In this paper, a dual deep neural network (DNN) is proposed to simultaneously estimate the central direction-of-arrival (DOA) angle and angular spread of coherently distributed (CD) sources. First, a DNN subnetwork is trained on covariance matrices of received signals from CD sources under different angular distributions to produce central-angle estimates. Meanwhile, the same data, together with the angle labels, are used to train the second subnetwork for angular spread estimation. Because fixed grid points are applied in angular space during DNN training, DOA estimation performance for off-grid sources may be degraded. To address off-grid sources, an interpolation scheme is employed to achieve higher-resolution DOA estimation. The proposed method addresses the angular spread estimation problem in deep-learning-based distributed source estimation, enabling high-accuracy DOA estimation even in large-angular-spread scenarios. Moreover, unlike conventional subspace methods that require a priori knowledge of the source angular distribution or probability density function, the proposed DNN method can estimate multiple types of CD sources simultaneously without such assumptions, owing to its generalization ability. The proposed method has been verified in simulations and realworld scenarios. The proposed method consistently outperforms the state-of-the-art methods with an average RMSE (Root-Mean-Square-Error) reduction of 0.47° under a large angular spread 10°.

Federated Learning (FL) is a promising distributed model training paradigm without the leakage of local data. In wireless communication networks, energy-constrained mobile devices may encounter outage when uploading local gradients to edge server due to fading channels, leading to lost devices and thus performance degradation. Retransmission is a classical mechanism to address the outage issue, which however increases energy consumption and delay of mobile devices. To this end, we propose an adaptive retransmission strategy for wireless FL, where the server decides whether to request retransmission according to the number of transmission outage, and devices decide whether to respond based on their channel conditions and remaining energy. A convergence analysis is conducted for the proposed FL framework. Based on this, we aim to minimize the convergence error by jointly optimizing the retransmission decisions and the allocation of retransmission slots, under the devices’ long-term energy budget and the communication resource constraints. Since the decisions are coupled across different iterations, we design a Lyapunov-based online algorithm to solve this problem. Through experiments, we find that the effect of outage is trivial sometimes and retransmission is not always necessary in FL because channel conditions and data distributions affect the degree of the retransmission gain, and that the retransmission strategy benefits wireless FL.

Federated learning (FL) is a promising distributed machine learning paradigm to train a machine learning model without the leakage of local data. However, as the sizes of models are increasing while Internet of Things (IoT) devices are heterogeneous and capability-limited, FL faces performance bottleneck. In this article, we propose a cooperative device-to-device (D2D)-based partial training scheme for wireless FL. Specifically, the IoT devices in each cluster extract and train the nonoverlapping submodels from the global model, and the trained submodels are transmitted to the cluster head (CH) to form a whole local model via D2D links. Then the CHs upload the local models to the server for global aggregation. We first conduct the convergence analysis for the proposed wireless FL scheme. Then a joint optimization problem is formulated to minimize the average delay by the optimization of model division, device selection, and bandwidth allocation. An efficient algorithm is proposed to solve this nonconvex problem. Comprehensive experiments verify the efficiency of the proposed scheme.

This paper proposes a sparse Bayesian learning (SBL)-based channel estimation algorithm for orthogonal time frequency space (OTFS) systems. By introducing the basis expansion model (BEM), we reconstruct the original high-dimensional time-varying channel matrix to a low-dimensional weighted combination of basis functions. Then, we propose a two-stage SBL model that estimates the coefficients of the basis functions by leveraging the sparseness of the channel in the delay-Doppler (DD) domain. Further, an iterative decision feedback scheme is proposed to refine the accuracy of channel estimation and symbol detection. Simulation results show that even with a few iterations, the accuracy of channel estimation can be significantly improved. Besides, the proposed data-aided two-stage SBL-based OTFS channel estimation algorithm outperforms the existing OTFS receivers exactly in terms of estimation mean square error and bit error rate.

The design and performance analysis of underwater acoustic sensor networks (UASNs) have attracted intensive attention. In this study, we present a theoretical framework to evaluate the performances of UASNs. In contrast to existing methods, we applied a new communication protocol model to characterize the channel interference by taking into account the unique characteristics of underwater acoustic channels, namely, the concurrent transmission opportunities owing to the large propagation delays of acoustic signals. Using the communication protocol model, we investigated the collision regions of a communication pair and then derived a closed-form expression for the successful packet transmission probability. Moreover, we considered practical networks where each node is equipped with a limited data buffer to store its generated packets. Using collision analysis and a stationary buffer distribution, we derived the closed-form expression of system performance in terms of the throughput and the packet queue delay. Finally, we validated the accuracy of our analysis via extensive simulation results. The impacts of various network parameters on the system performance are also evaluated.

