Affine frequency division multiplexing (AFDM) has been considered as a promising waveform to enable high-reliable connectivity in the internet of vehicles. However, accurate channel estimation is critical and challenging to achieve the expected performance of the AFDM systems in doubly-dispersive channels. In this paper, we propose a sparse Bayesian learning (SBL) framework for AFDM systems and develop a dynamic grid update strategy with two off-grid channel estimation methods, i.e., grid-refinement SBL (GR-SBL) and grid-evolution SBL (GE-SBL) estimators. Specifically, the GR-SBL employs a localized grid refinement method and dynamically updates grid for a high-precision estimation. The GE-SBL estimator approximates the off-grid components via first-order linear approximation and enables gradual grid evolution for estimation accuracy enhancement. Furthermore, we develop a distributed computing scheme to decompose the large-dimensional channel estimation model into multiple manageable small-dimensional sub-models for complexity reduction of GR-SBL and GE-SBL, denoted as distributed GR-SBL (D-GR-SBL) and distributed GE-SBL (D-GE-SBL) estimators, which also support parallel processing to reduce the computational latency. Finally, simulation results demonstrate that the proposed channel estimators outperform existing competitive schemes. The GR-SBL estimator achieves high-precision estimation with fine step sizes at the cost of high complexity, while the GE-SBL estimator provides a better trade-off between performance and complexity. The proposed D-GR-SBL and D-GE-SBL estimators effectively reduce complexity and maintain comparable performance to GR-SBL and GE-SBL estimators, respectively.

Smart alignment is a fundamental requirement for charging in the Internet of Things (IoT). The pose or relative pose is crucial for achieving this smart alignment. This paper addresses the deployment problem for anchors on the detector and tags on an object, leveraging range measurements for high accuracy. We designed a Reference Point Transformation (RPT) joint deployment algorithm, including a topology framework of deployment, a joint deployment model, and the RPT module in 6 Degrees of Freedom (6-DoF). First, the topology framework of deployment decreases the search space of the optimization problem. Second, the joint deployment model represents anchors and tags together mathematically, allowing the deployment problem to be treated in a unified framework. The final component of our framework aims to determine an optimal anchor/tag configuration at a designated target point. Specifically, using the translation and rotation between the reference and target points, the optimization criterion shifts from the Geometric Dilution of Precision (GDOP) at the reference point to the square root of the trace of the Cramér-Rao Lower Bound (CRLB) for the target point position. Thereafter, simulations and experiments were conducted to demonstrate the rationality of the RPT deployment algorithm compared with three other deployment algorithms. The good performance of the RPT deployment algorithm verifies that it is suitable for 6-DoF Rigid Body Localization (RBL). Finally, the extended performance analysis of the RPT deployment algorithm is conducted to illustrate two relationships related to the lower bound of localization errors. One is the detector dimensions under the topology framework. The other is the number of anchors and tags under the RPT deployment algorithm.

Federated Learning (FL) has become a prevalent distributed paradigm for privacy-sensitive edge applications, thanks to its decentralized data storage without raw data transmission. However, the distributed nature of FL brings critical model security challenges, especially for intellectual property ownership protection and malicious accountability tracing. Existing researches rarely propose a holistic solution that balances privacy preservation, model robustness and training efficiency concurrently. To address these limitations, this paper presents a novel framework named Secure and Traceable Federated Learning for Edge Computing (STFL-EC). Specifically, a K-medoids clustering based delay-aware client grouping method is designed to alleviate the straggler effect and improve training efficiency. Besides, a periodic watermark embedding scheme is developed to assign unique watermarks for different training cycles, enabling reliable ownership verification and tampering resistance. Moreover, an enhanced watermarking strategy combining traitor tracing and Enhanced Watermark Embedding (EWE) is proposed to ensure accountability with negligible model performance loss. Extensive experiments verify that STFL-EC reaches 98.3% watermark detection accuracy, induces less than 1% model performance degradation, and cuts total training latency by around 21%, offering a practical secure FL solution for resource-constrained edge environments.

