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°.

The proliferation of Internet of Things (IoT)-generated distributed personal data enables user-specific large language model (LLM) adaptation at the edge. The split federated learning (SFL) facilitates collaborative learning and reduces memory footprint by model splitting, which necessitates the transmission of intermediate activations, rendering it susceptible to reconstruction attacks and privacy breaches. In this paper, we present a privacy-aware SFL scheme addressing the accuracy-efficiency-privacy trilemma in LLM fine-tuning over heterogeneous IoT devices. Particularly, we develop a privacy quantification metric based on Fisher information to assess layer-wise privacy risks in smashed data transmission. Guided by this metric, we establish an analytical model that captures the intricate relationships between privacy leakage, fine-tuning convergence time, and device energy consumption. To optimize these three aspects, we formulate a multi-objective mixed-integer programming problem. Then, an -constraint-based block coordinate descent (BCD) algorithm is proposed to jointly determine the optimal LLM split layer, transmit power, and bandwidth allocation for IoT devices under their memory and network constraints. Extensive simulation results demonstrate the proposed scheme’s effectiveness in achieving 24% faster convergence, 40% lower energy consumption, and 7% reduced privacy leakage compared to baseline approaches, while maintaining competitive model accuracy.

Nonorthogonal multiple access (NOMA) has been recognized as a key enabling techniques for future communication systems since it can take advantage of differences in users’ channel conditions to achieve higher spectral efficiency compared with orthogonal multiple access. However, employing such a capacity-centric framework can result in intragroup co-channel interference for those users whose symbols are intended to be first decoded, potentially causing a decrease in reliability. Therefore, the Alamouti encoding method has been widely used in the multiuser MISO-NOMA broadcast channels to improve reliability. Although the Alamouti-based NOMA paradigm can improve reliability by providing full spatial diversity, it comes at the cost of a significant loss in spectral efficiency compared to the capacity-centric NOMA framework. To overcome these limitations, this article presents a new approach for improving the performance of multiuser MISO-NOMA broadcast channels, in which the diversity-multiplexing tradeoff is leveraged more effectively by providing multiplexing for near users and diversity for far users at the transceiver. The closed-form expressions of ergodic sum-rate and outage probability for the proposed scheme are derived. Simulation results demonstrate that the proposed scheme is capable of performing well in a wide range of scenarios and it surpasses traditional methods in terms of reliability and effectiveness.

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.

Affine frequency division multiplexing (AFDM) is an emerging multicarrier waveform that offers a potential solution for achieving reliable communications over time-varying channels. This paper proposes two maximum-likelihood (ML) estimators of symbol time offset and carrier frequency offset for AFDM systems. One is called joint ML estimator, which evaluates the arrival time and carrier frequency offset by comparing the correlations of samples. Moreover, we propose the other so-called stepwise ML estimator to reduce the complexity. Both proposed estimators exploit the redundant information contained within the chirp-periodic prefix inherent in AFDM symbols, thus dispensing with any additional pilots. To further mitigate the intercarrier interference resulting from the residual frequency offset, we design a mirror-mapping-based scheme for AFDM systems. Numerical results verify the effectiveness of the proposed time and carrier frequency offset estimation criteria and the mirror-mapping-based modulation for AFDM systems.

Nonorthogonal multiple access (NOMA) has been widely recognized as a promising spectral efficiency technique for the next generation of wireless communication networks due to its ability to support multiple users in the same orthogonal resource block. In response to the increasing demands for extensive connectivity and high-volume data transmission, a novel index modulation (IM)-aided NOMA scheme has been conceived to improve downlink transmission by capitalizing on the flexibility provided by the constellation rotation design for superposition coding. In the proposed scheme, known as IM aided NOMA with the constellation rotation (IM-NOMA-CR), users are categorized into cell-edge group (far-user accommodated) and cell-center group (near-user accommodated) based on their channel conditions. The rotated constellation-based IM operation is exclusively applied to users in the near-user group, who receive lower power allocations compared to the far-user group, enabling them to transmit additional information while ensuring system reliability. This approach enhances spectral efficiency by activating all subcarriers (orthogonal resource blocks) to transmit information to scheduled users, in contrast to the conventional IM-NOMA scheme. Moreover, extra information can be transmitted through constellation rotation in the superposition coding process, setting it apart from traditional NOMA schemes. Subsequently, the maximum likelihood detector employing successive interference cancellation (ML-SIC) is utilized at the receiving end to decode the intended symbols for all users. Numerical simulations have been carried out to validate the efficacy of the proposed IM-NOMA-CR design, demonstrating a significant enhancement in spectral efficiency and error performance compared to existing NOMA schemes.

