As an important component of the Internet of Things (IoTs), underground localization based on magnetic induction (MI) communication with the received signal strength indicator (RSSI) has been a topic of considerable interest in wireless underground sensor networks (WUSNs). Existing works have widely exploited the active device localization based on wireless communications in underground space, where the transmission media remains air. However, achieving device-free localization (DFL) in an underground endogenous environment presents relatively large gaps. In this paper, we propose a novel MI-based communication method to achieve the DFL accuracy in complex underground endogenous environment. First, we establish an end-to-end MI link model to precisely characterize the RSSI and explicitly correlate MI antenna configurations with magnetic field attenuation in the underground endogenous environment. Subsequently, based on the magnetic-field propagation characteristics, we analyze the shadowing loss caused by the passive target in the MI link. A MI-DFL method is then formulated by incorporating the shadowing model. Finally, extensive numerical simulations across various environmental settings and target types demonstrate the effectiveness and robustness of the proposed method, indicating that MI-DFL attains high localization accuracy within the underground endogenous environments.

In this paper, we propose a hybrid simultaneous wireless information and power transfer (SWIPT)–enabled multiple-input multiple-output (MIMO) architecture, where the base station (BS) uses a conventional radio-frequency (RF) transmitter for downlink transmission and a Rydberg atomic quantum receiver (RAQR) for receiving uplink signals from Internet of Things (IoT) devices. To fully exploit this integration, we jointly design the transmission scheme and the power-splitting strategy to maximize the weighted sum rate, which leads to a non-convex problem. To address this challenge, we first derive closed-form lower bounds on the uplink achievable rates for maximum ratio combining (MRC) and zero-forcing (ZF), as well as on the downlink rate and harvested energy for maximum ratio transmission (MRT) and ZF precoding. Building upon these bounds, we propose an iterative algorithm relying on the best monomial approximation and geometric programming (GP) to solve the non-convex problem. Finally, simulations validate the tightness of our derived lower bounds and demonstrate the superiority of the proposed algorithm over benchmark schemes. Importantly, by integrating RAQR with SWIPT-enabled MIMO, the BS can reliably detect weak uplink signals from IoT devices powered only by harvested energy, enabling battery-free IoT networks.

The range-dependent phases induced by Frequency Diverse Array (FDA)-Multiple-Input Multiple-Output (MIMO) radar provide additional degrees of freedom for range related scenarios. This paper investigates the joint transmit waveform and frequency increment design of FDA-MIMO radar to enhance target detection in multipath environments. Specifically, we establish a generalized FDA-MIMO signal model for multipath reception and formulate a Signal-to-Interference-plus-Noise Ratio (SINR) maximization problem over the transmit waveform and frequency increment, while constraining the waveform modulus and increment upper-bound. Leveraging on an alternating optimization framework, we update the waveform via power-method-like implementation and the frequency increment via iterating an analytical expression based on a Majorization-Minimization (MM) scheme. Simulation results indicate that the proposed method achieves a gain up to 14dB over conventional MIMO, demonstrating superior energy focusing effect at target location.

In this paper, the joint active terminal identification (ATI) and channel estimation (CE) problem is investigated for the asynchronous grant-free random access system in the context of low-earth orbit (LEO) satellite Internet of Things (IoT) network. An asynchronous grant-free model is established to characterize the implications of delay and Doppler caused by LEO satellites. Concurrently, a generalized approximate message passing-aided structured joint detection (SJD) scheme with Rician parameters learning (RPL) is proposed for joint ATI and CE. The prior mean and variance of the Rician channel are treated as hyperparameters and updated via the expectation-maximization algorithm. Furthermore, to alleviate the modeling mismatch, we develop an off-grid model following Taylor expansion, accompanied by a mismatch error parameter learning (MPL) framework to boost the accuracy of joint detection. Simulation results demonstrate that the proposed RPL-SJD scheme outperforms existing approaches in terms of normalized mean square error with 4dB and activity detection error rate with 7dB.

