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°.

Ambient backscatter communication (AmBC) is a newly cutting-edge technology for the Internet of Things, which utilizes the ambient radio frequency signal as the carrier to transmit information. Existing works focus on the simple on-off keying modulation which has low channel utilization. However, it is not desirable to develop the high-order modulation in the power domain due to the weak strength of the backscattered signal. In this paper, we extend the high-order modulation in the time domain instead, i.e., high-order time shift keying (TSK). Since the channel coherent time is unknown at the receiver and the tag, the detection methods with training symbols will have a huge performance degradation if the channels are changed and training symbols become outdated. To overcome this challenge, we further propose the transition-aided TSK (TA-TSK) modulation and the frequency-shifting TSK (FS-TSK) modulation, which do not need to send training symbols at the tag. These two methods can work well even if the channel coherent time is as short as one time slot. Meanwhile, the detection methods for TSK are developed and the corresponding closed-form bit-error-rate (BER) expressions are obtained. Simulation results show that a high modulation order is more suitable for the M-ary phase shift keying source than the complex Gaussian source. The high order TSK can provide at least$2~dB$signal-to-noise ratio (SNR) gain at the same BER compared to the on-off keying.

In this paper, signal detection in ambient backscatter communication (AmBC) with dynamic sources is investigated. Dynamic ambient radio frequency sources are the sources which transmit their signals randomly. The states of the dynamic source include the on-state and off-state. In the off-state, dynamic sources do not transmit any signal and the backscatter device cannot backscatter any signal accordingly. The received signals may contain only pure noises. Thus, it brings new challenges to the signal detection. We develop detection methods with/without truncating received signals for AmBC under both dynamic complex Gaussian source signals and dynamic M-ary phase-shift keying (M-PSK) source signals with on-off keying. To make detection simple and tractable for the dynamic M-PSK at the receiver, Manchester code is applied. Simulation results show it is necessary to truncate part of the signal in detections in the high signal-to-noise ratio region unless the received signals contain few pure noise samples, in order to reduce the bit-error-rate of backscatter signals.

Three dimensional (3D) source positioning techniques based on linear arrays have garnered significant attention from scholars in recent years, which use multiple one-dimensional angles of arrival (1D AOA) to localize sources. Existing 1D AOA-based 3D source positioning techniques are not directly applicable to cooperative positioning systems, particularly in scenarios involving cooperative sources such as underwater environments, the depth of the source is usually observable. This paper presents a novel cooperative positioning model that integrates 1D AOA and depth information, proposing a two-stage weighted least squares (TWLS) closed-form solution method. Furthermore, the Cramér-Rao lower bound (CRLB) for 1D AOAs and depth measurements is derived. The simulation experimental results demonstrate the feasibility of the proposed positioning scheme and the advantages of the TWLS method.

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.

In cases involving a rough interface, the echo frequency behavior of ultrawideband GPR approximates a non-linear Gaussian function. This characteristic leads to poor robustness and high computational complexity in existing modified MUSIC (Mod-MUSIC) methods. To address this challenge, this letter proposes an enhanced modified MUSIC (E-Mod-MUSIC) approach to mitigate the interference of false peaks, thus enhancing robustness. Furthermore, to reduce computational complexity while maintaining high estimation accuracy comparable to dense uniform frequency sampling structures, a distributed co-prime frequency sampling structure is employed with E-Mod-MUSIC. Numerical simulations with different scenarios show the effectiveness of the proposed method.

This paper proposes a joint multi-task Bayesian channel estimation and data detection algorithm for Turbo equalization (TEQ) in underwater acoustic (UWA) communication. The joint channel estimation and data detection (JCED) problem is formulated as a multi-task sparse Bayesian learning framework in single carrier (SC) communications. The framework treats the equalized symbols as unknown variables for improving the performance of iterative equalization and leverages temporal correlation in UWA channels by partitioning received symbols into subblocks. Furthermore, a JCED algorithm is derived with variational Bayesian inference. The proposed algorithm was evaluated based on the underwater field data collected during a lake experiment conducted in Qiandao Lake, Zhejiang province, China, in May 2016. The performance of the proposed algorithm has been validated with simulation and experiment results.