As a new candidate waveform for the next generation of mobile communications, orthogonal chirp division multiplexing (OCDM) has attracted growing attention for its high spectrum efficiency and robustness to narrow-band interference or impulsive noise. Under vehicular communication channels with multiple lags and multiple Doppler shifts (MLMD), the signal suffers doubly selective (DS) fadings in the time and frequency domain, and data symbols modulated on orthogonal chirps interfere with each other. To address the problem of symbol detection of OCDM over MLMD channels, under the assumption that path attenuation factors, delays, and Doppler shifts of the channel are available, we first derive the closed-form channel matrix in the Fresnel domain and then propose a low-complexity method to approximate it as a sparse matrix. Based on the approximated Fresnel-domain channel, we propose a message-passing (MP) based detector to estimate the transmit symbols iteratively. Finally, under two MLMD channels (an underspread channel for terrestrial vehicular communications and an overspread channel for narrow-band underwater acoustic communications), Monte Carlo simulation results and analyses are provided to validate its advantages as a promising detector for OCDM.

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.

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 propagation delay is non-negligible in underwater acoustic networks (UANs) since the propagation speed is five orders of magnitude smaller than the speed of light. In this case, space and time factors are strongly coupled to determine the collisions of packet transmissions. To this end, this paper analyzes the impact of space-time coupling on slotted medium access control (MAC). We find that a sending node has specific location-dependent interference slots and slot-dependent interference regions. Thus, the collisions may span multiple slots, leading to both inter-slot and intra-slot collisions. Interestingly, the slot-dependent interference regions could be annulus inside the whole transmission range. It is pointed out that collision-free regions exist when a guard interval in a slot is larger than a packet duration. In this sense, the long slot brings spatial reuse within the transmission range. We then derive the closed-form expressions for the successful transmission probability of Slotted-ALOHA and the upper-bound and the lower-bound for the successful transmission probability in UANs. We further find that the optimal guard interval to reach the peak successful transmission probabilities is not larger than a packet duration, which is much shorter than the existing slot setting in the typical Slotted-ALOHA in the uniformly distributed UANs. Simulation results verify our findings, and also show that the performance of vertical transmissions is more sensitive to the spatial impact than horizontal transmissions in UANs.

Recently, intelligent reflecting surface (IRS) emerges as an effective technique for saving power consumption by customizing the wireless propagation environment. On the other hand, the symbol level precoding (SLP) technique provides a clever solution to interference exploitation by converting the multiuser interference (MUI) into a beneficial part of the desired signal. In this paper, we propose to jointly exploit IRS and SLP to cope with the power control and interference management issues in a heterogeneous network (HetNet). Considering the possible coordination between the macro base station (MBS) and the pico base station (PBS), we propose two corresponding schemes to manage the inter-cell and intra-cell interference. For both proposed schemes, the power minimization problems are studied by jointly optimizing the precoding matrices at the MBS and PBS as well as reflecting coefficients at the IRS. Due to the non-convexity of these problems, the precoding matrices and reflecting coefficients are optimized alternately. We propose two Lagrangian based algorithms to obtain the optimal solutions of the precoding matrices, where the precoding matrix of the MBS always yields a closed-form. A multiple-gradient descent algorithm based on the Riemannian manifold (MGD-RM) is proposed as well to enhance the received signal quality of each MUE and PUE for the reflecting design. Simulation results manifest a significant performance gain achieved by our proposed HetNet over benchmarks.

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.