In this paper, an affine frequency division multiplexing aided grant-free random access (AFDM-GF-RA) system is designed for low Earth orbit (LEO) satellite Internet of Things (SIoT) networks, where users employ the GF-RA to access the satellite concurrently by configuring the AFDM modulation. In order to evaluate the system’s performance, average bit error probability (ABEP) bounds are first derived by Cholesky decomposition, which are verified by simulation results. Furthermore, a block sparse prior expectation propagation detection (BSPEP) is developed for joint active users detection (AUD) and data detection (DD). User activity probability is considered as a prior for joint detection. Concurrently, the accuracy of AUD is further improved by combining the block sparsity characteristics of the user transmission signals. Simulation results show that the proposed detector outperforms the classical oracle least squares (OLS) benchmark and approaches the theoretical ABEP bound at high signal-to-noise ratio (SNR).

In this paper, we investigate the problem of task offloading and resource allocation in non-orthogonal multiple access (NOMA) based multi-beam satellite mobile edge computing (SMEC) systems, aiming to minimize the average task completion latency over a finite number of time steps. Such a problem is formulated as a sequential mixed-integer programming problem, which cannot be solved by standard optimization techniques. Thus, we convert the problem into a Decentralized Partially Observable Markov Decision Process (Dec-POMDP) with hybrid action space and propose a deep reinforcement learning-based solution. In particular, a multi-agent hybrid Proximal Policy Optimization algorithm (MAHPPO) is designed to address the hybrid action space. Simulation results show that the proposed scheme outperforms several benchmark schemes.

In this paper, we propose a novel active reconfigurable intelligent surface (RIS)-assisted amplitude-domain reflection modulation (ADRM) transmission scheme, termed as ARIS-ADRM. This innovative approach leverages the additional degree of freedom (DoF) provided by the amplitude domain of the active RIS to perform index modulation (IM), thereby enhancing spectral efficiency (SE) without increasing the costs associated with additional radio frequency (RF) chains. Specifically, the ARIS-ADRM scheme transmits information bits through both the modulation symbol and the index of active RIS amplitude allocation patterns (AAPs). To evaluate the performance of the proposed ARIS-ADRM scheme, we provide an achievable rate analysis and derive a closed-form expression for the upper bound on the average bit error probability (ABEP). Furthermore, we formulate an optimization problem to construct the AAP codebook, aiming to minimize the ABEP. Simulation results demonstrate that the proposed scheme significantly improves error performance under the same SE conditions compared to its benchmarks. This improvement is due to its ability to flexibly adapt the transmission rate by fully exploiting the amplitude domain DoF provided by the active RIS.

Affine frequency division multiplexing (AFDM) is a promising waveform for next generation wireless networks, effectively resisting the channel double selectivity. Conventional embedded pilot (EP) scheme for AFDM requires additional guard symbols, leading to a reduction in spectral efficiency (SE). To address this issue, superimposed pilot (SP)-based schemes have been proposed, improving the SE; however, at the cost of increased computational complexity at the receiver. In this work, we propose an iterative joint channel estimation and data detection scheme for AFDM by harnessing SPs, aiming to enhance the SE while reducing the detection complexity. Simulation results demonstrate that our proposed scheme outperforms the conventional SP-based scheme in both channel estimation and data detection, while achieving a lower computational complexity. Notably, under low data power conditions, our approach surpasses the EP scheme in terms of both SE and data detection accuracy.

Satellite communication environments, characterized by dynamic links, exacerbate packet reordering, which leads to fundamental limitations in QUIC’s loss detection mechanism. A fixed packet threshold may misinterpret reordered packets as lost, leading to spurious retransmissions and throughput degradation. To address the challenge, we propose GRU-QUIC, a machine learning-enhanced QUIC protocol with packet reordering resilience for satellite networks. First, we model the relationship between the probability of spurious packet loss due to reordering and the throughput of QUIC, providing a theoretical basis for performance optimization. Second, we design a gated recurrent unit (GRU)-based prediction module on the receiver side to predict the next slot packet reordering displacement by learning the temporal correlation of packet reordering sequences. The sender then uses the reordering displacement indicated by the GRU to dynamically adjust the packet threshold of loss detection, effectively alleviating the problem of packet loss misjudgment due to packet reordering. Experiments demonstrate that GRU-QUIC significantly enhances the performance of QUIC transmission while maintaining protocol compatibility. In a simulated satellite network, GRU-QUIC effectively reduces unnecessary retransmissions and shortens file download completion time compared to standard QUIC.