High-speed train (HST) communication plays a crucial role in providing reliable data services to passengers inside the trains. However, due to the train’s high mobility, a fast time-varying channel generally exists between the static BS and high-speed users, resulting in severe communication performance degradation. To address this issue, we propose in this paper an intelligent reflecting surface (IRS)-aided HST relaying system, where an IRS is integrated with the relay to aid the data transmission from the BS to the relay. Specifically, an optimization problem is formulated to maximize the received signal-to-noise ratio (SNR) at the relay by jointly optimizing the active transmit beamforming at the BS, the active receive beamforming at the relay, and the passive reflect beamforming at the IRS. To solve this problem, we first decouple it into two simpler sub-problems by leveraging the low-dimensional channel decomposition of the high-dimensional BS-relay channel matrix, and then solve them in closed-form with low complexity. Additionally, an efficient transmission protocol tailored for HST communication systems is proposed to implement channel estimation and beam tracking with low complexity. Simulation results verify the performance gains of the proposed IRS-aided HST relaying system, compared with the traditional relaying scheme without IRS and other benchmark schemes.

Federated learning (FL) is a promising distributed learning framework where distributed clients collaboratively train a machine learning model coordinated by a server. To tackle the stragglers issue in asynchronous FL, we consider that each client keeps local updates and probabilistically transmits the local model to the server at arbitrary times. We first derive the (approximate) expression for the convergence rate based on the probabilistic client selection. Then, an optimization problem is formulated to trade off the convergence rate of asynchronous FL and mobile energy consumption by joint probabilistic client selection and bandwidth allocation. We develop an iterative algorithm to solve the non-convex problem globally optimally. Experiments demonstrate the superiority of the proposed approach compared with the traditional schemes.

In this paper, we investigate a practical structure of reconfigurable intelligent surface (RIS)-based double spatial scattering modulation (DSSM) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. A suboptimal detector is proposed, in which the beam direction is first demodulated according to the received beam strength, and then the remaining information is demodulated by adopting the maximum likelihood algorithm. Based on the proposed suboptimal detector, we derive the conditional pairwise error probability expression. Further, the exact numerical integral and closed-form expressions of unconditional pairwise error probability (UPEP) are derived via two different approaches. To provide more insights, we derive the upper bound and asymptotic expressions of UPEP. In addition, the diversity gain of the RIS-DSSM scheme was also given. Furthermore, the union upper bound of average bit error probability (ABEP) is obtained by combining the UPEP and the number of error bits. Simulation results are provided to validate the derived upper bound and asymptotic expressions of ABEP. We found an interesting phenomenon that the ABEP performance of the proposed system-based phase shift keying is better than that of the quadrature amplitude modulation. Additionally, the performance advantage of ABEP is more significant with the increase in the number of RIS elements.

With the rapid development of the Internet of Things (IoT), there are a dramatically increasing number of devices, leading to the fact that only using terrestrial infrastructure can hardly provide high-quality services to all devices. Due to their flexibility, maneuverability, and economy, unmanned aerial vehicles (UAVs) are widely used to improve the performance of IoT networks. UAVs can not only provide wireless access to IoT devices in the absence of a terrestrial network but can also perform rich IoT services and applications such as video surveillance, cargo transportation, pesticide spraying, and so forth. However, due to the high complexity, dynamics, and heterogeneity of the UAV-assisted IoT networks, growing attention has focused on using artificial intelligence (AI)-based methods to optimize, schedule, and orchestrate UAV-assisted IoT networks. In this article, we comprehensively analyze the impact of applying advanced AI architectures, models, and methods to different aspects of UAV-assisted IoT networks, including key IoT technologies, tasks, and applications. In addition, this article also explores challenges and discusses potential research directions of AI-enabled UAV-assisted IoT networks.

The ocean contains abundant resources and has high-scientific and economic values. Increasing underwater communication scenarios stimulates many research efforts on underwater wireless ad hoc networks. Underwater acoustic communication technology supports medium to long-range wireless communication. However, it suffers from low-transmission data rates and long link delays. This article introduces an acoustic-radio cooperation network (ARCNet) model for more efficient maritime information transmission. Under this ARCNet, we propose a new radio-acoustic opportunistic hybrid (RAOH) routing protocol composed of a neighbor discovery mechanism and a hybrid routing strategy. In the neighbor discovery stage, we explore the surface radio links to aid in selecting the shortest delay path between an underwater node and the surface nodes. In the route establishment stage, we combine the advantages of opportunistic routing and on-demand routing and design an opportunistic hybrid routing strategy, which improves the success rate of data forwarding and reduces the time spent on route establishment. Simulation results show that the proposed RAOH protocol outperforms traditional terrestrial and underwater routing strategies in routing response speed, packer delivery rate, throughput, end-to-end delay, and energy efficiency.

To achieve huge passive/reflect beamforming gain by intelligent reflecting surface (IRS), channel state information (CSI) on IRS is essential but challenging to obtain in IRS-aided systems due to the lack of signal transmitting/receiving/processing capabilities of IRS passive elements as well as the massive channel coefficients to be estimated. In this paper, we propose a new channel estimation scheme using the full-duplex technique for estimating the CSI of all users’ cascaded channels in an IRS-aided multiuser communication system. Specifically, one arbitrarily selected reference user is equipped with the full-duplex technique in the channel estimation phase to estimate the element-wise squared channel between it and the IRS via its pilot transmission together with the IRS’s time-varying reflection. Meanwhile, the base station (BS) and non-reference users estimate the CSI of their respective IRS-reflected channels, based on which the users-IRS-BS cascaded channels of the non-reference users are recovered by utilizing the fact that the IRS-BS channel is common to all users. As such, the proposed channel estimation scheme significantly reduces the required minimum training overhead for estimating all users-IRS-BS cascaded channels, which is determined by the number of IRS reflecting elements only, and is irrelevant to that of IRS-served users. Numerical results demonstrate the effectiveness of the proposed scheme.

