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Wireless sensing and wireless energy are enablers to pave the way for smart transportation and a greener future. In this paper, an intelligent reflecting surface (IRS) assisted integrated sensing and wireless power transfer (ISWPT) system is investigated, where the transmitter in transportation infrastructure networks sends signals to sense multiple targets and simultaneously to multiple energy harvesting devices (EHDs) to power them. Recognizing the inherent tradeoff between energy harvesting and sensing performance, we propose to jointly optimize the system performance via optimizing the beamforming and IRS phase shift. However, the coupling of optimization variables makes the formulated problem non-convex. Thus, an alternative optimization approach is introduced and based on which two algorithms are proposed to solve the problem. Specifically, the first algorithm involves the semi-positive definite programming techniques, and the second algorithm is based on the successive convex approximations and majorization minimization to design the closed form solutions of the optimization variables, which can effectively reduce the computational complexity. Our simulation results validate the proposed algorithms and demonstrate the advantages of using IRS to assist wireless power transfer in ISWPT systems. This research contributes to the integration of wireless sensing and wireless energy in intelligent transportation systems and underscores the optimization of system performance through the introduction of IRS.

The sparsity of millimeter wave (mmWave) channels in the angular and temporal domains is beneficial to channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage poses a significant challenge on the localization in mmWave systems, potentially leading to substantial positioning errors. A promising solution is to employ reconfigurable intelligent surface (RIS) to generate the virtual line-of-sight (VLoS) paths to aid localization. Consequently, wireless localization in the RIS-assisted mmWave systems has become the essential research issue. In this paper, a multiple measurement vector (MMV) model is constructed and a two-stage channel estimation based localization scheme is proposed. During the first stage, by exploiting the beamspace sparsity and employing a random RIS phase shift matrix, the channel parameters are estimated, based on which the precoder at base station and combiner at user equipment (UE) are designed. Then, in the second stage, based on the designed precoding and combining matrices, the optimal phase shift matrix for RIS is designed using the proposed modified temporally correlated multiple sparse Bayesian learning (TMSBL) algorithm. Afterwards, the channel parameters, such as angle of reflection, time-of-arrival, etc., embedding location information are estimated for finally deriving the location of UE. We demonstrate the achievable performance of the proposed algorithm and compare it with the state-of-the-art algorithms. Our studies show that the proposed localization scheme is capable of achieving centimeter level localization accuracy, when LoS path is blocked. Furthermore, the proposed algorithm has a low computational complexity and outperforms the legacy algorithms in different perspectives.

The recently-developed orthogonal time frequency space (OTFS) modulation is capable of transforming the time-varying fading of the time-frequency (TF) domain into the time-invariant fading representations of the delay-Doppler (DD) domain. The OTFS system using orthogonal frequency-division multiplexing (OFDM) as inner core naturally requires the subcarrier spacing (SCS) $\Delta f$ to be larger than the maximum Doppler frequency $\vartheta _{\text {max}}$ , i.e. $\Delta f > \vartheta _{\text {max}}$ , when perfect channel state information (CSI) knowledge is assumed. However, for the first time in literature, we explicitly demonstrate that the practical OFDM-based OTFS systems have to double their SCS in order to facilitate CSI estimation, requiring $\Delta f'=2\Delta f > 2\vartheta _{\text {max}}$ . In order to mitigate this loss, we propose a novel noncoherent OTFS system, which is capable of operating at $\Delta f > \vartheta _{\text {max}}$ . The major challenge in this context is the mitigation of the DD-domain interference without CSI. Against this background, we draw an analogy between the input-output model of OTFS and that of V-BLAST, where V-BLAST’s blind inter-antenna interference mitigation technique is invoked. Moreover, we propose to partition the DD-domain modulated symbols into groups, where space-time block coding is invoked in order to eliminate the DD-domain interference within each group. Our simulation results demonstrate that the proposed noncoherent OTFS is capable of substantially outperforming its coherent counterparts relying on CSI estimation.