Metasurface-aided near-field radio imaging is emerging as an essential part of the future mmWave communication systems. Adjusting the metasurface phase shift to generate multiple measurements can significantly increase the system imaging aperture and enhance the resolution. However, this imaging technique relies heavily on precise measurements, and high-precision sampling leads to expensive hardware costs and memory burdens. To address this issue, this paper draws inspiration from binary compressive sensing and proposes a method for imaging under one-bit quantization. First, we propose a dynamic metasurface-aided mmWave imaging system with one-bit sampling, which simplifies the received signal acquisition process and significantly reduces the hardware and storage requirements. Then, a fused binary compressive sensing model is developed with an additional total-variation norm penalty to promote target continuity and suppress artifacts in mmWave images. Subsequently, we employ the proximal binary iterative hard thresholding algorithm to optimize the joint sparsity and the total variation (TV) constraints. In addition, the hybrid ℓ1-TV constraint is introduced to solve the problem of unknown a priori sparsity of image, and the alternating direction multiplication method is designed for effective reconstruction. Finally, the simulation results show that the proposed algorithms can utilize the target features under binary measurements and achieve better imaging accuracy and focusing performance than the sparsity constraint-only methods.

This paper proposes interference suppression strategies for Low Earth Orbit (LEO) multi-satellite communication systems. A two-dimensional satellite-cell matching algorithm based on a utility function is introduced to mitigate partial interference. Additionally, a beam hopping (BH) pattern optimization algorithm is developed using satellite priority to minimize inter-satellite interference and enable collaborative BH transmission. To further suppress interference and address the impact of imperfect channel state information (CSI), a robust precoding algorithm leveraging multi-satellite cooperation is presented. Numerical results validate the effectiveness of the proposed algorithms in interference suppression, demonstrating their resilience in channel estimation errors.

In this paper, a low-complexity beam hopping pattern design with frequency reuse (FR) is proposed to efficiently adapt to the non-uniform traffic demands characteristics of the terrestrial cells. Concretely, an FR-based beam hopping (BH) downlink transmission model is conceived for multi-beam satellite systems. Next, a heuristic BH pattern optimization algorithm with FR and load balancing (BH-FR-LB) is proposed to improve the communication capacity, which is divided into two sub-problems with low complexity. Specifically, the cells are allocated into different frequency sub-bands to optimize the traffic load. The selection of the BH pattern is updated based on the remaining traffic demands of the terrestrial cells at each time slot, thereby achieving a trade-off between spectrum efficiency and inter-beam interference mitigation. Simulation results show that the proposed scheme obtains higher traffic satisfaction rate, while only requiring fewer time slots to achieve the data transmission under low traffic demands.

We study the problem of Non-line-of-sight (NLOS) mmWave imaging under inaccurate knowledge of multiangle relay surfaces. To this end, we propose a novel double sparse structure-enhanced Bayesian compressive sensing framework with dictionary parameters updating. First, the hierarchical probabilistic model with a parametric multipath dictionary is constructed, where the angles of multiple relay surface are considered as an unknown parameter. Then, a double sparse spike-and-slab (DS-SS) prior is introduced to model the intra-group and inter-group sparsity of multipath image, where the expectation propagation method is employed for posterior inference (dubbed as DS-SSEP). Moreover, the maximum likelihood solutions of the dictionary parameters are estimated iteratively by coupling the expectation maximization framework with DS-SSEP. Several experiments demonstrate the superiority of our proposed method, which significantly reduces image reconstruction errors in imprecise layout scenarios.

Affine frequency division multiplexing (AFDM) and orthogonal AFDM access (O-AFDMA) are promising techniques based on chirp signals, which are able to suppress the performance deterioration caused by Doppler shifts in high-mobility scenarios. However, the high peak-to-average power ratio (PAPR) in AFDM or O-AFDMA is still a crucial problem, which severely limits their practical applications. In this paper, we propose a discrete affine Fourier transform (DAFT)-spread AFDMA scheme based on the properties of the AFDM systems, named DAFT-s-AFDMA to significantly reduce the PAPR by resorting to the DAFT. We formulate the transmitted time-domain signals of the proposed DAFT-s-AFDMA schemes with localized and interleaved chirp subcarrier allocation strategies. Accordingly, we derive the guidelines for setting the DAFT parameters, revealing the insights of PAPR reduction. Finally, simulation results of PAPR comparison in terms of the complementary cumulative distribution function (CCDF) show that the proposed DAFT-s-AFDMA schemes with localized and interleaved strategies can both attain better PAPR performances than the conventional O-AFDMA scheme.