This paper is concerned with sparse code multiple access (SCMA) with protograph-based low-density parity-check (P-LDPC) codes over Rayleigh fading channels. To be specific, we first propose a two-step design method consisting of both low-density factor graph (LDFG) and energy-based constellation superposition (ECS) principles to construct a new type of non-uniform codebooks (CBs), which have superior robustness against inter-user interference. We also develop a new segment-wise interleaved transmission (SWIT) scheme, which features a systematic optimization among repetition coding, resource mapping, and cross-slot data coupling, to further improve the performance of P-LDPC-coded SCMA. Additionally, we conceive a decoding-threshold-guided multi-objective optimization (DT-MOO) method to construct protograph-based enhanced multi-user codes (EMUCs), which exhibit excellent error-rate performance under both turbo and non-turbo decoding compared to traditional single-user and existing multi-user codes. Theoretical and simulated results validate the superiorities of proposed non-uniform SCMA scheme with SWIT and EMUCs compared to state-of-the-art benchmarks. Owing to the above advantages, the proposed designs are competent to provide massive connectivity and energy-efficient transmission for simultaneous wireless information and power transfer (SWIPT) applications in 6G networks.

This contribution focuses on the optimization of bit error rate (BER) performance in fluid antenna (FA)-aided wireless communication systems, which leverage the new degrees of freedom provided by positional flexibility, thus surpassing the limitations of conventional fixed-position antenna (FPA) systems. In this context, a theoretical analysis identifies key metrics for maximum likelihood (ML) and zero-forcing (ZF) detectors, specifically the minimum singular value and effective rank, as determinants of system performance. Then, for single-input single-output (SISO) channels, the optimization problem is formulated as channel gain maximization constrained by the predefined moving region and then solved using a mixed-integer linear programming (MILP) model. Furthermore, for multiple-input multiple-output (MIMO) channels, an alternating optimization (AO) algorithm incorporating the Frank-Wolfe method is proposed to optimize ML and ZF performances, targeting minimum singular value maximization for ML and singular value balancing for ZF, both subject to moving region and antenna spacing constraints. Finally, numerical results exhibit significant performance gains for the FA system over its conventional FPA alternative. In general, these findings highlight the potential of FA systems for addressing ultra-reliable communication challenges in the sixth generation (6G) wireless communications.

To meet the sustainable connectivity requirements of 6G terrestrial networks, energy self-sufficiency and large-scale scalability have emerged as critical challenges. The Simultaneous Wireless Information and Power Transfer (SWIPT) technology provides a promising technical route for Battery-free SWIPT-enabled Sensor Networks (BSSN). However, due to the dynamic coupling and uncertainty inherent in spatial-temporal data and energy flows, existing solutions struggle to balance scalability and energy sustainability. To tackle this issue, this paper proposes a Spatial-temporal Fuzzy logic-driven Topological Decoupling mechanism with Balanced Supply and Demand (SFTD-BSD). A Multidimensional Spatial-Temporal Fuzzy prediction (MSTF-prediction) model combining Analytic Hierarchy Process (AHP) and Sparse Bayesian Learning (SBL) is developed to predict data and energy flows and capture spatial-temporal correlations. An Optimized Case-based Reasoning Inference Rule Generation (OCR-IRG) method is designed to enable online learning and adaptive inference, thereby smoothing and suppressing prediction errors. A lightweight fuzzy output scheme is further proposed to achieve robust Master Node (MN) election, thereby realizing dynamic topology decoupling and energy self-sustainability with low computational overhead. Extensive experiments demonstrate that SFTD-BSD significantly outperforms LEACH, LEACH-R, HHCA, and GWOA-CH in energy sustainability and scalability, validating its effectiveness for energy-harvesting BSSN and future 6G terrestrial networks.

The significant progress of quantum sensing technologies offer numerous radical solutions for measuring a multitude of physical quantities at an unprecedented precision. Among them, Rydberg atomic quantum receivers (RAQRs) emerge as an eminent solution for detecting the electric field of radio frequency (RF) signals, exhibiting great potential in assisting classical wireless communications and sensing. So far, most experimental studies have aimed for the proof of physical concepts to reveal its promise, while the practical signal model of RAQR-aided wireless communications and sensing remained under-explored. Furthermore, the performance of RAQR-based wireless receivers and their advantages over classical RF receivers have not been fully characterized. To fill these gaps, we introduce the RAQR to the wireless community by presenting an end-to-end reception scheme. We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow. Our scheme and model provide explicit design guidance to RAQR-aided wireless systems. We next study the performance of RAQR-aided wireless systems based on our model, and compare them to classical RF receivers. The results show that Doppler broadening-free RAQRs are capable of achieving a substantial received signal-to-noise ratio (SNR) gain of over 27 decibel (dB) and 40 dB in the photon shot limit and standard quantum limit regimes, respectively.