In order to maximize the spectral efficiency (SE) in multicarrier-division duplex (MDD) enabled cell-free massive MIMO (CF-mMIMO), a heterogeneous graph neural network (HGNN), referred to as CF-HGNN, is specifically introduced to optimize the power allocation (PA). To efficiently manage the interference invoked, a meta-path based mechanism is applied in CF-HGNN to enable individual access point (AP) and mobile station (MS) nodes to aggregate information from the interfering and communication paths with different priorities during message passing. Moreover, the proposed CF-HGNN employs the adaptive node embedding layer and adaptive output layer to make it scalable to the various numbers of APs, MSs and subcarriers. For comparison, a quadratic transform and successive convex approximation (QT-SCA) algorithm is proposed to solve the PA problem in classic way. Numerical results show that CF-HGNN is capable of achieving 99% of the SE achievable by QT-SCA but using only 10−4 times of its operation time, and it can outperform the conventional learning-based and greedy unfair methods in terms of SE performance. Furthermore, CF-HGNN exhibits good scalability to the CF networks with various numbers of nodes and subcarriers, and also to the large-scale CF networks when assisted by user-centric clustering.

We demonstrate that the proposed optical orthogonal time frequency space (O-OTFS) is capable of improving the bandwidth-/power-/energy-efficiencies of optical orthogonal frequency-division multiplexing (O-OFDM). The bandwidth-efficiency is improved because only a single cyclic prefix (CP) is needed for an entire O-OTFS frame. The power-efficiency is enhanced thanks to the diversity gain achieved by its symplectic finite Fourier transform (SFFT), which also leads to a reduced peak-to-average power ratio (PAPR), hence improving its energy-efficiency. These features are facilitated by the proposed layered asymmetrically clipped O-OTFS (LACO-OTFS), which is capable of removing the direct current (DC) bias while retaining the full optical throughput. Nonetheless, there exists an inherent trade-off, where increasing the O-OTFS frame size leads to a commensurately reduced CP percentage at the cost of an increased PAPR. In order to mitigate this, we propose to perform discrete Fourier transform based spreading (DFT-S) in the delay-Doppler (DD)-domain. Furthermore, we demonstrate that regardless of the choice of domain in which the information is modulated (i.e. O-OFDM/O-OTFS with/without DFT-S), the frequency-selectivity of the quasi-static but dispersive optical channel can always be equalized by single-tap frequency-domain equalization (FDE). Moreover, the channel estimation techniques are conceived to operate in the time-/frequency-/DD-domains for both O-OFDM and O-OTFS. Our simulation results demonstrate that for a multi-user optical wireless system associated with $M=64$ subcarriers and the OTFS frame length of $N=64$ , LACO-OTFS is capable of achieving a 7 dB power-efficiency gain over LACO-OFDM, where the CP overhead is reduced by a factor of $N=64$ . DFT-S-LACO-OTFS is also capable of providing a 7 dB power-efficiency gain over DFT-S-LACO-OFDM, where the low PAPR of single-carrier transmission is retained.

Probabilistic constellation shaping (PCS) is a promising technique employed in molecular communications, utilizing molecular shell mapping (MSM). However, the performance of MSM, particularly its bit error rate (BER), falls short when the exact channel state information is unavailable. To address this challenge and enhance robustness, we propose a scheme known as Reed-Solomon coded PCS (RS-PCS) scheme. This approach combines shaping and coding through a parallel transmitter structure, preserving the shaped distribution at the output of the RS encoder. In addition, we develop a low-complexity receiver that leverages the intra-sequence interference. Our numerical results on BER clearly demonstrate that the proposed RS-PCS scheme outperforms existing schemes that only consider the PCS or RS coding techniques alone.