The vision of backscatter communication always incorporates compatibility with active radios to enable low-cost and easy deployment. However, recent innovations lack the flexibility to communicate with heterogeneous wireless devices directly. In this paper, we design and implement a flexible backscatter system, i.e., Flexcatter, which can support various modulation schemes in a software-defined way, to be compatible with different kinds of active radios. The key technique is signal emulation, where the tag can vary the reflection coefficient in the time domain to emulate desired baseband signals. We first carefully design a cost-effective impedance network, which employs two radio frequency (RF) switches to provide up to 16 reflection coefficients. Next, we establish and model the emulation mapping between desired baseband signals and available reflection coefficients. Besides, the emulation frameworks for different modulation schemes are presented. After that, to face the emulation distortion for orthogonal frequency division multiplexing (OFDM), we introduce the oversampling method in the baseband modulation process. We further build the prototype hardware, and experiment results show that Flexcatter can flexibly generate various kinds of backscatter signals, including Wi-Fi, BLE, and LoRa. Especially the OFDM transmission generated by Flexcatter can achieve a throughput of 25.1 Mbps.

User-centric cell-free massive MIMO (CFmMIMO), where only partial access points (APs) are selected to serve a specific user equipment (UE), is a scalable extension of CFmMIMO. Existing works have studied the spectral efficiency of large-scale fading decoding (LSFD) aided user-centric CFmMIMO that includes local combining at each AP and statistical channel state information (S-CSI) based fusion in the central processing unit (CPU). However, few efforts have so far been paid to analyze the error probability bound, and existing detectors fail to balance the error probability and fusion complexity. In this paper, we analyze the symbol error rate (SER) and design low-complexity near-optimal detectors for uplink user-centric CFmMIMO systems. Considering non-identical large-scale fading coefficients and local channel estimation errors, we first leverage the pairwise error probability to derive an SER upper bound for optimal linear fusion (OLF) in the CPU, which is suitable to different local combining methods at the APs. Then, by combining local normalization methods and S-CSI based UE grouping or error correction, we design improved detectors for local maximum-ratio and local minimum mean squared error combining successively. Simulation results verify the correctness of the derived SER bound, and show that the proposed detectors are capable of approaching the SER performance of conventional OLF based counterparts with reduced fusion complexity even in scenarios with pilot contamination.

In this paper, a novel intelligent transmission surface (ITS) assisted terahertz (THz) wideband massive multiple-input multiple-output (MIMO) non-terrestrial communication architecture is conceived, which is capable of reducing the hardware cost and power consumption remarkably compared to traditional architectures. To further address the beam squint impact in the THz wideband system, an angle-based hybrid beamformer is designed for the proposed architecture, which can effectively suppress the beam squint and maintain high spectral efficiency (SE) performance. Numerical results demonstrate that the proposed scheme is capable of approaching the optimal full-digital architecture in terms of the SE performance, and the proposed method can achieve significant energy efficiency performance gains over other existing architectures.

This research investigates the digital-to-analog converter (DAC) free architecture for the digital reconfigurable intelligent surface (RIS) system, where the transmission lines are implemented for reflection coefficient (RC) control to reduce power consumption. In the proposed architecture, the radio frequency (RF) switch based phase shifter is considered. By using a single-pole four-throw (SP4T) switch to simultaneously control the RCs of a group of elements, a 2-bit phase shifter is realized for passive beam steering. A novel modulation scheme is developed to explore the cost effectiveness, which approaches the performance of traditional quadrature amplitude modulation (QAM). Specifically, to overcome the limitation of the phase shift bits, joint frequency-shift and phase-rotation operations are applied to the constellation points. The simulation and experimental results demonstrate that the proposed architecture is capable of providing an ideal transmission performance. Moreover, 64- and 256-QAM modulation schemes could be implemented by expanding the elements and phase bits.