As a promising technique for high-mobility wireless communications, orthogonal time frequency space (OTFS) has been proven to enjoy excellent advantages with respect to traditional orthogonal frequency division multiplexing (OFDM). Although multiple studies have considered index modulation (IM) based OTFS (IM-OTFS) schemes to further improve system performance, a challenging and open problem is the development of effective IM schemes and efficient receivers for practical OTFS systems that must operate in the presence of channel delays and Doppler shifts. In this paper, we propose two novel block-wise IM schemes for OTFS systems, named delay-IM with OTFS (DeIM-OTFS) and Doppler-IM with OTFS (DoIM-OTFS), where a block of delay/Doppler resource bins are activated simultaneously. Based on a maximum likelihood (ML) detector, we analyze upper bounds on the average bit error rates for the proposed DeIM-OTFS and DoIM-OTFS schemes, and verify their performance advantages over existing IM-OTFS systems. We also develop a multi-layer joint symbol and activation pattern detection (MLJSAPD) algorithm and a customized message passing detection (CMPD) algorithm for our proposed DeIM-OTFS and DoIM-OTFS systems with low complexity. Simulation results demonstrate that our proposed MLJSAPD and CMPD algorithms can achieve desired performance with robustness to the imperfect channel state information (CSI).

Molecular communication (MC) is an emerging field aiming at realizing information exchange via chemical signals between nanomachines in nanonetworks. Information transmission that is energy-efficient and relies on relatively low-complexity transceiver techniques is of practical importance for MC systems. Based on the fact that constellation shaping can improve energy efficiency, in this paper, we propose a molecular shell mapping (MSM) scheme to implement the probabilistic constellation shaping for MC. The MSM method is designed to exploit the concentration sequences with the lowest sum sequence weight, which results in an energy-efficient signal constellation. Furthermore, we propose an algorithm for selecting and sorting the concentration sequences to mitigate inter-symbol interference. For information detection, we design a genie-aided maximum-likelihood (ML) detector and a realistic ML detector to leverage the constructive effect of intra-sequence interference, as well as derive their bit error rates and achievable rates. Additionally, for the applications using large blocklength sequences, a low-complexity ML detection method is proposed. Numerical simulation results confirm that the shaped signaling using the MSM method is more energy-efficient than the conventional equiprobable signaling, achieving shaping gains of up to 1.5 dB at an ultra-short blocklength of 4.

Relaying is an effective method to achieve reliable and timely data collection, which is one of the most important parts of underwater acoustic sensor networks (UASNs). Considering that the relay position determines the reliability of relay communication, we study the problem of relay placement and place an autonomous underwater vehicle (AUV) to mobile relay data transmission and realize the reliable, low-latency, and low-energy data collection in UASNs. First, we formulate the relay positions that can meet a certain frame error rate (FER) requirement as the FER-restricted area (FRA), and approximate FRA with a three-dimensional geometry formula. The problem of reliable and timely data collection becomes planning a short AUV relaying trajectory under the FRA constraint. To this end, we propose a nearest-community (N-C) trajectory planning algorithm to design the AUV relay trajectory. A member grouping method and the necessity of position (NoP) concept are proposed to further reduce the relay positions and trajectory length of the AUV. Simulation results verify that the approximate FRA is more than 90% consistent with the real FRA and show that the N-C using NoP-based member grouping can successfully receive more packets per minute and consume fewer sensors’ energy than other algorithms.

Overcoming inter-symbol interference (ISI) is one of the key challenges in the design of molecular communication systems. In this paper, we propose a scheme for optimizing the detection interval to minimize the impact of ISI while ensuring the acquisition of effective information. Our detection interval optimization applies to both the absorbing and passive receivers. For analysis, we consider as the performance metrics signal-to-interference difference (SID) and signal-to-interference and noise amplitude ratio (SINAR) proposed in the literature rather than the intractable bit error rate (BER). Accordingly, we derive the optimal detection interval in closedform. Finally, simulation results in terms of BER verify the theoretical analysis and also show the promising advantages of the proposed scheme in signal detection.