To liberate indoor industrial cell-free massive multiple-input multiple-output (CF-mMIMO) networks from wired fronthaul, this paper proposes a multicarrier-division duplex (MDD)-enabled two-tier terahertz (THz) fronthaul scheme. In our scheme, two layers of fronthaul links rely on the mutually orthogonal subcarrier sets in the same THz band, while access links are implemented over sub-6G band. However, the proposed scheme leads to a complicated mixed-integer nonconvex optimization problem incorporating access point (AP) clustering, device selection, the assignment of subcarrier sets and the resource allocation at both the central processing unit (CPU) and APs. Hence, in order to address the formulated problem, we first resort to the low-complexity but efficient heuristic methods thereby relaxing the involved binary variables. Then, the overall end-to-end optimization is implemented by iteratively optimizing the assignment of subcarrier sets and the number of AP clusters. Furthermore, an advanced MDD frame structure consisting of three parallel data streams is tailored for the proposed scheme. Simulation results demonstrate the effectiveness of the proposed dynamic AP clustering approach in dealing with the networks of varying sizes. Moreover, benefiting from the well-designed frame structure, MDD is capable of outperforming TDD in the two-tier fronthaul networks. Additionally, the effect of the THz bandwidth on system performance is analyzed, and it is shown that empowered by sufficient bandwidth, our proposed two-tier fully-wireless fronthaul scheme can achieve a comparable performance to the fiber-optic based systems. Finally, the superiority of the proposed MDD-enabled fronthaul scheme is verified in a practical scenario with realistic ray-tracing simulations.

In orthogonal time sequency multiplexing (OTSM) modulation, the information symbols are conveyed in the delay-sequency domain upon exploiting the inverse Walsh Hadamard transform (IWHT). It has been shown that OTSM is capable of attaining a bit error ratio (BER) similar to that of orthogonal time-frequency space (OTFS) modulation at a lower complexity, since the saving of multiplication operations in the IWHT. Hence we provide its BER performance analysis and characterize its detection complexity. We commence by deriving its generalized input-output relationship and its unconditional pairwise error probability (UPEP). Then, its BER upper bound is derived in closed form under both ideal and imperfect channel estimation conditions, which is shown to be tight at moderate to high signal-to-noise ratios (SNRs). Moreover, a novel approximate message passing (AMP) aided OTSM detection framework is proposed. Specifically, to circumvent the high residual BER of the conventional AMP detector, we proposed a vector AMP-based expectation-maximization (VAMP-EM) detector for performing joint data detection and noise variance estimation. The variance auto-tuning algorithm based on the EM algorithm is designed for the VAMP-EM detector to further improve the convergence performance. The simulation results illustrate that the VAMP-EM detector is capable of striking an attractive BER vs. complexity trade-off than the state-of-the-art schemes as well as providing a better convergence. Finally, we propose AMP and VAMP-EM turbo receivers for low-density parity-check (LDPC)-coded OTSM systems. It is demonstrated that our proposed VAMP-EM turbo receiver is capable of providing both BER and convergence performance improvements over the conventional AMP solution.

Millimeter wave (mmWave) communications has been considered one of the key techniques for the future generations of wireless systems due to the large mmWave bandwidth available. In mmWave systems, channel state information (CSI) is critical for the design of the precoder and combiner for operations respectively at transmitter and receiver. In this article, we motivate to design the low-complexity and high-accuracy channel estimation methods for the mmWave systems employing orthogonal frequency division multiplexing (OFDM) signaling and hybrid transmitter/receiver beamforming. Specifically, a multi-layer sparse Bayesian learning (SBL) channel estimator is proposed to both improve the performance of channel estimation and reduce the complexity of signal processing, when compared with a range of related channel estimators, including the orthogonal matching pursuit (OMP)-, approximate message passing (AMP)- and conventional SBL-assisted channel estimators. The proposed multi-layer SBL estimator is compared with these legacy channel estimators, when impacts from different perspectives are considered. Furthermore, the Bayesian Cramer-Rao Bound of channel estimation is analyzed and evaluated. Our studies and simulation results show that the proposed multi-layer SBL estimator is capable of achieving better performance than the benchmark estimators considered. Specifically, when compared with the traditional SBL estimator, the proposed multi-layer SBL estimator is capable of achieving a lower mean-square error (MSE), while simultaneously, requiring only about 1/10 of the computational complexity of the traditional SBL estimator.