In this paper, theoretical performance of optimal maximum likelihood (ML) detector and near-optimal simulated detectors are designed for massive multiple-input multiple-output (MIMO) aided grant-free (MM-GF) systems. Specifically, the approximated average bit error probability (ABEP) bounds are firstly derived by exploiting the relationship between the Hamming distance (HD) and the pairwise error probability (PEP), which are confirmed by the simulation results. Moreover, an extended alphabet based expectation propagation (EA-EP) and an adaptive subspace matching pursuit (ASMP) algorithm are devised for signal detection of MM-GF without the prior information (PI) of active users. Simulation results show that the proposed detectors are able to outperform the classic oracle least squares (OLS) benchmark and are capable of approaching the theoretical ABEP bounds of ML.

Multiple-antenna backscatter is emerging as a promising approach to combat the cascaded backscatter channel fading to offer a high data rate for ambient backscatter communication (AmBC). However, related designs in AmBC fail to provide both diversity and multiplexing gains to backscatter tags. To address this issue, in this paper, we present a unified generalized space-time shift keying (GSTSK)-Backscatter architecture for AmBC to strike a flexible tradeoff between the attainable diversity and multiplexing gains in an efficient but low-complexity manner. Moreover, taking the non-identically distributed characteristics of the backscatter channel into account, we first derive asymptotic BER bounds for both the source and backscatter signals, which are verified by simulation results. Furthermore, we propose a residual- and threshold-based joint error correction mechanism (JECM) and design the near-optimal and low-complexity detector for GSTSK-Backscatter systems. Simulation results validate the analysis and show that our proposed JECM-based detector approaches the optimal detector and balances the BER and complexity performance.

In this paper, generalized space-time block-coded (GSTBC) spatial modulation (SM)-assisted reconfigurable intelligent surface (RIS) structures are designed for three communication scenarios, where RIS works as a transmitter, passive relay as well as active relay. Specifically, the information bits are first mapped to an appropriate GSTBC-SM symbol according to the required transmit rate and diversity order. Then, the mapped GSTBC-SM symbol is transmitted by the based station or the RIS elements based on the communication scenario. Both the signal detector and the upper bounds of average bit error probability are derived for the three cases, which are verified by the simulation results. Simulation results show that the proposed GSTBC-SM-RIS scheme is capable of providing a significant performance gain over the existing SM-RIS scheme, as well as the conventional GSTBC-SM counterpart.

Ambient LoRa backscatter enables battery-free wireless communication with long-range connectivity for the Internet of Things. However, current efforts mainly focus on symbol-level modulation, which trades considerable data rates for long backscatter ranges. To enhance the data rate, this paper presents and prototypes Pacim, which fully explores the potential of long-period LoRa chirps and conveys additional information by varying symbol lengths. Specifically, we propose the chirp interval modulation scheme that modulates multiple data bits in each time interval between two chirp-based anchor symbols. Moreover, we design a twin-chirp cancellation method at the receiver that eliminates the frequency discontinuity within anchor symbols, and propose a fine-grained detection algorithm to measure the arrival time of anchor symbols in the frequency domain. We further propose three reliable methods to improve transmission reliability and analyze the symbol error rate (SER) performance. We also build a hardware prototype and perform comprehensive evaluations. Our experiment results show that Pacim can achieve up to$8.6 \times $throughput gain while keeping long-range, compared with the state-of-the-art ambient LoRa backscatter design.

In this paper, we design and implement a flexible backscatter system, i.e., Flexcatter, which can support various baseband modulation schemes to be compatible with active radios. The key technique employed by Flexcatter is signal emulation, where the tag can vary the reflection coefficient in the time domain to emulate desired baseband signals. To meet the low-power requirements of Flexcatter, we first carefully design a cost-effective impedance network, which can provide up to 16 reflection coefficients for different signal amplitudes and phases. Moreover, we present an effective emulation strategy for desired baseband signals. Next, we introduce the oversampling method in the baseband modulation pipeline to mitigate the effect of emulation errors caused by hardware deficiency. We further build the prototype hardware, including an FPGA platform and the radio frequency (RF) module. Experiment results show that OFDM transmissions from Flexcatter can achieve a throughput of 21.1 Mbps at the backscatter range of 12 m. To the best of our knowledge, we are the first to generate OFDM transmission with 64 subcarriers using low-power RF switches. Our design has the potential to accept other types of existing active radios as the receiver to reduce the deployment cost significantly.