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.

In this paper, we analyse the achievable rate and capacity for ambient backscatter communications with dynamic sources under the binary input and signal output (BISO) channel. Dynamic sources are the sources that transmit signals to the air intermittently, rather than continuously transmitting signals like static sources. Instead of assuming static ambient sources, we investigate the dynamic sources. Due to the complexity of the expression of mutual information, we resort to the numerical simulation results for the BISO channel capacity and the capacity-achieving distribution is obtained by one-dimensional searching. We utilize inequality to show the relationship between static sources and dynamic sources in terms of the achievable rate and capacity. The numerical studies show that the maximal of the mutual information of the BISO channel is not achieved by a uniform input distribution, and the mutual information and capacity of the BISO channel with dynamic sources are close to that of the BISO channel with static sources, scaled by the probability that the dynamic source is in the on-state.

To cope with the explosive traffic growth expected in next-generation wireless networks, it is necessary to design next-generation multiple access techniques that can provide higher spectral efficiency as well as larger-scale connectivity. As a promising candidate, power-domain non-orthogonal multiple access (NOMA) has been widely studied. In conventional power-domain NOMA, multiple users are multiplexed in the same time and frequency band with differentpresetpower levels, which, however, may limit the spectral efficiency under practical finite alphabet inputs. Inspired by the concept of spatial modulation, we propose to solve this problem by encoding extra information bits into the power levels, and exploiting different signal constellations to help the receiver distinguish between them. To convey this idea, termed power selection (PS)-NOMA, clearly, we consider a simple downlink two-user NOMA system with finite input constellations. Assuming maximum-likelihood detection, we derive closed-form approximate bit error rate (BER) expressions for both users. Moreover, the two-user achievable rate region is also characterized. Simulation results verify the analysis and show that the proposed PS-NOMA can outperform conventional NOMA in terms of BER and achievable rate.

Abstract Here, an enhanced spatial modulation with generalized antenna selection (ESM‐GAS) for multiple‐input single‐output systems is proposed. Unlike the conventional SM systems, in ESM‐GAS, the number of active antennas is not fixed, but varies from only one to all. In ESM‐GAS, a concept called equivalent antennas is introduced to describe the status of the antennas. Out of all the available equivalent antennas, a subset is selected as the candidate antennas for SM, in which additional information bits are used to determine the indices of the activated equivalent antennas. Two criteria are used to select the candidate equivalent antennas. One is ESM‐GAS, where a part of candidate equivalent antennas with larger channel power gains are selected with low computational complexity. To further improve the bit error ratio (BER) performance of ESM‐GAS, the other one is a combination scheme of Euclidean distance antenna selection (EDAS) and ESM‐GAS (ESM‐GAS‐EDAS). Simulation results show that ESM‐GAS has better BER performance than conventional SM, and ESM‐GAS‐EDAS outperforms conventional SM, EDAS‐based SM, and ESM‐GAS, which verifies the effectiveness of the proposed schemes.

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.

As a typical usage scenario for the next-generation mobile communication network, massive Internet of Things (mIoT) is requested to provide high machine-type communication device (MTCD) density service. Nonorthogonal multiple access (NOMA) and mobile-edge computing (MEC) can further enhance the performance of mIoT. Furthermore, to cope with the energy consumption constraint of MTCD, energy harvesting (EH) can be leveraged. In this article, considering the social trusts of MTCDs, we propose an MEC offloading scheme for cellular Internet of Things networks with massive NOMA-aided EH MTCD and several road side units with edge servers randomly distributed in a macrocell. We aim to maximize the total sum rate of the network by jointly considering the processing mode selection, device clustering, subchannel allocation, and power allocation while satisfying the power, energy, latency, and quality of service requirements. To this end, we prove the NP-hardness of the considered optimization problem and decompose it into three subproblems, which can be solved by an iterative algorithm. Numerical results demonstrate the superior performance of the proposed scheme.