With the development of Internet-of-Things (IoT), we witness the explosive growth in the number of devices with sensing, computing, and communication capabilities, along with a large amount of raw data generated at the network edge. Mobile (multi-access) edge computing (MEC), acquiring and processing data at network edge (like base station (BS)) via wireless links, has emerged as a promising technique for real-time applications. In this paper, we consider the scenario that multiple devices sense then offload data to an edge server/BS, and the offloading throughput maximization problems are studied by joint radio-and-computation resource allocation, based on time-division multiple access (TDMA) and non-orthogonal multiple access (NOMA) multiuser computation offloading. Particularly, we take the sequence of TDMA-based multiuser transmission/offloading into account. The studied problems are NP-hard and non-convex. A set of low-complexity algorithms are designed based on decomposition approach and exploration of valuable insights of problems. They are either optimal or can achieve close-to-optimal performance as shown by simulation. The comprehensive simulation results show that the sequence-optimized TDMA scheme achieves better throughput performance than the NOMA scheme, while the NOMA scheme is better under the assumptions of time-sharing strategy and the identical sensing capability of the devices.

Reflection modulation based on reconfigurable intelligent surface (RIS) is considered to be a promising information transfer mechanism, which does not require any additional radio frequency chains. However, existing reflection modulation schemes consider manipulating the ON/OFF states of RIS elements, which may result in some power loss. In this paper, we propose a new scheme, called RIS-aided quadrature reflection modulation (RIS-QRM), for better utilizing the reflection power in RIS-aided downlink multiple-input single-output (MISO) wireless systems. To this end, RIS-QRM partitions the RIS elements into two subsets in order to reflected the incident signals towards two orthogonal directions by using passive beamforming and encodes its local data onto the two partitions. A closed-form expression for the unconditional pairwise error probability of RIS-QRM in Rician fading is derived assuming maximum-likelihood detection at the receiver. Moreover, a low-complexity detection method that decouples the joint search of the constellation symbol and the RIS element partition is proposed. Computer simulation results corroborate the effectiveness of RIS-QRM and validate the analytical results. It is shown that RIS-QRM improves the error performance of the additional bits delivered by the RIS without deteriorating the error performance of the bits modulated on the constellation symbol, as compared to ON/OFF-based RIS-aided schemes.

Reflection modulation based on reconfigurable intelligent surface (RIS) is considered to be a promising information transfer mechanism that conveys information by reconfiguring incident electromagnetic waves without using radio frequency chains. Existing reflection modulation schemes propose to convey additional information by manipulating the ON/OFF states of reflecting elements, which underuse the potential of the RIS. In this paper, we first propose a new reflection modulation scheme, called RIS-based quadrature reflection modulation (RIS-QRM), to achieve joint passive beamforming and information transfer for RIS-aided single-input single-output wireless communications by assuming that the direct link between the transceivers is blocked. We then extend the RIS-QRM scheme to the multiple-input single-output setting with the direct link between transceivers being taken into account. In this case, an optimization problem is formulated to jointly optimize the active beamforming and the phase shifts of the RIS in order to improve the received signal power at the user. Computer simulation results corroborate the effectiveness of the RIS-QRM scheme. It is shown that in contrast to ON/OFF-based schemes, the RIS-QRM scheme is able to improve the error performance of the additional bits delivered by the RIS without deteriorating that of the bits carried by the constellation symbols.

The long propagation delay presents a nonnegligible impact on medium access control (MAC) in underwater acoustic networks (UANs), leading to the coupling of spatial propagation delay and transmission time in determining frame collisions at receivers. To this end, this article first reveals the space-time coupling relationship for collision-free transmissions. We find that the interference region for simultaneous transmissions is an annulus, which is much smaller than the whole transmission range. Thus, UANs have more spatial multiplexing opportunities than terrestrial radio networks. By deriving the collision probabilities of the ALOHA-based protocols, we show that the annulus interference region in UANs provides potential to improve random access protocols. We further derive the heuristics for optimal scheduling for periodical transmissions under space-time coupling, which is formulated as a mixed-integer linear programming problem. The obtained heuristics are then used to design low-complexity algorithms, which are verified by simulation results. Our proposed algorithms can achieve the optimal scheduling in star networks and outperform the existing MAC in mesh networks.

In this letter, we propose a novel sparse Bayesian learning (SBL) algorithm using Generalized Double Pareto (GDP) prior to enhance the performance of direction of arrival (DOA) estimation for complex signals. Firstly, a novel hierarchical prior model is formulated for complex signals so that the marginal distribution of the complex signal is the GDP distribution, which promotes the sparsity more significantly than conventional priors used in SBL. Secondly, a novel fixed-point update rule of the hyperparameters is derived to speed up the convergence of the proposed SBL. Finally, a refined DOA searching method is also derived to tackle the grid-mismatch problem. Simulation results demonstrate the improved accuracy and efficiency of the proposed algorithm in low SNR and limited snapshots scenarios compared with other SBL-based DOA estimation methods.