In this paper, we propose a spatial modulation (SM) space time block code (STBC) aided orthogonal time frequency space modulation (SM-STBC-OTFS) system to achieve both high transmission rate and diversity gain in the context of high mobility communication scenarios. Specifically, we apportion the information bits into multiple groups depending on the number of subcarriers and time slots, where each group is modulated by the SM. Next, Alamouti code, orthogonal STBC (OSTBC) and quasi-orthogonal STBC (Q-OSTBC) structures are associated with SM systems. Then the SM-STBC symbols are invoked by OTFS modulation based on different transmission cycle. Finally, block message passing (BMP) detector is designed for the proposed SM-STBC-OTFS systems, which provides significant performance gain in multiple-input and multiple-output (MIMO) configurations. Simulation results show that the SM-STBC-OTFS system relying on the proposed detector is capable of providing considerable bit error rate (BER) performance gains over the OTFS and the MIMO-OTFS systems.

Multiple-input–multiple-output (MIMO) is a promising enabler for massive connectivity of Internet of Things (IoT) devices by offering abundance of spatial degrees of freedom. One of the challenging issues is the excessive overhead induced by users’ channel state information (CSI) feedback, which impedes the gains of MIMO techniques. The popular solution to reduce the overhead is to adopt implicit feedback. However, existing implicit feedback methods mainly rely on either expensive hardware circuits or large antenna separation, making them unaffordable to low-cost and small-factor IoT devices. To circumvent this issue, in this article, a new channel feedback mechanism calledLazyBackis proposed, which jointly calibrates multiple users’ channels by removing the hardware diversity of different users. Furthermore, to ensure up-to-date CSI, LazyBack adopts a channel prediction algorithm to infer channel stability. When the channel varies quickly over time, LazyBack switches back to the explicit feedback mode to obtain the real-time downlink channel. A LazyBack prototype is implemented based on USRPs, and the experimental results show that it provides a$1.7 \times $and$3.1 \times $throughput improvement compared with the IEEE 802.11ac for$4 \times 4$and$8 \times 8$multiuser MIMO (MU-MIMO), respectively.

The Internet of Things (IoT) is envisioned to connect everything, spanning from terrestrial to nonterrestrial terminals, where reliable communication is expected to be allowed in both time-invariant and time-variant wireless channels. Since classic orthogonal frequency-division multiplexing (OFDM) modulation, which has been widely used in both the fourth-generation (4G) and the fifth-generation (5G) cellular systems, is sensitive to high Doppler effect, it is challenging to satisfy the ever-growing demands of future IoT. To circumvent this issue, the orthogonal time–frequency space (OTFS) scheme is proposed, which modulates the information bits in both the delay and the Doppler domains, and exhibits beneficial advantages in both static and high-mobility wireless channel scenarios. In this article, we present a comprehensive overview of OTFS for IoT, including the current transceiver design, the potential benefits, the challenge issues, as well as future design guidelines.

Cloud-based cell-free massive multiple-input multiple-output (CFmMIMO) technology, which exploits a large number of distributed antennas to cooperatively serve multiple users, constitutes an appealing technique for B5G/6G wireless communications. However, the distributed nature of cloud-based CFmMIMO imposes great challenges in analyzing the error probability bounds, and very few efforts have so far been paid to optimize the detector design. In this paper, we try to add a stroke to this blank by analyzing the symbol error rate (SER) and design near-optimal detection algorithms. Specifically, considering non-identical large-scale coefficients and channel estimation errors, we first leverage the pairwise error probability to derive an asymptotic SER bound for uplink cloud-based CFmMIMO systems, which is verified by simulation results. Furthermore, motivated by the concepts of successive interference cancellation (SIC) and error correction mechanism (ECM), we design two distinct types of near-optimal detectors for cloud-based CFmMIMO systems and analyze their complexity and convergence performance. Finally, extensive simulation results show that our proposed SIC and ECM based detectors outperform conventional matched filtering (MF) and minimum mean squred error (MMSE) counterparts. In particular, the MMSE-SIC and MMSE-ECM detectors approach the derived asymptotic bound, and the MF-ECM detector strikes a balance between the SER and complexity in ultra CFmMIMO scenarios.