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.

Nowadays, intelligent transportation industry is becoming a hot spot in Internet of vehicles (IoV). However, owing to the existence of numerous intelligent terminals, communication security becomes a pressing problem. On the other hand, pervasive edge computing (PEC), as a pivotal technology, can significantly improve the performance of the system compared to the traditional cloud computing. In this article, we propose a nonorthogonal multiple access (NOMA)-based PEC power allocation framework in IoV, aiming at minimizing the system latency in the presence of eavesdroppers. Besides, queuing models, imperfect channel state information, and vehicles' speeds are all considered. Since the formulated problem is complicated, we consider its lower bound and derive the suboptimal closed-form expressions of the power allocation coefficients. Furthermore, a Frank-and-Wold algorithm is proposed to achieve the optimum total power. Simulation results illustrate the superior performance of the proposed NOMA scheme.

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.

In this article, we propose a downlink non-orthogonal multiple access (NOMA) based coordinated direct and relay system with one cell-center user and multiple cell-edge users, where a decode-and-forward (DF) relay bridges the connection between the base station and the cell-edge users. Both full-duplex (FD) and half-duplex (HD) protocols are considered for the relay. We assume that the performance of the cell-edge users is subjected to the relay, and the cancellation of the mutual interference between the relay and cell-center user is imperfect. Both the exact analytical expression of outage probability and an approximate expression of the ergodic sum rate at high signal-to-noise ratio (SNR) are derived. Numerical results demonstrate that: 1) the FD relaying NOMA system outperforms the HD relaying NOMA system at low SNR, but the situation is exactly the opposite at high SNR; 2) the mutual interference can cause a larger performance gap than the self-interference at the relay; 3) the power allocation coefficients for the cell-center user and relay can affect the performance more significantly than those for cell-edge users.11This article was presented in part at the IEEE International Workshop on Signal Processing Advances in Wireless Communications 2019 [1].

In this paper, we propose a coordinated direct and relay transmission scheme based on non-orthogonal multiple access (NOMA), where the relay and cell- center user operate in full-duplex mode to help enhance the quality of service of the cell-edge user as well as ensure the secure transmission. In the proposed system, the base station directly serves the cell-center user and relay while communicating with the cell-edge user through the relay and cell-center user. Taking advantage of NOMA, the cell-center user and relay are able to cancel the mutual interference between themselves. Moreover, they can also cooperatively apply the artificial noise scheme to interfere any potential eavesdropper without impairing the legitimate cell-edge user. Exact and closed-form expressions for the outage probability, achievable rate, and achievable secure rate are derived. Numerical results prove that the proposed system outperforms the existing counterpart in the low signal-to-noise ratio region, and ensures secure communications with appropriate power allocation.

With the advance of the Internet of Underwater Things, underwater acoustic sensor network (UASN) has been considered as a promising technology for oceanic engineering to explore and exploit marine resources. Due to the time variability, frequency selectivity, and the very limited available bandwidth, underwater acoustic (UWA) channels are generally known as one of the most challenging communication media in use today. The highly dynamic nature of UWA links calls for adaptive, scalable, and efficient routing schemes for UASNs. Depth-based routing has attracted much attention because it can work efficiently without the need for full-dimensional location information of sensors. However, it suffers from the problems of void region and detouring forwarding. To this end, this paper proposes a distance-vector-based opportunistic routing (DVOR) scheme to address these problems. DVOR uses a query mechanism to establish the distance vectors for UWA nodes, which record the smallest hop counts toward the sink. Then, an opportunistic routing is developed to coordinate the packet forwarding based on the distance vectors. DVOR has a low signaling overhead in opportunistic forwarding, as well as the ability to avoid the problems of void region and long detour. Simulation results show that DVOR outperforms the existing routing protocols in terms of packet delivery ratio, energy-efficiency, and average end-to-end delay.

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.

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.

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.

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.

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.