This work investigates iterative receiver and code design for protograph-coded overloaded sparse code multiple access (SCMA) systems. As a type of non-orthogonal multiple access (NOMA) technology, SCMA relies on an iterative detection and decoding (IDD) receiver to eliminate inter-user interference and channel noise. However, the existing IDD receiver requires a larger number of iterations to achieve successful convergence. To overcome this limitation, we propose aranked successive detection and decoding(R-SDD) receiver, which dynamically allocates computational resources to bottleneck users with the lowest reliability. This approach accelerates information convergence and improves error performance without increasing overall complexity. Moreover, we introduce afactor-graph-based(FG) interleaving scheme for the proposed SCMA systems, which outperforms the existing interleaver. To further enhance the error-correction capability for overloaded multi-user scenarios, we propose a new protograph design principle tailored for SCMA systems and develop two high-performance protograph low-density parity-check (LDPC) codes, achieving over 0.3 dB improvement compared with the state-of-the-art alternatives. Owing to the salient advantages of the new iterative receiver and code design, the proposed SCMA system offers a promising solution for future high-bandwidth, high-reliability communication networks.

As sixth-generation (6G) wireless networks demand unprecedented connectivity and interference management capabilities, rate-splitting multiple access (RSMA) emerges as a promising solution through common and private stream partitioning and remains effective across a range of channel state information at the transmitter (CSIT) qualities and traffic heterogeneity. In practical multiuser deployments, two considerations arise: the common stream decoding constraint imposed by the weakest user, and residual inter-user interference can remain non-negligible-particularly in single-input single-output (SISO) broadcast settings and under an imperfect CSIT scenario. Motivated by prior advances of RSMA research, we investigate a complementary mechanism-fluid antenna systems (FAS) with dynamic port reconfiguration-that supplies adaptive spatial selectivity without altering the RSMA signaling structure. Can FAS help alleviate these considerations and enhance RSMA performance? This paper demonstrates that dynamic port reconfiguration in FAS provides adaptive spatial selectivity that can strengthen the weakest user’s effective channel, improve signal-to-interference-plus-noise (SINR) ratios through enhanced channel gains and reduced relative noise impact. We develop a tractable, correlation-aware analytical framework that captures realistic spatial dependencies through advanced block-correlation modeling, considering both constant block correlation (CBC) and variable block correlation (VBC) variants. Our analysis yields closed-form expressions for outage probability (OP) and ergodic rate (ER) that quantify the impact of FAS on RSMA performance. Extensive simulations validate our theoretical findings: VBC-based results exhibit consistently tighter agreement with Monte Carlo simulations than CBC across all port configurations. Moreover, FAS-RSMA achieves enhancing performance gains over traditional fixed-position antenna (FPA) and non-orthogonal multiple access (NOMA), demonstrating lower OP and substantially higher ER through the synergy of RSMA’s flexible interference management and FAS’s adaptive spatial diversity.

Inter-satellite communication (ISC) is a critical component of low Earth orbit (LEO) satellite communication systems, enabling seamless data exchange between satellites within the same or across different orbital planes. This paper investigates the impact of position and attitude perturbations caused by gravitational forces, theJ2effect, aerodynamic torques, and Earth’s magnetic field between inter-orbital LEO satellite pairs on ISC channels. It reveals that such perturbations significantly degrade the LEO ISC link performance at the terahertz (THz) band. To address this challenge, a novel statistical symbol-level power and modulation adaptation (SSL-PMA) algorithm is proposed to mitigate the effects of the perturbations, enhance spectral efficiency, and reduce transmission power consumption under varying perturbation levels. Simulation results demonstrate that the proposed algorithm achieves substantial spectral efficiency gains over conventional adaptive and non-adaptive schemes, maintaining bit error rate (BER) performance comparable to or better than that of fixed modulation schemes across different perturbation levels. Notably, transmission power savings of up to 40% are achieved at inter-plane angles of 90◦ and 150◦, with slightly lower savings at 30◦. These findings highlight the potential of the proposed SSL-PMA algorithm for efficient and adaptive transmission in future THz-band inter-satellite communication systems.