In this paper, we study the total effective capacity (EC) of single-cluster non-orthogonal multiple access (NOMA) networks and demonstrate the performance gain of single-cluster NOMA over user-paired NOMA and orthogonal multiple access (OMA). Specifically, the exact closed-form expression and an approximate closed-form expression at high signal-to-noise ratios (SNRs), in terms of the total EC, are derived for single-cluster NOMA networks. The derivations reveal that the total EC at high SNRs only relies on the statistical delay requirement of the strongest user and is independent of the other users' delay requirements. Further, we theoretically analyze the total EC differences between single-cluster NOMA and user-paired NOMA/OMA communications and explore the impact of transmit SNR. Simulation results verify the accuracy of analytical results and further reveal that the single-cluster NOMA network achieves a greater gain in terms of the total EC, compared to the conventional OMA, when the number of users increases.

This paper proposes a joint iterative optimization based hybrid beamforming technique for massive MU-MIMO systems. The proposed technique jointly and iteratively optimizes the transmitter precoders and combiners, aiming to approach the global optimum solution for the system sum-rate maximization problem. The proposed technique develops an adaptive algorithm exploiting the stochastic gradients (SG) of the local beamformers and provides low-complexity closed-form solutions. Furthermore, an efficient adaptive scheme is developed based on the proposed adaptive algorithm and the closed-form solutions. The proposed algorithm requires the signal-to-interference-plus-noise ratio (SINR) feedback from each user and a limited size transition vector to be exchanged between the transmitter and receivers at each step to update beamformers locally. Analytic result shows that the proposed adaptive algorithm achieves low-complexity when the array size is large and is able to converge within a small number of iterations. Simulation result shows that the proposed technique is able to achieve superior performance comparing to the existing state-of-art techniques. In addition, the knowledge of instantaneous channel state information (CSI) is not required as the channels are also adaptively estimated with each coherence time which is a practical assumption since the CSI is usually unavailable or have time-varying nature in real-time applications.

In this treatise, we introduce a novel polarization modulation (PM) scheme, where we capitalize on the reconfigurable polarization antenna design for exploring the polarization domain degrees of freedom, thus boosting the system throughput. More specifically, we invoke the inherent properties of a dual polarized (DP) antenna for transmitting additional information carried by the axial ratio (AR) and tilt angle of elliptic polarization, in addition to the information streams transmitted over its vertical (V) and horizontal (H) components. Furthermore, we propose a special algorithm for generating an improved PM constellation tailored especially for wireless PM modulation. We also provide an analytical framework to compute the average bit error rate (ABER) of the PM system. Furthermore, we characterize both the discrete-input continuous-output memoryless channel (DCMC) capacity and the continuous-input continuous-output memoryless channel (CCMC) capacity as well as the upper and lower bounds of the CCMC capacity. The results show the superiority of our proposed PM system over conventional modulation schemes in terms of both higher throughput and lower BER. In particular, our simulation results indicate that the gain achieved by the proposed Q-dimensional PM scheme spans between 10dB and 20dB compared to the conventional modulation. It is also demonstrated that the PM system attains between 54% and 87.5% improvements in terms of ergodic capacity. Furthermore, we show that this technique can be applied to MIMO systems in a synergistic manner in order to achieve the target data rate target for 5G wireless systems with much less system resources (in terms of bandwidth and the number of antennas) compared to existing MIMO techniques.

In this paper, we propose a generalized space-time block coded spatial modulation (GSTBC-SM) scheme for open-loop massive multiple-input and multiple-output (MIMO) downlink communication systems. Specifically, we firstly partition the information bits into multiple groups with each group modulated by the spatial modulation (SM), where the SM symbols are invoked for orthogonal STBC (OSTBC) and quasi-orthogonal STBC (Q-OSTBC) structures. Then, message passing (MP) and block minimum mean square equalization (B-MMSE) detectors are designed for our GSTBC-SM systems, to achieve near-optimal performance with significantly reduced complexity in massive MIMO configurations. Finally, we derive the theoretical average bit error probability (ABEP) of the proposed scheme. The main contribution is that the propose scheme achieves high transmission rate and diversity gain even with small number of radio frequency (RF) chains at the transmitter. Simulation results verify the theoretical derivations and show that the proposed GSTBC-SM scheme provides near 20 dB gain over the conventional GSTBC scheme under massive MIMO configurations.