Recent research on reconfigurable intelligent surfaces (RISs) suggests that RISs can perform passive beamforming and information transfer (PBIT) simultaneously via smart reflections. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation (RPM) to achieve PBIT, where the joint active and passive beamforming is carefully designed by taking into account the communication outage probability. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS under the assumption that the RIS's state information is statistically known by the AP, and propose a high-quality suboptimal solution based on the alternating optimization technique. Moreover, a closed-form expression for the asymptotic outage probability of the proposed scheme in Rician fading is derived. The achievable rate of the proposed scheme is also investigated under the assumption that the transmitted symbols are drawn from a finite constellation. Simulation results validate the effectiveness of the proposed scheme and reveal the effect of various system parameters on the achievable rate. It is shown that the proposed scheme outperforms, in terms of achievable rate, the conventional RIS-assisted system without information transfer.

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the energy and/or spectrum efficiency of future wireless communications. In this paper, we propose a transmission protocol for the wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system to execute channel estimation and passive beamforming simultaneously. A new channel estimation method is proposed to progressively resolve the channel state information (CSI) over the training symbols, based on which the passive beamforming at the RIS is optimized to improve the channel gains on the data tones in the remaining training symbols. Based on the incomplete CSI, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different subcarriers and different receive antennas. Moreover, we propose a low-complexity algorithm to find a high-quality solution for the formulated problem. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods.

Recent considerations for reconfigurable intelligent surfaces (RISs) assume that RISs can convey information by reflection without the need of transmit radio frequency chains, which, however, is a challenging task. In this paper, we propose an RIS-enhanced multiple-input single-output system with reflection pattern modulation, where the RIS can configure its reflection state for boosting the received signal power via passive beam-forming and simultaneously conveying its own information via reflection. We formulate an optimization problem to maximize the average received signal power by jointly optimizing the active beamforming at the access point (AP) and passive beamforming at the RIS for the case where the RIS's information is statistically known by the AP. Since the formulated problem is non-convex and thus difficult to solve optimally, we propose an efficient algorithm based on the alternating optimization technique to find a high-quality solution. Simulation results validate the effectiveness of the proposed reflection pattern modulation and beamforming design. It is shown that the proposed scheme outperforms the conventional RIS-assisted system with full RIS reflection in terms of achievable rate performance.

Internet of Underwater Things (IoUT) is regarded as the network of interconnected smart underwater objects that enables marine monitoring and exploration. Underwater acoustic sensor networks (UASNs) is an ideal network infrastructure to support IoUT. Usually, network-level analysis requires a simplified model for channel interference. Existing research on UASNs usually applies channel interference models from terrestrial wireless networks. Its effectiveness is questionable. In this article, we investigate two specific features of acoustic channels and propose a new channel interference model. First, based on the fact that acoustic signals have inconsistent transmission range in vertical and horizontal directions, we propose an ellipsoid sensing model and derive the probability that a communication pair will have an effective link. Second, we investigate the receiving time inconsistency in time-slotted acoustic networks and derive the analytical probability of collision-free transmission. Finally, a new protocol model is proposed to describe the acoustic channel interference. Its theoretical throughput, in terms of delivery rate, is also presented. Numerical examples illustrate the effectiveness of the theoretical analysis.

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the coverage, throughput, and energy/spectrum efficiency of future wireless communications. In this paper, we propose a new transmission protocol for wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) communication systems, where each transmission frame is divided into multiple sub-frames to execute channel estimation simultaneously with passive beamforming. As the training symbols are discretely distributed over multiple sub-frames, the channel state information (CSI) associated with RIS cannot be estimated at once. As such, we propose a new channel estimation method to progressively estimate the associated CSI over consecutive sub-frames, based on which the passive beamforming at the RIS is fine-tuned to improve the achievable rate for data transmission. In particular, during the channel training, the RIS plays two roles of embedding training reflection states for progressive channel estimation and performing passive beamforming for data transmission on the data tones. Based on the estimated CSI in each sub-frame, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different sub-carriers and different receive antennas. As the formulated problem is non-convex and thus difficult to solve optimally, we propose two efficient algorithms to find high-quality solutions. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods. It is shown that the proposed joint channel estimation and passive beamforming scheme is able to drastically improve the average achievable rate and reduce the delay for data transmission as compared to existing schemes.

Exploiting the idle computation resources of mobile devices in mobile edge computing (MEC) system can achieve both channel diversity and computing diversity as mobile devices can offload their computation tasks to nearby mobile devices in addition to MEC server embedded access point (AP). In this paper, we propose a non-orthogonal multiple-access (NOMA)-aided cooperative computing scheme in a basic three-node MEC system consisting of a user, a helper, and an AP. In particular, we assume that the user can simultaneously offload data to the helper and the AP using NOMA, while the helper can locally compute data and offload data to the AP at the same time. We study two optimization problems, energy consumption minimization and offloading data maximization, by joint communication and computation resource allocation of the user and helper. We find the optimal solutions for the two non-convex problems by some proper mathematical methods. Simulation results are presented to demonstrate the effectiveness of the proposed schemes. Some useful insights are provided for practical designs.

