Vector OFDM (V-OFDM) provides significant flexibility in system design. This paper presents an analytical study of V-OFDM over multi-path fading channels. Our goal is to investigate the diversity gain and coding gain of each vector block (VB) in V-OFDM so as to ultimately reveal its performance limits over fading channel. By using algebraic number theory tools, we rigorously prove for the first time that a majority of VBs in V-OFDM can surely realize the diversity gain of min {M,G} , where M is the length of each VB, and G is the total number of channel taps. Furthermore, some specific VBs, whose length equals the total number of channel taps, can not only harvest the maximum diversity gain but also achieve the maximum coding gain. It is further demonstrated that, even though VBs fail to benefit from additional diversity gain when M exceeds G, they can enjoy significantly increased coding gains. Our analysis concludes that it is preferable to choose the length of VBs to be equal to the number of channel taps in consideration of both overall system performance and computational complexity.

Peak-cancellation is a pre-distortion technique for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems. The distortion introduced to the transmitted signals can be mitigated on the condition that the signal attenuation factor is known at the receiver. In this paper, to alleviate the distortion and disclose the signal feature of peak-cancellation combined OFDM signals, the signal attenuation factor is analyzed and hence evaluated, which is supported by simulation results.

In this letter, a low-complexity structure of PAPR reduction scheme, user grouping, in multicarrier code-division multiple access (MC-CDMA) is introduced. The proposed structure is based on joint spreading and inverse fast Fourier transform (S-IFFT) processing, and a class of low-complexity structure of user grouping scheme is proposed on the tradeoff between computational complexity and storage size. Comparison with the conventional PTS schemes, it shows that the proposed low-complexity scheme can achieve similar PAPR reduction with lower complexity.

In this paper, we address the problem of channel estimation in time-variant multi-path environment in Orthogonal frequency division multiplexing systems(OFDM). Based on the assumption that the channel varies in a linear fashion during an OFDM block duration, a novel iterative channel estimation arithmetic with noise and interference suppression using pilot tones is investigated. Through iterative noise and interference suppression to determine the positions of channel taps, then we compute the time averages and slopes of that to get the time domain channel matrix, so such method can improve the precision of the estimation. To increase the spectral efficiency and reduce the computational complexity, assuming the positions of channel taps are not changed in one frame duration, a frame based on such estimation scheme is presented. Along with the channel estimation technique, we also analyze the optimum pilot tones placement. Theoretical analysis and simulation results show that our assumption is reasonable, and the proposed channel estimation arithmetic has a good performance with low computational complexity and high spectral efficiency.

Partial transmit sequence (PTS) is an attractive distortless peak-to-average power ratio (PAPR) reduction technique for orthogonal frequency division multiplexing (OFDM) system. However, the complexity of PTS increases exponentially with the number of sub-blocks as it requires an exhaustive searching over all phase factor combinations. In this paper, we explored the correlation among the multiple candidate signals of PTS and proposed a modified scheme with lower complexity. The main idea is based on selecting a subset from the whole set of candidate signals by analyzing the correlation of candidate signals. Simulation results show that the performance of the modified scheme is close to the theoretical lower bound and outperforms selected mapping (SLM) scheme with the same computational complexity.

Selected mapping (SLM) is a promising peak-to-average power ratio (PAPR) reduction technique for orthogonal frequency division multiplexing (OFDM) system. In SLM, phase sequences are combined with the data for generating alternative signals, so as to reduce the PAPR. In this paper, a new criterion is developed to examine the effects of different phase sequence sets in SLM-OFDM based on the mathematical correlation analysis among the alternative signals. Furthermore, according to the proposed criteria, the chaotic phase sequence set is introduced for improving the PAPR reduction performance in SLM. Simulation results show that the improved SLM outperforms conventional methods such as proposed in [6].

The high peak-to-average power ratio (PAPR) is one of the main drawbacks of multicarrier code division multiple access (MC-CDMA) system. To alleviate this problem, in this paper, we propose a novel user grouping scheme for PAPR reduction in the downlink of MC-CDMA. The basic principle is to partition the users into subgroups, and generate multiple alternatives to achieve considerable PAPR reduction. The proposed scheme can be regarded as a modified version of partial transmit sequence (PTS), but with improved performance. And simulation results show that the user grouping scheme is more effective than the conventional PTS in MC-CDMA system.

High peak-to-average power ratio (PAPR) is one of the main drawbacks of MC-CDMA system. Among current methods that aim at reducing PAPR, partial transmit sequence (PTS) and variable code sets (VCS) are the kind of schemes that can improve PAPR statistics effectively but with large search complexity. To overcome this disadvantage, in this paper, we investigate a PAPR reduction scheme which employs recursion algorithm to decrease the computational complexity and a cyclical-shift technique to reduce the signals' PAPR effectively. Theoretical analysis and simulation results show that the proposed scheme can achieve better PAPR reduction performance than VCS but with lower computational complexity.

Partial transmit sequence (PTS) with pseudo-random sub-block partition can achieve lower Peak-to-Average Power Ratio (PAPR) performance. Based on the correlation of the candidate signals, we can prove that the performance is uncorrelated with the input signal. In general, the correlation of the candidate signals will increase with the number of candidate signals. As a result, the performance improvement will decrease evidently. In this paper, we provide a novel method to design the phase factor for PTS scheme, so as to achieve quasi orthogonal candidates to get better PAPR reduction performance. Simulation results prove it is more efficient than the traditional one.

This letter presents a class of low complexity partial transmit sequence (LC-PTS) techniques for reducing the peak-to- average power ratio (PAPR) in orthogonal frequency division multiplexing (OFDM) systems. The basic principle is to analyze and utilize the correlation among the candidate signals generated in PTS, so as to simplify the computational complexity. Furthermore, the proposed technique focuses on simplifying the computation for each candidate signal, instead of reducing the total number of candidate signals such as in (Jayalath and Tellambura, 2000). Thus, it can be easily combined with other simplified techniques. Simulation results show that the new technique can effectively reduce the complexity compared with the conventional PTS scheme.