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 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.

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.

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.

In this paper, we propose a new framework by combining the concepts of maximum distance separable (MDS) code and generalized spatial modulation (GSM) for multiple-input multiple-output (MIMO) transmission, namely MDS-GSM-MIMO. In our design, we exploit the powerful MDS code to increase the minimum Hamming distance (MHD) of the code-words, up to 2, and use the concept of space-domain index modulation to enlarge the minimum Euclidean distance (MED) of the achieved multidimensional GSM constellations. Moreover, we add the maximum minimum distance (MMD) precoder and guaranteed Euclidean distance (GED) precoder to enhance the MED further. Therefore, the MDS-GSM-MIMO we proposed with precoding is capable of optimizing both the overall MHD and MED. Then, we conduct theoretical analyses and comparisons with regard to average bit error rate (ABER) bound, MHD and MED of the conventional GSM-MIMO and the proposed MDS-GSM-MIMO. Simulation results show that the MDS-GSM-MIMO exhibits a BER performance gain of 3 dB compared to conventional GSM-MIMO, while the proposed MDS-GSM-MIMO with precoding exhibits a BER performance gain of up to 4 dB compared to the counterpart without precoding.

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.

This paper investigates the model-driven federated learning (FL) for channel estimation in multi-user millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Firstly, we formulate it as a sparse signal recovery problem by exploiting the beamspace domain sparsity of the mmWave channels. Then, we propose an FL-based learned approximate message passing (LAMP) channel estimation scheme, namely FL-LAMP, where the LAMP network is trained by an FL framework. Specifically, the base station (BS) and users jointly train the LAMP network, where the users update the local LAMP network parameters by local datasets consisting of measurement signals and beamspace channels, and the BS calculates the global LAMP network parameters by aggregating the local network parameters from all the users. The beamspace channel can thus be obtained in real time from the measurement signal based on the parameters of the trained LAMP network. Simulation results demonstrate that the proposed FL-LAMP scheme can achieve better channel estimation accuracy than the existing orthogonal matching pursuit (OMP) and approximate message passing (AMP) schemes, and provides satisfactory prediction capability for multipath channels.

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).

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.

In order to mitigate the high peak-to-average power ratio (PAPR) of the uplink waveform, the frequency domain spectrum shaping (FDSS) and $\frac{\pi }{2}$ BPSK modulation are adopted to DFT spread orthogonal frequency-division multiplexing (DFT-s-OFDM) for both data and pilot transmission in 5G new radio (NR). However, generating such pilot sequences with acceptable orthogonality and reduced PAPR causes a computationally intensive search. This paper introduces the constant envelope (CE) OFDM waveform for both pilot and data transmitting with a PAPR of 0dB. Especially, an modified channel estimation scheme with the optimized selection of modulation index is proposed to overcome the non-flat power spectral density caused by phase modulation in CE-OFDM. Numerical results prove the superiority of the proposed low-PAPR waveform over the classical NR signal in terms of both PAPR and BER performance.

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.

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.

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).

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 uplink communication of a reconfigurable intelligent surface (RIS) assisted system, in which an user equipment (UE) with single radio frequency (RF) chain delivers information to an access point (AP) by adopting the spatial modulation (SM). Specifically, we first investigate the transmit SM (TSM) scheme and jointly optimize the UE’s power allocation matrix and the RIS reflection coefficients to enhance the system reliability. We formulate a non-convex optimization problem to reduce the system symbol-error-rate (SER) and propose a novel penalty-alternative optimizing algorithm to obtain a near-optimal solution. Following this, we show that with the assistant of RIS, receive SM (RSM) scheme can also be performed even if the UE has only one RF chain. Based on this observation, a novel RIS-assisted RSM scheme is proposed, which can provide a low cost and complexity solution for system realization. The reflection coefficients of the RIS are also optimized for the proposed RSM. Numerical results show that the RIS-assisted TSM can achieve a lower SER than the conventional communication scheme (CTS) without SM and the proposed RSM scheme has lower detection complexity than that of CTS. It is also shown that the performance of the TSM and RSM schemes is more sensitive to the quantization accuracy of the phase of the RIS coefficients than that of the amplitude.

In this paper, two novel adaptive spatial scattering modulation (ASSM) algorithms, namely unequal transmission probability-based ASSM (UTP-ASSM) and equal transmission probability-based ASSM (ETP-ASSM), are proposed to pursue a better tradeoff between the computational complexity and spectral efficiency. According to available channel state information, the proposed ASSM algorithms are conducted to compute the optimal numbers of scatters and the optimal modulation orders constrained by the maximal tolerable symbol error probability (SEP). In addition, the minimal tolerate value of the minimal tolerant signal-to-noise ratio and SEP threshold are computed with a given data rate requirement by the bisection algorithm. At the receiver, a new type of optimal maximum likelihood detector is proposed to enhance the SEP performance. Furthermore, the union upper bound on the SEP is derived and analyzed. In addition, the computational complexity and system performance of the ASSM schemes are analyzed. Simulation results demonstrate that the proposed ASSM algorithms yield a lower SEP than the conventional spatial scattering modulation at the same value of average data rate.

Spatial modulation (SM) is a new multiple-input multiple-output (MIMO) paradigm in which only one transmit antenna is activated over every symbol duration. So far, efficient SM training sequences (different from the existing design for conventional MIMO systems) remain largely open. Motivated by this research problem, we introduce a novel class of sequence pairs, called "cross Z-complementary pairs (CZCPs)", each displaying zero-correlation zone (ZCZ) properties for both their aperiodic autocorrelation sums and cross-correlation sums. A CZCP may be transmitted in two non-orthogonal SM channels and hence proper design should be conducted to minimize the cross-interference of the two constituent sequences. We construct perfect CZCPs based on selected Golay complementary pairs. We show that the training sequences derived from our proposed CZCPs lead to optimal channel estimation performance over frequency-selective SM channels.

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.

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.

Internet of Things (IoT) as a prospective platform to develop mobile applications, is facing with significant challenges posed by the tension between resource-constrained mobile smart devices and low-latency demanding applications. Recently, mobile edge computing (MEC) is emerging as a cornerstone technology to address such challenges in IoT. In this paper, by leveraging social ties in human social networks, we investigate the optimal dynamic computation offloading mode selection to jointly minimize the total tasks' execution latency and the mobile smart devices' energy consumption in MEC-aided low-latency IoT. Different from the previous studies, which mostly focus on how to exploit social tie structure among mobile smart device users to construct the permutation of all the feasible modes, we consider dynamic computation offloading mode selection with social awareness-aided network resource assignment, involving both the computing resources and transmit power from heterogeneous mobile smart devices. On the one hand, we formulate the dynamic computation offloading mode selection into the infinite-horizon time-average renewal-reward problems subject to time average latency constraints on a collection of penalty processes. On the other hand, an efficient solution is also developed, which elaborates on a Lyapunov optimization-based approach, i.e., drift-plus-penalty (DPP) algorithm. Numerical simulations are provided to validate the theoretical analysis and assess the performance of the proposed dynamic social-aware computation offloading mode selection method considering different configurations of the IoT network parameters.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.