In this paper, a novel opportunistic spectrum sharing scheme, based on orthogonal frequency division multiplexing with index modulation (OFDM-IM), is proposed for cognitive radio (CR) networks. In the considered OFDM-IM based CR (OFDM-IM-CR) model, the primary transmitter (PT) communicates with the primary receiver with the aid of an amplify-and-forward (AF) relay by transmitting OFDM-IM signals. Meanwhile, the secondary transmitter (ST) passively senses the spectrum and transmits its own information over those inactive subcarriers of the primary network to the secondary receiver if the signal-to-noise ratio of the PT$\to $ST link is above a predefined threshold; otherwise, the ST stays in silent mode. Two different types of maximum-likelihood (ML) detectors are designed for the primary network, based on the knowledge of either the estimated channel state information or the statistical channel information of the secondary network. A complexity-reducing method, which is applicable to both types and achieves near optimal performance, is further proposed. To evaluate the performance, a tight upper bound on the bit error rate (BER) is derived, assuming the first type of ML detection. Simulation results corroborate the analysis and show that OFDM-IM-CR has the potential of outperforming OFDM-CR and OFDM-IM-AF in terms of BER with higher spectral efficiency.

In this paper, a novel dual-hop spatial modulation (SM) relay network is proposed, in which the relay is enabled to transmit its own information while forwarding the SM signal from the source over the same frequency band. Both decode-and-forward (DF) and amplify-and-forward (AF) relaying protocols are studied. For DF relaying, the relay embeds its own information into one out of two spatial dimensions of quadrature SM. For AF relaying, the relay activates only one transmit antenna to forward the received signal, and encodes its own information by the index of the active antenna. Under both relaying protocols, the throughput of the network is increased without consuming extra power. The bit error rate (BER) performance is analyzed, and tight upper bounds on the BERs are derived in closed form for the source and relay over Rayleigh fading channels for both DF and AF relaying. The performance analysis is verified by Monte Carlo simulations, showing that the proposed scheme provides a simple yet effective solution to the implementation of SM relay networks in which the relay has its own information to be transmitted.

Orthogonal frequency division multiplexing (OFDM) is widely considered as a promising technique for vehicular communications. In current OFDM-based vehicular communications systems, a portion of tones are dedicated for pilot symbols and transmitted along with the data-bearing tones to estimate residual frequency error and/or channel state information. However, these pilot symbols themselves do not carry any information, thus inevitably degrading the system spectral efficiency (SE). Motivated by the concept of index modulation, in this paper, we propose a novel pilot insertion technique to enhance the SE, in which the pilot positions are selected according to extra information bits. Specifically, three different types of pilot position selection schemes, which result in equal-spaced, unequal-spaced, and hybrid pilot placements, are studied, and the corresponding pilot position detection methods are designed, where the pilots can be used for either carrier phase tracking or channel estimation purpose. Computer simulation results corroborate the advantages of the proposed technique in terms of bit error rate.

To enjoy advantages of both the single-carrier (SC) transmission and the generalized spatial modulation (GSM), a hybrid architecture of SC-GSM has been developed in the literature. However, low-complexity demodulation for SC-GSM signals with near-optimal performance is challenging, especially in soft decision making. To this end, based on the deterministic sequential Monte Carlo (SMC) principle, we propose two low-complexity demodulators for the SC-GSM system with zero-padding (ZP) in broadband channels. By exploiting the natural triangular structure of the multi-path channel to apply SMC sampling, a vector-level SMC demodulator without the need of the sequential construction process is proposed, significantly saving the computational burden. To lower the required computation during the sampling and weight updating processes of the vector-level SMC demodulator, a symbol-level SMC demodulator is further proposed, which takes samples from the expanded constellation including the origin. In particular, an efficient online legality check scheme, which eliminates illegal samples during each sampling step, is integrated into the symbol-level SMC demodulator to avoid error propagation and save the computational burden dramatically. Both proposed demodulators produce soft outputs, which can be used for soft demodulation in coded systems, especially for turbo receivers. The simulation results verify the near-optimal performance of the two proposed demodulators in the uncoded and coded SC-GSM system with ZP, respectively.

In this paper, we develop a secure non-orthogonal multiple access (NOMA)-based two-way relay network, in which two users wish to exchange their NOMA signals via a trusted relay in the presence of an eavesdropper. To ensure secure communications, the relay not only forwards confidential information to the legitimate users but also keeps emitting jamming signals all the time to confuse any potential eavesdropper. Specifically, we equip the relay and each user with the full-duplex technique in the multiple-access phase to combat the eavesdropping and improve the data transmission efficiency, respectively. Different decoding schemes based on the successive interference cancellation (SIC) are proposed for the legitimate users, relay, and eavesdropper. Closed-form expressions for the achievable ergodic secrecy rates of all data symbols are derived, validated by the excellent fitting to the computer simulation results for our proposed network.