Space-time shift keying-aided orthogonal time frequency space modulation-based multiple access (STSK-OTFS-MA) is proposed for reliable uplink transmission in high-Doppler scenarios. As a beneficial feature of our STSK-OTFS-MA system, extra information bits are mapped onto the indices of the active dispersion matrices, which allows the system to enjoy the joint benefits of both STSK and OTFS signalling. Due to the fact that both the time-, space- and DD-domain degrees of freedom are jointly exploited, our STSK-OTFS-MA achieves increased diversity and coding gains. To mitigate the potentially excessive detection complexity, the sparse structure of the equivalent transmitted symbol vector is exploited, resulting in a pair of low-complexity near-maximum likelihood (ML) multiuser detection algorithms. Explicitly, we conceive a progressive residual check-based greedy detector (PRCGD) and an iterative reduced-space check-based detector (IRCD). Then, we derive both the unconditional single-user pairwise error probability (SU-UPEP) and a tight bit error ratio (BER) union-bound for our single-user STSK-OTFS-MA system employing the ML detector. Furthermore, the discrete-input continuous-output memoryless channel (DCMC) capacity of the proposed system is derived. The optimal dispersion matrices (DMs) are designed based on the maximum attainable diversity and coding gain metrics. Finally, it is demonstrated that our STSK-OTFS-MA system achieves both a lower BER and a higher DCMC capacity than its conventional spatial modulation (SM) and its orthogonal frequency-division multiplexing (OFDM) counterparts. As a benefit, the proposed system strikes a compelling BER vs. system complexity as well as BER vs. detection complexity trade-offs.

Reconfigurable intelligent surface (RIS) is capable of improving the wireless system performance by steering the reflected signal in the desired direction. One of the major challenges is that both the transceiver and RIS have to be jointly optimized, where the optimization problems have to be reformulated for different system models and scenarios. To circumvent this challenge, new low-complexity antenna selection (AS) algorithms for transceiver-agnostic RIS configuration are proposed. Given a multiple-input multiple-output (MIMO) channel, the proposed RIS-AS opts for accurately aligning the RIS both with the transmit antenna (TA) and receive antenna (RA) for the sake of maximizing the MIMO channel’s overall output power. The proposed RIS-AS only has to configure the RIS alone, i.e. without iterations with the transceiver optimization. As a result, the proposed RIS-AS has the compelling benefit that they are generically applicable, regardless of the specific transceiver architecture. Our simulation results confirm that the proposed RIS-AS is capable of supporting any MIMO configuration, regardless of their closed/open-loop, single-/ full-RF and multiplexing-/diversity-oriented setups.

A spatial modulation-aided orthogonal time frequency space (SM-OTFS) scheme is proposed for high-Doppler scenarios, which relies on a low-complexity distance-based detection algorithm. We first derive the delay-Doppler (DD) domain input-output relationship of our SM-OTFS system by exploiting an SM mapper, followed by characterizing the doubly-selective channels considered. Then we propose a distance-based ordering subspace check detector (DOSCD) exploiting the a priori information of the transmit symbol vector. Moreover, we derive the discrete-input continuous-output memoryless channel (DCMC) capacity of the system. Finally, our simulation results demonstrate that the proposed SM-OTFS system outperforms the conventional single-input-multiple-output (SIMO)-OTFS system, and that the DOSCD conceived is capable of striking an attractive bit error ratio (BER) vs. complexity trade-off.

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.

The separation of training and data transmission as well as the frequent uplink/downlink (UL/DL) switching make time-division duplex (TDD)-based massive multiple-input multiple-output (mMIMO) systems less competent in fast time-varying scenarios due to the resultant severe channel aging. To this end, a multicarrier-division duplex (MDD) mMIMO scheme associated with two types of well-designed frame structures are introduced for combating channel aging when communicating over fast time-varying channels. For comparison, the corresponding TDD frame structures related to the 3 rd Generation Partnership Project (3GPP) standards and their variant forms are presented. The MDD-specific general Wiener predictor and decision-directed Wiener predictor are introduced to predict the channel state information, respectively, in the time domain based on UL pilots and in the frequency domain based on the detected UL data, considering the impact of residual self-interference (SI). Moreover, by applying the zero-forcing precoding and maximum ratio combining, the closed-form approximations for the lower bounded rate achieved by TDD and MDD frame structures over time-varying channels are derived. Our main conclusion from this study is that the MDD, endowed with the capability of full-duplex but less demand on SI cancellation than in-band full-duplex (IBFD), outperforms both the conventional TDD and IBFD in combating channel aging.