The impact of both multiplicative and additive hardware impairments (HWIs) on multiple-input multiple-output affine frequency division multiplexing (MIMO-AFDM) systems is investigated. For small-scale MIMO-AFDM systems, a tight bit error rate (BER) upper bound associated with the maximum likelihood (ML) detector is derived. By contrast, for large-scale systems, a closed-form BER approximation associated with the linear minimum mean squared error (LMMSE) detector is presented, including realistic imperfect channel estimation scenarios. Our first key observation is that the full diversity order of a hardware-impaired AFDM system remains unaffected, which is a unique advantage. Furthermore, our analysis shows that 1) the BER results derived accurately predict the simulated ML performance in moderate-to-high signal-to-noise ratios (SNRs), while the theoretical BER curve of the LMMSE detector closely matches that of the Monte-Carlo based one. 2) MIMO-AFDM is more resilient to multiplicative distortions, such as phase noise and carrier frequency offset, compared to its orthogonal frequency division multiplexing (OFDM) counterparts. This is attributed to its inherent chirp signal characteristics; 3) MIMO-AFDM consistently achieves superior BER performance compared to conventional MIMO-OFDM systems under the same additive HWI conditions, as well as different velocity values. The latter is because MIMO-AFDM is also resilient to the additional inter-carrier interference (ICI) imposed by the nonlinear distortions of additive HWIs. In a nutshell, compared to OFDM, AFDM demonstrates stronger ICI resilience and achieves the maximum full diversity attainable gain even under HWIs, thanks to its intrinsic chirp signalling structure as well as to the beneficial spreading effect of the discrete affine Fourier transform.

The study of anytime codes over non-ergodic block-fading (BF) channels remains largely unexplored. To address this gap, we propose a novel root-protograph-based anytime (RP-anytime) coding framework and develop modified density evolution (DE) equations. Leveraging these equations, we introduce two performance metrics to evaluate the asymptotic behavior of RP-anytime codes under the assumption of infinite frame length: the average asymptotic bit error rate (BER), which characterizes anytime reliability, and the anytime outage region, which reflects the code’s ability to achieve full diversity. Using these metrics, we analyze the performance of the proposed RP-anytime codes and compare them with both root-protograph LDPC codes and conventional anytime codes. The results demonstrate the superiority of the RP-anytime structure in terms of reliability and diversity over non-ergodic BF channels. Furthermore, we employ a multi-objective differential evolution (MODE) algorithm to design optimized RP-anytime codes. Simulation results confirm that the newly designed codes achieve excellent anytime reliability, full diversity, and near-outage-limit error performance—significantly outperforming prior-art root-protograph LDPC codes and anytime codes.

Clustering mechanism and the Wireless Energy Transfer (WET) technology are pivotal for enhancing the energy efficiency of the Wireless Sensor Network (WSN). However, existing clustering algorithms often fail to fully optimize energy efficiency, while WET face the challenge of spatial-temporal instability in energy. To address these challenges, an Energy efficient Clustering Approach based on wireless Energy Provision (ECAEP) mechanism is proposed in this paper. Specifically, a Clustering method integrated the Probability Theory and Multiple Factors (CPMF) is presented, in which multiple factors are taken into consideration to optimize cluster head election. Additionally, an energy provision mechanism based on WET is designed to enable mutual energy complementarity among sensor nodes. The issue of energy supply path planning is transformed into a shortest path problem, which is solved through the Dijkstra-based Energy efficient Supply Attenuation Coefficient (DESAC) algorithm. Extensive simulations are conducted to evaluate the effectiveness of ECAEP, and results exhibit improvements in packet delivery to the Sink by 26.5%, 27.0%, and 35.3% over LEACH; by 28.9%, 8%, and 8.5% over R-LEACH, by 12.3%, 14.0%, and 15.1% over HHCA, by 6.3%, 6.1%, and 10.9% over GWOA-CH in three different scenarios respectively.

The intrinsic integration of Rydberg atomic receivers into wireless communication systems is proposed, by harnessing the principles of quantum physics in wireless communications. More particularly, we conceive a pair of Rydberg atomic receivers, one incorporates a local oscillator (LO), referred to as an LO-dressed receiver, while the other operates without an LO and is termed an LO-free receiver. The appropriate wireless model is developed for each configuration, elaborating on the receiver's responses to the radio frequency (RF) signal, on the potential noise sources, and on the signal-to-noise ratio (SNR) performance. The developed wireless model conforms to the classical RF framework, facilitating compatibility with established signal processing methodologies. Next, we investigate the associated distortion effects that might occur, specifically identifying the conditions under which distortion arises and demonstrating the boundaries of linear dynamic ranges. This provides critical insights into its practical implementations in wireless systems. Finally, extensive simulation results are provided for characterizing the performance of wireless systems, harnessing this pair of Rydberg atomic receivers. Our results demonstrate that LO-dressed systems achieve a significant SNR gain of approximately 40~50 dB over conventional RF receivers in the standard quantum limit regime. This SNR head-room translates into reduced symbol error rates, enabling efficient and reliable transmission with higher-order constellations.