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.

Generalized index modulation (GIM) which implicitly conveys information by the activated indices is a promising technique for next-generation wireless networks. Due to the prohibitive challenge of bit-to-index combination (IC) mapping optimization, conventional GIM system obtains the bit-to-IC mapping table randomly, which may suffer from some performance loss. To circumvent this issue, we propose a low-complexity graph theory assisted bit-to-IC gray coding for GIM systems by minimizing the average hamming distance (HD) between any two ICs having one different value. Specifically, we decompose and transform the optimization problem into two subproblems using the graph theory, i.e., 1) Select an IC set whose corresponding graph has the minimum degree; 2) Design a bit-to-IC mapping principle to minimize the weight of the selected graph. Low-complexity algorithms are developed to solve the subproblems with a significant reduced complexity. Both simulation and theoretical results are shown that the GIM systems with our proposed mapping table are capable of providing significant performance gains over the conventional counterparts without the need for any additional feedback-link and without extra computational complexity. It is also shown that the proposed bit-to-IC mapping table is straightforward for any GIM systems over generalized fading channels.

The concept of massive spatial modulation (SM) assisted vertical bell labs space-time (V-BLAST) (SM-VBLAST) system [1] is proposed, where SM symbols (instead of conventional constellation symbols) are mapped onto the VBLAST structure. We show that the proposed SM-VBLAST is a promising massive multiple input multiple output (MIMO) candidate owing to its high throughput and low number of radio frequency (RF) chains used at the transmitter. For the generalized massive SM-VBLAST systems, we first derive both the upper bounds of the average bit error probability (ABEP) and the lower bounds of the ergodic capacity. Then, we develop an efficient error correction mechanism (ECM) assisted compressive sensing (CS) detector whose performance tends to achieve that of the maximum likelihood (ML) detector. Our simulations indicate that the proposed ECM-CS detector is suitable both for massive SM-MIMO based point-to-point and for uplink communications at the cost of a slightly higher complexity than that of the compressive sampling matching pursuit (CoSaMP) based detector in the high SNR region.

An index modulation (IM) assisted Discrete Cosine Transform based Orthogonal Frequency Division Multiplexing (DCT-OFDM) with Enhanced Transmitter Design (termed as EDCT-OFDM-IM) is proposed. It amalgamates the concept of Discrete Cosine Transform assisted Orthogonal Frequency Division Multiplexing (DCT-OFDM) and Index Modulation (IM) to exploit the design freedom provided by the double number of available subcarrier under the same bandwidth. In the proposed EDCT-OFDM-IM scheme, the maximum likelihood (ML) detector used for symbol bits and index bits recovering is derived and the sophisticated designing guidelines for EDCT-OFDM-IM are provided. Based on the derived pairwise error event probability, a theoretical upper bound on the average bit-error probability (ABEP) of EDCT-OFDM-IM is provided over multipath fading channels. Furthermore, the maximum peak-to-average power ratio (PAPR) of our proposed EDCT-OFDM-IM scheme is derived and compared to than the general Discrete Fourier Transform (DFT) based OFDM-IM counterpart.

In this paper, Generalized Space-Time Block Coded Spatial Modulation (GSTBC-SM) is proposed for Multiple-Input and Multiple-Output (MIMO) system, which can be extended into an arbitrary even number of Transmit Antennas (TAs). The proposed GSTBC-SM scheme employs the hybrid concepts of Generalized Space-Time Block Coding (GSTBC) and Spatial Modulation (SM) to further exploit the diversity benefits of GSTBC using sparse Radio Frequency (RF) chains. To be more specific, the information bits are divided into Nugroups and each group is modulated by SM scheme. Finally, the Nusymbols are invoked for GSTBC structure. In order to demonstrated the advantages of our proposed GSTBC-SM schemes, the theoretical Average Bit Error Probability (ABEP) of our proposed GSTBC-SM is derived. Both our analytical and simulation results demonstrated that the proposed GSTBC-SM scheme is capable of providing considerable performance gains over the corresponding GSTBC schemes at the same transmit rate associated with the same number of RF chains.

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).

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.

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.

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.

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.