A multicarrier-division duplex (MDD)-based cell-free (CF) scheme, namely MDD-CF, is proposed, which enables downlink (DL) data and uplink (UL) data or pilots to be concurrently transmitted on mutually orthogonal subcarriers in distributed CF massive MIMO (mMIMO) systems. To demonstrate the advantages of MDD-CF, we firstly study the spectral-efficiency (SE) performance in terms of one coherence time (CT) interval associated with access point (AP)-selection, power- and subcarrier-allocation. Since the formulated SE optimization is a mixed-integer non-convex problem that is NP-hard to solve, we leverage the inherent association between involved variables to transform it into a continuous-integer convex-concave problem. Then, a quadratic transform (QT)-assisted iterative algorithm is proposed to achieve SE maximization. Next, we extend our study to the case of one radio frame consisting of several CT intervals. In this regard, a novel two-phase CT interval (TPCT) scheme is designed to not only improve the SE in radio frame but also provide consistent data transmissions over fast time-varying channels. Correspondingly, to facilitate the optimization, we propose a two-step iterative algorithm by building the connections between two phases in TPCT through an iteration factor. Simulation results show that, MDD-CF can significantly outperform in-band full duplex (IBFD)-CF due to the efficient interference management. Furthermore, compared with time-division duplex (TDD)-CF, MDD-CF is more robust to high-mobility scenarios and achieves better SE performance.

Next-generation communication systems aim for providing pervasive services, including the high-mobility scenarios routinely encountered in mission-critical applications. Hence we harness the recently-developed reconfigurable intelligent surfaces (RIS) to assist the high-mobility cell-edge users. However, in the face of high Doppler frequencies, the existing RIS channel estimation techniques that assume block fading generally result in irreducible error floors. In order to mitigate this problem, we propose a new RIS channel estimation technique, which is the first one that performs minimum mean square error (MMSE) based interpolation for the sake of taking into account the time-varying nature of fading even within the coherence time. The RIS modelling invokes only passive elements without relying on RF chains, where both the direct link and RIS-reflected links as well as both the line-of-sight (LoS) and non-LoS (NLoS) paths are taken into account. As a result, the cascaded base station (BS)-RIS-user links involve the multiplicative concatenation of the channel coefficients in the LoS and NLoS paths across the two segments of the BS-RIS and RIS-user links. Against this background, we model the multiplicative RIS fading correlation functions for the first time in the literature, which facilitates MMSE interpolation for estimating the high-dimensional and high-Doppler RIS-reflected fading channels. Our simulation results demonstrate that for a vehicle travelling at a speed as high as 90 mph, employing a low-complexity RIS at the cell-edge using as few as 16 RIS elements is sufficient for achieving substantial power-effieincy gains, where the Doppler-induced error floor is mitigated by the proposed channel estimation technique.

Changes in oceanic variables, such as sea surface temperature (SST) and chlorophyll-a (Chl-a), have important implications for marine ecosystems and global climate change. The deep learning (DL) methods relying on convolutional neural networks can be employed to extract the spatial correlation for the prediction of oceanic variables. However, these methods are inflexible in the cases where some regions, e.g., land and islands, are invalid for the prediction of oceanic variables. By contrast, the graph convolutional network (GCN) is capable of capturing the large-scale spatial dependency existing in the irregular data. Owing to this, in this article, we propose a GCN-based method for the prediction of oceanic variables, including SST and Chl-a, with high accuracy, which is referred to as OVPGCN. The proposed OVPGCN consists of three modules aiming to fully extract the spatial correlation and temporal dependency via modeling the multicharacteristics of the spatio-temporal dynamic evolution. In particular, three modules are implemented to extract the stationary and nonstationary variations in the recent spatio-temporal sequences, the spatial differences between different sites, and the periodic features in historical data, respectively. The well-designed OVPGCN is applied to the monthly SST and Chl-a prediction in the Bohai Sea and the Northern South China Sea (NSCS). The performance demonstrates that the proposed OVPGCN is highly effective and enables to achieve much higher prediction accuracy than the state-of-the-art methods.