The recently proposed affine frequency division multiplexing (AFDM) modulation has been considered as a promising technology for narrowband doubly-dispersive channels. However, the time-scaling effects, i.e., pulse widening and pulse shortening phenomena, in extreme wideband doubly-dispersive channels have not been considered in the literatures. In this paper, we investigate such wideband transmission and develop an efficient transmission structure with chirp-periodic prefix (CPP) and chirp-periodic suffix (CPS) for AFDM system. We derive the input-output relationship of AFDM system under time-scaled wideband doubly-dispersive channels and demonstrate the sparsity in discrete affine Fourier (DAF) domain equivalent channels. We further optimize the AFDM chirp parameters to accommodate the time-scaling characteristics in wideband doubly-dispersive channels and verify the superiority of the derived chirp parameters by pairwise error probability (PEP) analysis. We also develop an efficient cross domain distributed orthogonal approximate message passing (CD-D-OAMP) algorithm for AFDM symbol detection and analyze its corresponding state evolution. By analyzing the detection complexity of CD-D-OAMP detector and evaluating the error performance of AFDM systems based on simulations, we demonstrate that the AFDM system with our optimized chirp parameters outperforms the existing competitive modulation schemes in time-scaled wideband doubly-dispersive channels. Moreover, our proposed CD-D-OAMP detector can achieve the desirable trade-off between the complexity and performance, while supporting parallel computing to significantly reduce the computational latency.

Orbital angular momentum (OAM) can enhance the spectral efficiency by multiplying a set of orthogonal modes on the same frequency channel. To maintain the orthogonal among different OAM modes, perfect alignments between transmitters and receivers are strictly required. However, in multi-user OAM communications, the perfect alignments between the transmitter and all the receivers are impossible. The phase turbulence, caused by misaligned transmitters and receivers, leads to serious inter-mode interference, which greatly degrades the capacity of OAM transmissions. To eliminate the negative effects of phase turbulence and further enhance the transmission capacity, we introduce RIS into the system, and propose a joint precoder and reflector design for reconfigurable intelligent surface (RIS)-assisted multi-user OAM communication systems. Specifically, we propose a three-layer design at the transmitter side, which includes inter-user OAM mode interference cancellation, intermode self-interference elimination and the power allocation among different users. By analyzing the characteristics of the overall channels, we are able to give the specific expressions of the precoder designs, which significantly reduce the optimization complexity. We further leverage RIS to guarantee the line-of-sight (LoS) transmissions between the transmitter and users for better sum rate performance. To verify the superiority of the proposed multi-user OAM transmission system, we compare it with traditional MIMO transmission schemes, numerical results have shown that our proposed design can achieve better sum rate performance due to the well-designed orthogonality among different users and OAM modes.

Stacked intelligent metasurfaces (SIMs), which integrate multiple programmable metasurface layers, have recently emerged as a promising technology for advanced wave-domain signal processing. SIMs benefit from flexible spatial degree-of-freedom (DoF) while reducing the requirement for costly radio-frequency (RF) chains. However, current state-of-the-art SIM designs face challenges such as complex phase shift optimization and energy attenuation from multiple layers. To address these aspects, we propose incorporating meta-fibers into SIMs, with the aim of reducing the number of layers and enhancing the energy efficiency. First, we introduce a meta-fiber-connected 2-layer SIM that exhibits the same flexible signal processing capabilities as conventional multi-layer structures, and explains the operating principle. Subsequently, we formulate and solve the optimization problem of minimizing the mean square error (MSE) between the SIM channel and the desired channel matrices. Specifically, by designing the phase shifts of the meta-atoms associated with the transmitting-SIM and receiving-SIM, a non-interference system with parallel subchannels is established. In order to reduce the computational complexity, a closed-form expression for each phase shift at each iteration of an alternating optimization (AO) algorithm is proposed. We show that the proposed algorithm is applicable to conventional multi-layer SIMs. The channel capacity bound and computational complexity are analyzed to provide design insights. Finally, numerical results are illustrated, demonstrating that the proposed two-layer SIM with meta-fiber achieves over a 25% improvement in channel capacity while reducing the total number of meta-atoms by 59% as compared with a conventional seven-layer SIM.