Marine science and Internet of Underwater Things applications rely significantly on collecting data from underwater sensors. Data collection using long-distance underwater acoustic communications consumes a lot of energy in underwater sensor nodes, which are powered by batteries. To achieve low-energy consumption, we can use the autonomous underwater vehicle (AUV) to move close to sensor nodes and exploit the short-range and high-rate communications. Most of the existing AUV-based data collection schemes consider the scenarios having the knowledge of node positions, where the cruising trajectory can be computed before the AUV’s departure. These schemes cannot apply to some scenarios such as turtle tracking for a certain sea area having no position information. To this end, we first propose a planning-while-detecting approach to dynamically detect the sensors on turtles and adjust the AUV cruising direction to collect data. To further improve data efficiency under the energy limit of the AUV, we group the sensors that can share the same trajectory using their detected directions. A grouping-based dynamic trajectory planning (GDTP) is then proposed to determine the next cruising direction that can visit the group of sensors having the largest amount of data and demanding the least cruising energy at the risk of detection errors. Simulation results show that GDTP achieves significantly higher data collection efficiency than the existing trajectory planning algorithms in dynamic scenarios, and as the communication range increases, it can even outperform the existing algorithms with node locations.

In this paper, we propose a two-layer heterogeneous network to realize the remote monitoring and advanced warning for infectious diseases spread by airborne pathogens, whose detection can be considered as a binary detection problem. To intuitively study the detection process, we abstract it as a molecular communication via diffusion (MCvD) model and uncover that one of the key factors to impact the detection performance is inter-symbol interference (ISI), i.e., the viral aerosol from other biological entities. Therefore, overcoming ISI is imperative to ensure reliable detection. In the abstracted MCvD model, the detection performance can be described by the bit error rate (BER) performance. Following this assumption, we propose to optimize the detection interval to minimize the impact of ISI while ensuring the accurate detection of the transmitted information symbol, which is suitable for both the absorbing and passive receivers. For tractability, based on the signal-to-interference difference (SID) and signal-to-interference-and-noise amplitude ratio (SINAR), we design a modified-SINAR (mSINAR) to measure BER performance for the MCvD system with a variable detection interval. Besides, we derive the optimal detection interval in closed-form. Using simulation results, we show that in terms of BER, our proposed mSINAR scheme is superior to the competitive schemes, and performs similarly to the scheme with optimal intervals determined by the exhaustive search.

The berth allocation problem (BAP) is an NP-hard problem in maritime traffic scheduling that significantly influences the operational efficiency of the container terminal. This paper formulates the BAP as a permutation-based combinatorial optimization problem and proposes an improved ant colony system (ACS) algorithm to solve it. The proposed ACS has three main contributions. First, an adaptive heuristic information (AHI) mechanism is proposed to help ACS handle the discrete and real-time difficulties of BAP. Second, to relieve the computational burden, a divide-and-conquer strategy based on variable-range receding horizon control (vRHC) is designed to divide the complete BAP into a set of sub-BAPs. Third, a partial solution memory (PSM) mechanism is proposed to accelerate the ACS convergence process in each receding horizon (i.e., each sub-BAP). The proposed algorithm is termed as adaptive ACS (AACS) with vRHC strategy and PSM mechanism. The performance of the AACS is comprehensively tested on a set of test cases with different scales. Experimental results show that the effectiveness and robustness of AACS are generally better than the compared state-of-the-art algorithms, including the well-performing adaptive evolutionary algorithm and ant colony optimization algorithm. Moreover, comprehensive investigations are conducted to evaluate the influences of the AHI mechanism, the vRHC strategy, and the PSM mechanism on the performance of the AACS algorithm.

This paper studies virtual machine (VM) scheduling in a queueing cloud computing system with stochastical arrivals of heterogeneous jobs by considering jobs’ delay requirements. The delay-optimal VM scheduling in such a cloud computing system is formulated as a multi-resource multi-class problem minimize the average job completion time, which is often NP-hard. To solve such a problem, we first propose a queueing model that buffers the same type of VM jobs in one virtual queue. The queueing model then divides the VM scheduling into two parallel low-complexity algorithms, i.e., intra-queue buffering and inter-queue scheduling. A min-min best fit (MM-BF) policy is used to schedule the jobs in different queues to minimize the remaining system resources, while a shortest-job-first (SJF) policy is used to buffer the job requests in each queue based on their job lengths in an ascending order. To avoid job starvation for the long-duration jobs in SJF-MMBF, we further propose a queue-length-based MaxWeight (QMW) policy based on Lyapunov drift to minimize the queue lengths of VM jobs, which is called SJF-QMW. Simulation results show that, SJF-MMBF and SJF-QMW achieve low delay performance in terms of average job completion time and high throughput performance in terms of job hosting ratio.