Long-term chlorophyll-a (Chl-a) prediction has the potential to provide an early warning of red tide and support fishery management and marine ecosystem health. The existing learning-based Chl-a prediction methods mostly predict a single point or multiple points with monitoring data. However, the monitoring data are subject to sparse sampling and difficult to be measured in a large-scale and synchronous way. Moreover, the advanced learning-based models for point Chl-a prediction, such as long short-term memory (LSTM) and convolutional neural network (CNN)-LSTM, are unable to fully mine the spatiotemporal correlation of Chl-a variations. Therefore, by using the satellite remote sensing data with extensive coverage, we design a framework, namely, Ca-STANet, to simultaneously predict the Chl-a of all the locations in a large-scale area from the perspective of spatiotemporal field. Specifically, in our method, the original data are first divided into multiple subregions to capture the spatial heterogeneity of large-scale area. Then, two modules are, respectively, operated to mine the spatial correlation and long-term dependency features. Finally, the outputs from the two modules are integrated by a fusion module to fully mine the spatiotemporal correlations, which are exploited to attain the final Chl-a prediction. In this article, the proposed Ca-STANet is comprehensively evaluated and compared with the legacy methods based on the OC-CCI Chl-a 5.0 data of the Bohai Sea. The results demonstrate that the proposed Ca-STANet is highly effective for Chl-a prediction and achieves higher prediction accuracy than the baseline methods. Moreover, as the OC-CCI Chl-a 5.0 data have many missing areas, we introduce DINEOF method to fill the data gaps before using them for prediction.

This paper proposes a multi-cluster wireless powered Internet of Things (WP-IoT) network assisted by multiple intelligent reflecting surfaces (multi-IRS). In this network, a power station (PS) first broadcasts wireless energy to the distributed IoT devices grouped into multiple clusters. The IoT devices then use the harvested energy to convey their information to an access point (AP), based on a hybrid time- and frequency-division multiple access (TDMA-FDMA) protocol. Furthermore, multiple IRSs are deployed to perform anomalous reflection for energy and information transfer, to improve energy harvesting and data transmission capabilities. Under the constraints of the unit-modulus phase shifts, the transmission time shared among clusters and the bandwidth shared by the devices in each cluster, the considered system is optimized by maximizing its sum throughput. The optimization problem is non-convex and with complicatedly coupled variables. To solve this problem, we propose to first apply the Lagrange dual method and the Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission scheduling and bandwidth allocation, then the quadratic transformation (QT) and the alternating optimization (AO) algorithm are introduced to solve the downlink and uplink IRS phase shifts, whilst the Majorization-Minimization (MM) and Riemannian Manifold Optimization (RMO) methods are applied to iteratively derive their closed-form solutions. Additionally, we provide a benchmark scheme to facilitate the system design, where each IRS can control its “on/off” state to aid the downlink and uplink transmissions in the condition of at most one activated IRS during one certain time duration. Finally, simulation results are presented to verify the optimality of our proposed scheme and highlight the beneficial role of the IRS.

Molecular type permutation shift keying (MTPSK) performs information transmission by the full permutations of different molecular types, which can combat the severe inter-symbol interference (ISI) in molecular communications. This letter proves that the achievable rate (AR) of MTPSK is restricted by its modulation principle and as a remedy proposes an enhanced (E-)MTPSK to improve the AR by conveying information through the permutations of partial molecular types. The exact and approximate ARs are derived for E-MTPSK, MTPSK, and molecular shift keying. Numerical results on ARs and bit error rates reveal the trade-off between the AR and ISI-mitigation capability, and that the proposed scheme can outperform the prevalent modulation counterparts.