Artificial noise (AN) has been recognized as an effective physical-layer security scheme impairing the eavesdropper (Eve). Recently, artificial noise elimination (ANE) has emerged as a promising strategy to mitigate the impact of AN at Eves. However, conventional ANE schemes rely on prior knowledge, such as legitimate channel state information (CSI) or classification information, which may limit their practical applicability. To address these practical challenges, we propose an ANE scheme beyond prior knowledge (BPK) by leveraging machine learning algorithms. Firstly, a coarse projection is applied to partially eliminate the impact of AN using maximum likelihood estimation on the equivalent AN matrix. Secondly, a density clustering algorithm is introduced to obtain classification information based on the coarsely-projected observed vectors. Thirdly, a generalized principal component analysis (PCA)-based ANE algorithm is developed to effectively mitigate the residual AN using the obtained classification information. Furthermore, the artificial-noise-to-signal ratio (ANSR) and computational complexity are analyzed for performance revaluation, and a redefinition of several AN design principles is provided for scenarios involving a powerful Eve equipped with the BPK-ANE scheme by deriving the validity boundary. Finally, numerical results reveal key insights into four principles of AN: 1) Allocating less power to AN; 2) Reducing the randomness of AN; 3) Increasing the number of transmit antennas; and 4) Increasing the modulation order.

In this paper, we propose to exploit movable elements of intelligent reflecting surface (IRS) to enhance the overall performance of integrated sensing and communication (ISAC) systems. Firstly, focusing on a single-user scenario, we reveal the function of movable elements by performance analysis, and then design a joint beamforming and element position optimization scheme. Further, we extend it to a general multi-user scenario, and also propose an element position optimization scheme according to the derived performance expressions. Finally, simulation results confirm that the movement of IRS elements can improve the communication rate and the sensing accuracy, and especially broaden the coverage of ISAC.

Integrated sensing and communication (ISAC) opens new opportunities for next-generation systems by supporting simultaneous communication and sensing within a unified framework. In this paper, we investigate the waveform design and parameter estimation problem for orthogonal time-frequency-space (OTFS)-based ISAC in monostatic multiuser scenarios under dynamic multipath and cluttered environments. A key challenge lies in developing an OTFS-ISAC waveform to establish a structured input-output relationship, which is critical for enabling high-accuracy parameter estimation. To overcome this, we construct a periodic convolution sum spectrum (CSS) by designing practical transceiver filters, yielding a tractable received signal model. The corresponding optimization problem is solved efficiently within the alternating direction method of multipliers (ADMM) framework, where closed-form solutions are derived to avoid reliance on high-complexity numerical solvers. Furthermore, strong clutter in the environment makes it difficult for the base station (BS) to detect non-line-of-sight (NLoS) echoes, hindering full communication channel reconstruction. To address this issue, we adopt a hierarchical estimation scheme and embed pilot sequences (PSs) within a tailored delay-Doppler (DD) pattern. In addition, a weighted integrated sidelobe level (WISL)-based PS design is introduced, which leverages coordinate descent (CD) and discrete Fourier transform (DFT) techniques to support short sequence lengths. Building on the proposed OTFS-ISAC waveform, which integrates transceiver filters, DD pattern, and optimized PS, we propose a successive parameter estimation (SPE) algorithm with a dual-direction estimation strategy. By exploiting inter-Doppler interference (IDI) dispersion, the algorithm derives closed-form channel parameter estimates, applicable at both the BS and user equipments (UEs). Simulation results demonstrate that the proposed PS design method exhibits near-optimal correlation properties, while the estimation algorithm achieves competitive performance with significantly reduced complexity.