Molecular communication (MC) is an emerging communication paradigm, where the information is carried via the patterns of molecules that are mainly governed by the diffusion process. Current MC literature concentrates on the time-domain analysis, while the signal analysis in other domains may facilitate the MC research. To this end, this paper performs the frequency-domain analysis by deriving the frequency response of the diffusion-based MC channels, manifesting an explicitly low-compass characteristic. The energy of the channel impulse response in the diffusion-based MC is also derived, and the corresponding bandwidth definition is proposed, which determines the sampling frequency for the one-shot diffusive channel impulse response in MC. The results in this work lay the foundation for the frequency-domain signal processing in diffusion-based MC channels.

Ocean big data is becoming a future trend of the Internet of Underwater Things (IoUT). Underwater wireless sensor networks (UWSNs) technique is a promising method to realize ocean big data. However, the limited energy and the low location accuracy of sensor nodes make the data collection of UWSNs difficult. To reduce the energy consumption of sensor nodes, we consider an autonomous underwater vehicle (AUV) based data collection, where the AUV moves close to the sensors to collect data using short-range high-rate communications. Particularly, we propose a grouping-based dynamic trajectory planning (GDTP) for the AUV. GDTP does not require the position information of sensor nodes. It dynamically detects the existence and directions of sensor nodes, based on which the detected sensor nodes are grouped by a proposed common communication area model. The cruising direction of the AUV is dynamically determined with the maximum expected payoff that considers the data collection and energy consumption in inaccurate detection. Simulation results show that the proposed GDTP collects more data packets with less energy compared to the existing schemes.

Due to the serious frequency selective fading and time selective fading in underwater acoustic (UWA) channel, the design of receiver becomes more difficult. We present a convolutional neural network (CNN) based orthogonal frequency division multiplexing (OFDM) receiver that fully considers the banded channel matrix features of doubly-selective channels in the UWA communication to achieve the integrated design of channel estimation and equalization. We propose a novel architecture using decoder and encoder convolutional neural network, named DECCN, which uses twenty-one convolution layers to compose an encoder and a decoder based on the dilation convolution and the feature reuse. DECCN focuses on reducing the complexity without considering the full connection (FC) layer. DECCN performs well for various length signals without changing the structure of system, such as 1024 bits and 2048 bits. Simulation results show that the proposed scheme reduces the bit error rate compared with the traditional algorithms and other DL-based schemes, especially with the pilot only 1/8 length of the signal or without cyclic prefix.

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.

Communication between nanomachines is still an important topic in the construction of the Internet of Bio-Nano Things (IoBNT). Currently, molecular communication (MC) is expected to be a promising technology to realize IoBNT. To effectively serve the IoBNT composed of multiple nanomachine clusters, it is imperative to study multiple-access MC. In this article, based on the molecular division multiple access technology, we propose a novel multiuser MC system, where information molecules with different diffusion coefficients are first employed. Aiming at the user fairness in the considered system, we investigate the optimization of molecular resource allocation, including the assignment of the types of molecules and the number of molecules of a type. Specifically, three performance metrics are considered, namely, min-max fairness for error probability, max-min fairness for achievable rate, and weighted sum-rate maximization. Moreover, we propose two assignment strategies for types of molecules, i.e., best-to-best (BTB) and best-to-worst (BTW). Subsequently, for a two-user scenario, we analytically derive the optimal allocation for the number of molecules when types of molecules are fixed for all users. In contrast, for a three-user scenario, we prove that the BTB and BTW schemes with the optimal allocation for the number of molecules can provide the lower and upper bounds on system performance, respectively. Finally, numerical results show that the combination of BTW and the optimal allocation for the number of molecules yields better performance than the benchmarks.

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.