Differential frequency hopping (DFH) technique is widely used in wireless communications by exploiting its capabilities of mitigating tracking interference and confidentiality. However, electronic attacks in wireless systems become more and more rigorous, which imposes a lot of challenges on the DFH sequences designed based on the linear congruence theory, fuzzy and chaotic theory, etc. In this article, we investigate the sequence design in DFH systems by exploiting the equivalence principle between the G-function algorithm and the encryption algorithm, in order to achieve high security. In more details, first, the novel G-function is proposed with the aid of the Government Standard algorithm and the Rivest–Shamir–Adleman algorithm. Then, two sequence design algorithms are proposed, namely, the G-function-assisted sequence generation algorithm, which takes the full advantages of the symmetric and asymmetric encryption algorithms, and the high-order G-function-aided sequence generation algorithm, which is capable of enhancing the correlation of the elements in a DFH sequence. Moreover, the security and ergodicity performance of the proposed algorithms are analyzed. Our studies and results show that the DFH sequences generated by the proposed algorithms significantly outperform the sequences generated by the reversible hash algorithm and affine transformation in terms of the uniformity, randomness, complexity, and the security.

In this letter, we propose a low-complexity detection algorithm for multi-input-multi-output (MIMO) molecular communication (MC). By virtue of a rising edge in received signals, this detection algorithm is capable of recovering the information bits in MIMO-MC systems. From an engineering point of view, this algorithm can significantly eliminate the impact of inter-symbol interference. It has also proven itself useful for dealing with the received signal from the MIMO-MC testbed with sufficiently low bit error rate (BER) or even an error-free performance. Moreover, in terms of BER performance, this algorithm outperforms existing detection algorithms for single-input single-output MC systems.

Recent advances in communication and networking technologies are leading to a plethora of novel wireless services that range from unmanned aerial vehicle (UAV) communication to smart cognitive networks and massive Internet of Things (IoT) systems. Enabling these emerging applications over the fifth generation (5G) of wireless cellular systems requires meeting numerous challenges pertaining to spectrum sharing and management. In fact, most 5G applications will be highly reliant on intelligent spectrum management techniques, which should adapt to dynamic network environments while also guaranteeing high reliability and high quality-of-experience (QoE). In this context, the use of artificial intelligence (AI) techniques that include deep learning, convolutional neural networks, and reinforcement learning, among many others, is expected to play a very important role in paving the way towards truly AI-driven spectrum management, thus enabling tomorrow's smart city services. Therefore, it has become imperative to investigate and apply AI techniques to solve emerging spectrum management problems in various wireless networks. This includes leveraging AI to address a wide range of wireless networking challenges ranging from network management to dynamic spectrum sharing and resource management.

In this letter, we propose a novel modularity-based dynamic clustering relying on modified Louvain method for unmanned aerial vehicles (UAVs)-aided mobile communications. Our aim is to save the transmit power of the mobile devices, by locating the UAVs vertically projected on the centroids of the user clusters. We further propose two types of operation for the modularity-based dynamic clustering, namely the recurring operation and the differential operation. We show that the proposed method requires substantially lower transmit power of the mobile devices and lower energy consumption of the UAVs than that required by the ${K}$ -means-based solution. We also show that the differential operation is more suitable for networks with lower proportion of moving users, since it consumes significantly less energy than that required by the recurring operation at the cost of requiring slightly higher transmit power of mobile devices.

Non-orthogonal multiple access (NOMA) is a promising technique for future mobile communication systems, which can approach multiuser channel capacity by sharing the same time-frequency resources with multiple users. In this article, we provide a unified framework for NOMA and review the principles of various NOMA schemes in different domains with the objective of creating a unified framework. A systematic performance comparison of different NOMA schemes regarding their peak-to-average power ratio, receiver complexity, latency, grant-free access, user load, and peak throughput is also provided for different application scenarios. Relying on our unified framework, we generalize the current understanding of the NOMA principle from the conventional code and power domains to the spatial domain as well as to their hybrids and to the networking domain. Finally, the challenges in terms of resource allocation, channel estimation, security, system flexibility, and implementation issues are also discussed.