Extremely large-scale reconfigurable intelligent surface (XL-RIS) can effectively overcome severe fading and provide higher communication performance. However, current research on XL-RIS overlooks the discrete phase-shift characteristics of RIS in practical systems, which will result in significant performance degradation. In this paper, we investigate near-field communication schemes assisted by XL-RIS with discrete phase shifts. Specifically, we propose a hierarchical beam training method to obtain the user channel state information (CSI), and develop the jointly optimized codebook construction (JOCC) method and separately optimized codebook construction (SOCC) method for base station (BS) precoding and XL-RIS phase shifts, respectively. With JOCC, the most superior beam training performance can be obtained. With SOCC, higher performance than the single-antenna BS codebook can be obtained at a similar complexity. Further, we propose a flexible multiuser interference management (IM) method that is simple to solve. The IM method uses adaptive gain matrix approximation to take into account user fairness and can be solved in closed-form iterations. In addition, we extend the proposed method to a hybrid precoding design. Simulation results demonstrate that the proposed multi-resolution codebook construction method can obtain more accurate beam patterns and user CSI, and the proposed IM method obtains superior performance over the benchmark methods.

Stacked intelligent metasurfaces (SIM) provide a cost-effective and scalable solution for large-scale antenna communications. However, efficient channel state information acquisition and phase shift optimization remain critical challenges. In this paper, we develop a unified framework of low-complexity algorithms for SIM-assisted communication systems to address these issues. Specifically, we propose a generalized two-step codebook construction (TSCC) method that lever-ages two-dimensional angular-domain decoupling to transform planar array beamformer design into two independent one-dimensional linear array beamformer design problems, efficiently solved via the Gerchberg–Saxton algorithm and our proposed majorization–minimization-based proximal-distance (PDMM) algorithm. We further develop a two-stage coded-sliding beam training (TSCSBT) method for low-overhead and high-accuracy beam training, where error-correcting codes are embedded in the first-stage training to enhance robustness against noise, and sliding sampling is subsequently performed around the matched angular samples to improve angular resolution. The proposed framework is further extended to multi-path user channels. Finally, a variable decoupling-based block successive upper bound minimization (VD-BSUM) algorithm is proposed to directly solve the QoS-constrained sum-rate maximization problem through closed-form iterative updates with substantially reduced computational complexity. Simulation results demonstrate the effectiveness of the proposed methods in achieving precise beam pattern realization, improved beam training accuracy and angular resolution, and enhanced sum-rate performance.

Massive multi-input multi-output (MIMO) combined with orthogonal time frequency space (OTFS) modulation has emerged as a promising technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) massive MIMO-OTFS systems. Specifically, first, an iterative basis expansion model (BEM) based UL channel estimation scheme is proposed to accurately estimate UL channels with the aid of carefully designed OTFS frame pattern. Then a set of Slepian sequences are used to model the estimated UL channels, and the dynamic Slepian coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels by iteratively extrapolating the Slepian coefficients. Simulation results verify that the proposed UL channel estimation and DL channel prediction schemes outperform the existing schemes in terms of normalized mean square error of channel estimation/prediction and DL spectral efficiency, with less pilot overhead.

Integrated sensing and communication (ISAC) technology is crucial for next-generation underwater networks. However, covering multiple users and targets and balancing sensing and communication performance in complex under-water acoustic (UWA) environments remains challenging. This paper proposes an interleaved orthogonal frequency division multiplexing-based MIMO UWA-ISAC system, which employs a horizontal array to simultaneously transmit adaptive waveforms for downlink multi-user communication and omnidirectional target sensing. A multi-objective optimization framework is formulated to maximize the product of communication rate and range (PRR) while ensuring sensing performance and peak-to-average power ratio (PAPR) constraints. To solve this mixed-integer nonconvex problem, a two-dimensional grouped random search algorithm is developed, efficiently exploring subcarrier interleaved patterns and resource allocation schemes. Numerical simulations under real-world UWA channels demonstrate the designed system’s superiority and effectiveness: our algorithm achieves 90% faster convergence than conventional exhaustive search with only a marginal 0.5 kbps•km PRR degradation. Furthermore, the proposed resource allocation scheme maintains robustness beyond the baseline allocation schemes under stringent PRR and PAPR constraints.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation communication systems. In this paper, a dual-mapping SVC (DM-SVC) based short packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme lies in mapping the transmitted information bits onto sparse vectors via block and single-element sparse mappings. The block sparse mapping pattern is able to concentrate the transmit power in a small number of non-zero blocks thus improving the decoding accuracy, while the single-element sparse mapping pattern ensures that the code length does not increase dramatically with the number of transmitted information bits. At the receiver, a two-stage decoding algorithm is proposed to sequentially identify non-zero block indexes and single-element non-zero indexes. Extensive simulation results verify that proposed DM-SVC scheme outperforms the existing SVC schemes in terms of block error rate and spectral efficiency.