In this paper, we develop a non-orthogonal multiple access (NOMA)-based two-way relay network with secrecy considerations, in which two users wish to exchange their NOMA signals via a trusted relay in the presence of single and multiple eavesdroppers. To ensure secure communications, the relay not only forwards confidential information to the legitimate users but also keeps emitting jamming signals all the time to degrade the performance of any potential eavesdropper. Moreover, we equip the relay and each user with the full-duplex technique in the multiple-access phase to combat the eavesdropping and improve the data transmission efficiency, respectively. We propose different decoding schemes based on the successive interference cancellation for the legitimate users, relay, and eavesdroppers. Closed-form expressions for the achievable ergodic secrecy rates of all data symbols under both single- and multiple-eavesdropper cases are derived, validated by the excellent fitting to the computer simulation results for our proposed network.

As an emerging multi-carrier transmission scheme, multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) conveys information through multiple distinguishable constellations (or modes, alternatively) and their full permutations, increasing the spectral efficiency of OFDM-IM and classical OFDM. However, both MM-OFDM-IM and its extension, that is, MM-OFDM with in-phase/quadrature index modulation (MM-OFDM-IM-IQ), cannot provide any transmit diversity gain, which may be critical for ultra-reliable communications. In this paper, aiming at enhancing the diversity gain of MM-OFDM-IM(-IQ) schemes, coordinate interleaved (CI-)MM-OFDM-IM and linear constellation precoded (LCP-) MM-OFDM-IM-IQ are proposed, both of which achieve a diversity order of two without loss of spectral efficiency. The optimal rotation angle for CI-MM-OFDM-IM and the optimal precoding matrix for LCP-MM-OFDM-IM-IQ, in the sense of maximizing their coding gains, are derived in closed form. Computer simulations corroborate the advantages of the proposed schemes in terms of diversity and bit error rate performance toward next-generation wireless networks.

In this paper, we propose an index modulated orthogonal frequency division multiplexing spread spectrum (IM-OFDM-SS) scheme, which combines the techniques of SS and IM under the framework of OFDM. In this scheme, the information bits are jointly conveyed by the indices of spreading codes and the conventional$M$-ary modulated symbols. A low-complexity maximal ratio combining (MRC) detector is designed, in which the receiver first detects the spreading codes and then de-spreads and demodulates the symbols. The bit error rate (BER) performance of IM-OFDM-SS systems in the presence of channel estimation errors is analyzed. An upper bound and approximate average bit error probability associated with maximum-likelihood and MRC detection, respectively, are derived. Subsequently, we extend the idea of IM-OFDM-SS to multi-code and multi-user scenarios, proposing generalized (G-)IM-OFDM-SS and IM-based multi-carrier code division multiple access (IM-MC-CDMA), respectively. Simulation results verify the analyses and show that the proposed IM/ GIM-OFDM-SS outperforms the existing OFDM-IM and OFDM-SS schemes significantly. In addition, the IM-MC-CDMA exhibits a lower BER than MC-CDMA at the same SEs for either multi-user or single-user detection.

Multiple-input multiple-output orthogonal frequency division multiplexing with index modulation (MIMO-OFDM-IM), which provides a flexible trade-off between spectral efficiency and error performance, is recently proposed as a promising transmission technique for energy-efficient 5G wireless communication systems. However, due to the dependence of subcarrier symbols within each subblock and the strong interchannel interference, it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose a low-complexity detector based on the sequential Monte Carlo (SMC) theory for the detection of MIMO-OFDM-IM signals. The proposed detector, which draws samples based on the importance weights at the subblock level, achieves near-optimal error performance with considerably reduced computational complexity. Simulation and numerical results in terms of bit error rate (BER) and number of complex multiplications (NCM) corroborate the superiority of the proposed detector.

This paper proposes a novel generalised pre-coding aided spatial modulation (GPSM) scheme, called generalised virtual spatial modulation (GVSM), which performs index modulation on the virtual parallel channels obtained by the singular value decomposition (SVD) of multiple-input multiple-output (MIMO) channels. To improve the diversity performance, the coordinate interleaving technique is further introduced into GVSM, resulting in so-called coordinate interleaved GVSM (CI-GVSM), in which the real and imaginary parts of two complex symbols are interleaved and transmitted over strong and weak eigen-modes. A closed-form upper bound on the average bit error probability (ABEP) applicable to both GVSM and CI-GVSM is derived, followed by the asymptotic performance analysis that reveals the error floor and diversity order under imperfect and perfect channel estimation, respectively. Numerical and simulation results corroborate our analysis and show that the proposed GVSM and CI-GVSM outperform the existing GPSM schemes at the same spectral efficiency.