Ultrasonic intra-body communication (IBC) is a promising enabling technology for future healthcare applications, due to low attenuation and medical safety of ultrasonic waves for the human body. A splitting receiver, referred to as the splitting-detection separate-decision (SDSD) receiver, is introduced for ultrasonic pulse-based IBCs, and SDSD can significantly improve bit-error rate (BER) performance over the traditional coherent-detection (CD) and energy detection (ED) receivers. To overcome the high complexity and improve the BER performance of SDSD, a splitting-detection joint-decision (SDJD) receiver is proposed. The core idea of SDJD is to split the received signal into two streams that can be separately processed by CD and ED, and then summed up as joint decision variables to achieve diversity combining. The theoretical channel capacity and BER of the SDSD and SDJD are derived for M-ary pulse position modulation ( M-PPM) and PPM with spreading codes. The derivation takes into account the channel noise, intra-body channel fading, and channel estimation error. Simulation results verify the theoretical analysis and show that both SDSD and SDJD can achieve higher channel capacity and lower BER than the CD and ED receivers with perfect channel estimation, while SDJD can achieve the lowest BER with imperfect channel estimation.

This paper proposes a novel precoding‐aided and efficient data transmission scheme called virtual spatial channel number and index modulation (VS‐CNIM), which conveys extra data by changing both the number and index of active virtual parallel channels of multiple‐input multiple‐output (MIMO) channels, obtained through the singular value decomposition (SVD) in each time slot. Unlike the conventional virtual spatial modulation (VSM), where extra data bits are transmitted only using index of active virtual parallel channels, the VS‐CNIM scheme, depending on incoming information bits, transmits extra bits utilizing both the number and indices of active parallel channels along the bits carried by M‐ary constellation symbols. Therefore, VS‐CNIM provides significantly superior spectral efficiency (SE) compared to VSM. Considering the influence of imperfect channel estimation, a closed‐form upper bound is derived on average bit error probability (ABEP). The asymptotic performance is also analyzed, which gives the coding gain and diversity order and describes error floor under the consideration of perfect and imperfect channel estimation, respectively. Monte Carlo simulations exhibit that the VS‐CNIM scheme achieves considerably better error performance and high SE than precoding‐aided SM (PSM) and VSM schemes.

Ultrasonic intra-body communication (IBC) is a promising enabling technology for many future healthcare applications, due to the low attenuation and medical safety of ultrasonic waves for human body. This paper proposes a splitting-detection joint-decision (SDJD) receiver structure for ultrasonic pulse-based IBCs. The core idea is to split the received signal into two steams for the coherent-detection (CD) and energy detection (ED), and then the two detection outputs are summed up as joint decision variables to estimate the received signals. The theoretical channel capacity and bit-error rate (BER) expressions are derived, considering receive noise, tissue attenuation, and channel fading in intra-body channels. Both simulation and theoretical results show that the SDJD receiver outperforms the pure CD, ED, and splitting-detection separate-decision receivers in terms of BER performance. In addition, the proposed receiver has a low complexity requirement on the hardware and decision algorithm. Therefore, SDJD is an important structure to receive signals correctly and efficiently for ultrasonic IBCs.

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.

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.

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, a new channel model is presented for molecular communications (MC), where a point source emitted by the transmitter undergoes three phases, with effect of convection dominating in the first two phases, whereas diffusion prevailing in the final phase. The point source obtains its initial velocity and passes through the nozzle of the nanomachine transmitter in the first phase, followed by a deceleration process in the second phase. The free diffusion model is considered in the third phase. Based on this channel model, the energy transfer issue for two-way MC system is also taken into account, in which one of the transceivers is assumed to have abundant information molecules from its ambient environment, whereas the other one obtains the information molecules by implementing the simultaneous molecular information and energy transfer (SMIET). Finally, analytical bit error rate (BER) expressions are validated by computer simulations. Our results suggest that the symbol duration and the SMIET order significantly influence the BER performance in our two-way MC system.

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.