We propose and investigate a full-duplex receiver assisted secure spatial modulation (FDR-SSM) scheme, whose security is enhanced by the jamming sent from a full-duplexing legal receiver. In our FDR-SSM system, jamming signals are designed to allow the legal receiver free from self-interference, while imposing time-varying interference on the passive eavesdropper. Therefore, the FDR-SSM scheme can maintain all the advantages of spatial modulation. In this letter, the secrecy performance of the FDR-SSM system is analyzed via analyzing its ergodic secrecy rate (ESR). Furthermore, the asymptotic ESR is analyzed, when assuming that the number of transmit antennas at the legal receiver approaches infinite. Finally, performance results are provided to demonstrate the efficiency of the FDR-SSM scheme, and to verify the analytical expressions.

The goals of the fifth generation (5G) cellular communication systems are to achieve 10–100 times higher typical user data rates, ranging from 1 to 10 Gbps in dense urban environments. 5G may support 10–100 times more connected devices. Furthermore, the energy efficiency for low-power massive machine communications is to be improved more than ten times. However, there is no clear roadmap to achieve such high performance targets. The introduction of new technologies in 5G systems is necessary. The aim of the papers in this special section highlight how massive MIMOmeet 5G requirements with developing mobile networks. These papers focus on new technologies being investigated that have great potential to let massive MIMO be practical.

In this contribution, the performance of wideband code-division multiple-access (W-CDMA) systems using space-time-spreading- (STS-) based transmit diversity is investigated, when frequency-selective Nakagami-fading channels, multiuser interference, and background noise are considered. The analysis and numerical results suggest that the achievable diversity order is the product of the frequency-selective diversity order and the transmit diversity order. Furthermore, both the transmit diversity and the frequency-selective diversity have the same order of importance. Since W-CDMA signals are subjected to frequency-selective fading, the number of resolvable paths at the receiver may vary over a wide range depending on the transmission environment encountered. It can be shown that, for wireless channels where the frequency selectivity is sufficiently high, transmit diversity may be not necessitated. Under this case, multiple transmission antennas can be leveraged into an increased bitrate. Therefore, an adaptive STS-based transmission scheme is then proposed for improving the throughput of W-CDMA systems. Our numerical results demonstrate that this adaptive STS-based transmission scheme is capable of significantly improving the effective throughput of W-CDMA systems. Specifically, the studied W-CDMA system's bitrate can be increased by a factor of three at the modest cost of requiring an extra 0.4 dB or 1.2 dB transmitted power in the context of the investigated urban or suburban areas, respectively.

This paper presents the evaluatian of a residue number system (RNS) based direct-sequence, code division multiple-access communication system with respect to bit error rote (BER), when the channel is modeled as a multipath Rayleigh fading and noncoherent square-law detection with equal gain combining (EGC) is emplayed. Bath the uncoded system and the well-known concatenated coded system with nonbinary Reed-Solomon (RS) codes as the outer codes and the so-coiled residue number system product codes (RNS-PC) as the Inner codes are concerned. Expressions of BER for both uncoded and concatenated coded systems are presented. Our results show that the proposed scheme constitutes a high efficiency parallel transmissian method for high-bit-rate COMA systems.

The vision of wireless 2020 is to become a smart, sustainable and inclusive economy, and as part of these priorities the world have set forth the 20:20:20 targets where greenhouse gas emissions should be reduced by 20 percent, whilst energy from renewable source efficiency should be increased by that respective amount. In fact, in today s energy conscious society, Information and Communication Technology (ICT) account for 2 percent of global CO2 emissions. To place this in perspective, a medium sized cellular network uses as much as 170,000 homes. Therefore, new solutions are required whereby operators can accommodate this additional traffic volume whilst reducing their investments in new infrastructure and beyond that significantly reducing their energy bill.

A novel iterative soft sequential-estimation (ISSE) method is proposed for the acquisition of m-sequences. The ISSE algorithm is designed based on the principle of iterative soft-in-soft-out (SISO) decoding. The employment of the proposed ISSE acquisition scheme is particularly beneficial for the acquisition of long m-sequences. Results demonstrate that a chip signal-to-noise ratio reduction of ∼10 4;dB is achievable at an erroneous acquisition probability of 10−4.