Due to the low speed of sound in water, the underwater acoustic (UWA) signal suffers obvious time scaling when relative motion exists between the transceivers. In general, given a single scale, the scaling-induced distortion can be rectified by a resampling operation. However, the scaling coefficients of the channel paths may be different from each other, especially in shallow water. In recent years, a multi-scale multi-lag (MSML) model has been used to describe the high speed mobile UWA channels in the literature. In this paper, we analyze the performance of OFDM system under this model. Considering the resalmper as a part of the channel, we get a an equivalent MSML channel. We first derive the sub-channel gains in closed form, and then get the analytical expression of the average channel gains and inter-carrier interference (ICI) under given statistics of the channel paths. Finally, we discuss the average achievable rate of the system and get a lower bound of that in closed form.

In order to solve the problem of correlated source number estimation under noisy background, a generalized Khatri-Rao products approach based on Gerschgorin radii is proposed in this paper. Generalized Khatri-Rao products of the received data are computed by weighted summation of the rows of Khatri-Rao product of received data on a uniform linear array, and its element sequence is readjusted. The reconstruction of generalized Khatri-Rao product works like spatial smoothing technique. By exploring the second-order statistical property of the generalized Khatri-Rao products auto correlation matrix, a correlated source detection criterion based on Gerschgorin radii is deduced. Simulation results validate the efficiency and robustness of the proposed method.

Recently, a new multiple-input multiple-output (MIMO) transmission scheme termed as receive spatial modulation (RSM) is proposed, which activates a subset of receive antennas to carry additional information bits through the index domain. In this paper, a novel variant of RSM, which is referred to as enhanced RSM (ERSM), is proposed to further improve the system performance, especially in high-mobility scenarios. For ERSM, the log-likelihood ratio (LLR) and maximum likelihood (ML) detectors are developed, which exhibit very low implementation complexity. With the proposed ML detector, the ERSM is analyzed using different performance metrics, including the asymptotic average bit error probability (ABEP), the exact coding gain, and the achievable rate. Both the analysis and simulation results corroborate the superiority of the proposed scheme.

In this paper, we develop a non-orthogonal multiple access (NOMA)-based multi-pair two-way relay (TWR) network, in which a rate splitting scheme and a successive group decoding strategy are employed. By exploiting the interference signals received from neighbor users with the leverage of the full-duplex technique, we enhance the decoding ability of each user and further achieve an effective multiuser interference management for the network. We propose different decoding strategies for different types of nodes by processing the received signals with only local incoming channel state information in different manners. Moreover, under the limited group decoding size, each individual node decodes its own desired messages along with a fraction of the interference successively. We further investigate the joint uplink and downlink fair rate allocation problem based on the max-min criterion, and the solution to which also contains the optimal group decoding schedule. Simulation results in terms of ergodic rate and outrage probability corroborate the superiority of our NOMA-based multi-pair TWR network over the OMA-based counterpart.

Due to the interchannel interference and the varying active antennas in generalized spatial modulation (GSM), it is challenging to detect the transmitted data effectively while imposing low computational burden to the receiver. In this paper, we propose a novel algorithm based on the K-best sphere decoding for the soft detection of the GSM signal, which performs the breadth-first tree search in two stages sequentially. By exploiting the null space of the GSM channel, the inactive antenna searching is carried out antennawise in the first stage, in which a recursive algorithm for updating the QL decomposition (a decomposition of a matrix into a product of an orthogonal matrix and a lower triangular matrix) is proposed to calculate the branch metrics and an effective examination scheme is developed to prune those illegal or repetitive child nodes. Based on the available QL decomposition structure that has been updated recursively during the first stage, the second stage of the proposed detector is then readily concatenated to search for the modulated symbols carried on the active antennas in a one-by-one manner. Moreover, owing to the soft-input soft-output nature, the proposed detector can be applied in a turbo decoder for a coded GSM system. Both complexity analysis and simulation results corroborate the superiority of the proposed soft detector for the GSM system.

Generalized spatial modulation (GSM) is a relatively new multi-input multi-output transmission technique that enables a flexible trade-off between the achievable transmission rate and the cost of radio frequency chains. However, due to the constraint of transmit antenna combination and the variation of interchannel interference, the efficient low-complexity demodulation of GSM signals is challenging, especially when soft demodulation is needed. In this paper, we propose two low-complexity algorithms based on the deterministic sequential Monte Carlo (SMC) technique for the demodulation of GSM. The type-I SMC demodulator, which uses the conventional successive interference cancellation as the kernel and draws antenna-wise samples from the extended constellation, is proposed for the overdetermined GSM system. The type-II SMC demodulator, which consists of two stages and uses the orthogonal matching pursuit as the kernel in the first stage, is proposed for the underdetermined GSM system. A key component in both algorithms is an efficient online scheme to eliminate the illegal samples during the sampling process. Both proposed algorithms achieve near-optimal performances with complexity linear in terms of the antenna size. Moreover, owing to their soft-input soft-output nature, they can be employed in a turbo receiver for a coded GSM system.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for future wireless communications as it results in high spectral efficiency. NOMA differs from recently adopted orthogonal multiple access techniques in that it superimposes multiple data flows at the same time and same frequency band but with different power levels, and resorts to the successive interference cancellation (SIC) for data decoding. This paper studies NOMA in a dual-hop decode-and-forward (DF) relaying where two relays cooperate to deliver messages to the destination. Specifically, the achievable sum-rate is analyzed and a optimal power allocation (PA) scheme is designed for the proposed system. Performance comparison with the two conventional cooperative schemes that rely on the maximal ratio combining (MRC) and the max-min criterion, respectively, is also made. Comparison results reveal the advantages of the proposed scheme on not only the sum-rate, but also the energy efficiency.