In this paper, we study the performance of a downlink non-orthogonal multiple access-based cooperative relay system with a single relay over Nakagami-m fading channels, where both decode-and-forward (DF) and amplify-and-forward (AF) protocols are considered. We assume that only statistical channel state information is available to the system and used to determine the decoding order of cell-edge users' data. For DF relaying, both ergodic sum rate and outage probability are solved in closed form. For AF relaying, closed-form asymptotic ergodic sum rate and outage probability are provided. Numerical results verify the accuracy of the analysis and demonstrate that the DF protocol significantly outperforms the AF one in terms of ergodic sum rate even the channel's near-far effect weakens. In addition, the DF protocol exhibits better outage performance than the AF one at low signal-to-noise ratio (SNR), though the superiority becomes negligible with the increasing SNR.

In this paper, we investigate the performance of simultaneous wireless information and power transfer cooperative amplify-and-forward communication systems in shadowed Nakagami-m fading channels. Both coherent and non-coherent modulations, namely, binary phase-shift keying (BPSK) and binary differential phase-shift keying (BDPSK) are considered. We propose closed-form expressions of the moment generating function and the probability density function for the received signal-to-noise ratio of the cooperative link. Based on this, we further derive the average bit-error-rate and the outage probability expressions for the systems with both BPSK and BDPSK modulations. All of these expressions are given in closedform except one in single integral that can be easily evaluated using numerical integration methods. Numerical results are used to confirm the validity of the proposed analytical results. It is shown that the performance of the systems is more susceptible to the fading parameter m than to the shadowing level σs and does not change much with the power splitting ratio.

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.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique for future wireless communications as it results in high spectral efciency. In this paper, we study a NOMA based dual-hop X-relay system, where a decode-and- forward relay assists the communication between the sources and the destinations. We assume that the statistical channel state information is known to the network. In such an assumption, the achievable sum-rate at high signal-to-noise ratio is further solved in closed form. Especially, the corresponding power allocation schemes, including the theoretically optimal and practical suboptimal ones, are proposed. Analyses and simulation results show that in the considered system, NOMA signicantly outperforms the conventional orthogonal multiple access schemes in terms of sum-rate performance, especially when channel's near-far effect is signicant.

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.

Index modulation concept has attracted considerable research interest in the past few years. As a realization of index modulation in the frequency domain, orthogonal frequency division multiplexing with index modulation (OFDM-IM) has recently been proposed, which conveys information bits through both the subcarrier activation patterns and the amplitude phase modulation constellation points. This paper proposes two enhanced OFDM-IM schemes aimed at achieving higher spectral efficiency and diversity gain, respectively. The first one, termed OFDM with hybrid in-phase/quadrature index modulation (OFDM-HIQ-IM), explores the I- and Q- dimensions jointly for index modulation, allowing transmission of more index modulation bits in each subcarrier group. The second one, termed linear constellation precoded OFDM-IQ-IM (LP-OFDM-IQ-IM), spreads information symbols across two adjacent active subcarriers through linear constellation precoding to harvest additional diversity gain. By maximizing the minimum squared Euclidean distance, two different realizations of LP-OFDM-IQ-IM are derived, which leads to a rotated and a diamond-shaped constellation, respectively. The proposed OFDM-HIQ-IM and LP-OFDM-IQ-IM, as revealed by both theoretical analyses and computer simulations, enable low-complexity detection and exhibit superior error rate performance over the existing OFDM-IM schemes.

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.

In this paper, a novel self-interference (SI) cancellation scheme operated in the digital domain is proposed for the full duplex (FD) system configured with two antennas. The basic idea of our scheme is to transfer the strong SI to the inter-symbol interference (ISI), which is easier to cope with, through a combination of the signals received in successive time slots based on the assumptions that the SI channel is reciprocal and the previous transmitted data can be stored. However, this transfer may result in an un-removable error floor when the transmit antenna remains the same during the time slots in the SI cancellation. To solve this problem, we propose to apply differential spatial modulation, which forces two antennas to take turns to transmit signals in every two consecutive time slots. To further compensate for the loss of spatial degrees of freedom owing to the transfer, we propose to involve more time slots in the SI cancellation and allow the transmit antenna to be selected randomly by spatial modulation starting from the third time slot. Simulations on the bit error rate (BER) are conducted, whose results show that the performance of the proposed scheme is improved as more time slots are involved in the SI cancellation, and the proposed scheme with four time slots outperforms the conventional FD system with 3 dB interference-to-noise ratio (INR) for QPSK modulation.

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.

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.