The redundant design and dynamic nature of traffic raise an energy inefficiency issue in communication networks. We exploit the selfishness of both users and the network to schedule cooperatively the idle links and nodes into sleep to save energy. We first formulate the sleep scheduling problem from a perspective of routing, and then propose a greedy algorithm to solve the problem. To reduce the complexity of centralized computation, we further propose a user-network cooperation-based mechanism, where the network publishes a proportionally weighted cost-sharing rule related to energy consumption, while the users selfishly choose their routes with the least cost accordingly. The proposed cooperation mechanism attracts users to aggregate their traffic on fewer links and nodes. The network then simply puts the idle links and nodes into sleep. Selfish routing behaviors are modeled by an α-approximate routing game, where the α factor is adopted to consider the energy consumption, packet losses, and delay during re-routing. We prove the equilibrium existence, convergence, and convergence speed of the best responses, and evaluate the lower bound performance in terms of price of anarchy with further improvement by an advertisement method. Distributed algorithms based on the best responses are also developed to implement the cooperative mechanism. Simulation results over network instants from SNDlib show that our game-based algorithms outperform the greedy and heuristic centralized algorithms in saving energy.

Frequency domain pilot multiplexing technique (FDPMT) has recently emerged as an appealing technique for channel estimation in single-carrier frequency domain equalization systems as it achieves high spectral efficiency at the expense of tiny signal distortion. In FDPMT, pilot positions are dynamically selected to achieve a relatively low level of signal distortion, rendering pilot position detection (PPD) indispensable at the receiver. In this letter, by exploiting both the prior known pilots and the statistical information of the data tones, we propose a novel PPD scheme based on the maximum-likelihood criterion. Simulation results show that our proposed PPD scheme significantly improves the PPD accuracy and outperforms the existing counterparts in terms of bit error rate.

Orthogonal frequency division multiplexing (OFDM) is promising for underwater acoustic (UWA) communications as it is robust against large delay spread. However, OFDM suffers from intercarrier interference (ICI) caused by the serious Doppler effect in UWA channels. Attentive to the property that the ICI is mainly contributed by the adjacent subcarriers in UWA channels, in this paper, we propose a novel low-complexity frequency-domain equalizer to combat the ICI for OFDM UWA communications. Specifically, the coefficients of the equalizer are obtained by using the nor- malized gradient algorithm which is based on the minimum symbol error rate (MSER) criterion. The proposed equalizer uses very few (typical 3) FIR taps to mitigate the ICI for each subchannel, yet achieves better SER performance than the conventional equalizer based on the minimum mean square error (MMSE) criterion.

Existing minimum-symbol-error-rate equalizers were derived based on the symbol-error-rate objective function. Due to the complexity of the objective function the derivation is not straightforward. In this paper we present a new approach to derive the minimum-symbol-error-rate adaptive equalizers. The problem is formulated as minimizing the norm between two subsequent parameter vectors under the constraint of symbol-error-rate minimization. The constrained optimization problem then is solved with the Lagrange multiplier method, which results in an adaptive algorithm with normalization. Simulation results show that the proposed algorithm outperforms the existing adaptive minimum-symbol-error-rate equalizer in convergence speed and steady-state performance.

The model of memoryless Gaussian channel with deterministic interference are common in various engineering applications. In this letter we propose a non-data-aided algorithm for joint measurement of the interference level and the noise power. The algorithm applies an iterative approach based on the maximum likelihood criterion. The Cramer-Rao bound (CRB) is also derived as the performance benchmark. Simulation results show that the proposed approach has fast convergence speed and its measurement performance is almost identical to the CRB.

This paper proposed an ESPRIT based 2D joint direction and polarization estimation algorithm that does not require a separate pairing algorithm, thus does not suffer from incorrect pairing problem as existing algorithms in literature. Simulation results have shown that the proposed algorithm can achieve accurate and stable estimation results, and can potentially save the computational complexity of the estimation problem.

Direct blind equalization of Single-Input-Single-output (SISO) Infinite-Impulse-Response (IIR) channels is investigated by oversampling the channel output. Only second-order statistics is required and the channel order need not to be known. We first transform the oversampled channel into a special Single-Input- Multiple-Output (SIMO) model. Then a linear prediction approach is applied to partially estimate the model parameters. Based on the estimated parameters, Zero-forcing equalizers and MMSE equalizers are developed and their efficiency are evaluated by computer simulations.

Based on the minimum mean-square error (MMSE) criterion, a new blind multiuser detector is proposed. The searching space for the optimal detector is constrained according to the spreading code and channel properties of the desired user. The proposed constraint technique can guarantee that the optimal detector associated with the desired user locates in the searching space, whereas the existing methods consider only the spreading code and, consequently, cannot guarantee that the target optimal detector locates in the constrained searching space. Theoretical analysis and numerical results are presented.