To further enhance the performance of intelligent reflecting surface (IRS) aided integrated sensing and communication (ISAC) system, the conception of movable reflection elements is introduced to IRS. We derive the performance bound of both communication and sensing in the context of movable reflection elements, and then design a joint transmit beamforming and element position design algorithm. Numerical results demonstrate that the proposed algorithm, using angular information, aligns with the performance of both communicationonly systems and sensing-only systems with perfect channel state information (CSI).

In this paper, a new waveform called discrete Fourier transform spread orthogonal frequency division multiplexing with chirp modulation (DFT-s-OFDM-CM) is proposed for the next generation of wireless communications. The information bits are conveyed by not only Q-ary constellation symbols but also the starting frequency of chirp signal. It could maintain the benefits provided by the chirped discrete Fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM), e.g., low peak-to-average power ratio (PAPR), full frequency diversity exploitation, etc. Simulation results confirm that the proposed DFT-s-OFDM-CM could achieve higher spectral efficiency while keeping the similar bit error rate (BER) to that of chirped DFT-s-OFDM. In addition, when maintaining the same spectral efficiency, the proposed DFT-s-OFDM-CM with the splitting of information bits into two streams enables the use of lower-order constellation modulation and offers greater resilience to noise, resulting in a lower BER than the chirped DFT-s-OFDM.

This paper introduces an advanced near-field multi-user access system that integrates the programmable metasurface, also known as Reconfigurable Intelligent Surfaces (RIS), with Orbital Angular Momentum (OAM) technology to enhance spectral efficiency and reduce interference. The system employs a multi-mode OAM transmitter to generate signals carrying multiple data streams, which are directed toward a metasurface-based RIS. The RIS is designed to receive incoming OAM beams, demultiplex the data, and dynamically focus the signals on specific spatial regions, ensuring high Signal-to-Noise Ratio (SNR) and minimal interference for efficient multi-user transmission. To achieve adaptive beam control, a 2-bit transmissive RIS is utilized, allowing dynamic adjustments of OAM modes and enabling precise energy focusing at various user locations in the near-field. The orthogonality of OAM modes further contributes to increased spectral efficiency. Furthermore, extensive full-wave simulations and a complete communication test environment are developed, covering the entire transmission process from the OAM transmitter to the RIS-assisted communication link. Experimental results demonstrate that multi-mode OAM beams are effectively converted to spot focusing through RIS and achieve the same-frequency data separation at each focal point. This novel approach offers a high-spectral-efficiency, low-interference communication solution. It provides valuable insights into improving multi-user access and data transmission efficiency in various IoT applications, including smart factories, logistics hubs, and in-vehicle communication networks.

Collision prediction is crucial for safety in Vehicle-to-Everything (V2X) networks, essential for smart cities and intelligent transportation. Traditional methods struggle with complexity, while learning-based approaches face challenges in feature extraction and prediction accuracy. To address this, we propose attentive TabNet for collision prediction in V2X networks. Our model utilizes attention mechanisms in deep neural networks to focus on key spatial and temporal features, enhancing prediction reliability. We also apply data preprocessing to handle imbalances. Experimental evaluations using real-world V2X data show that our method outperforms random forest and neural network-based algorithms in accuracy and efficiency. Both simulations and real-world tests confirm its effectiveness. By improving collision prediction, our approach enhances road safety, advancing intelligent transportation and smart city initiatives.

Academia and industry are constantly striving to improve the driving experience, where vehicles can now communicate, and different technologies can coexist and share the wireless medium to send and receive information. In terms of vehicle-to-vehicle (V2V) communication, vehicles can use Dedicated Short-Range Communication (DSRC) or Cellular Vehicle-to-Everything (C-V2X) sidelink technologies to communicate on the roads by sharing the 5.9 GHz band. However, coexistence between the two technologies remains challenging, as interference can occur when both technologies use co-channels to send vehicle messages in parallel, degrading the Packet Delivery Ratio (PDR) metric. In addition, malicious users can carry out various types of attacks to disrupt the vehicle connection, such as jamming attacks. In this paper, we transmitted packets using a constant bit rate (CBR). We considered Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios (i.e., in the DSRC communication) to understand how damaging the jamming attack can be. Based on our tests, the jamming attack caused by the C-V2X co-channel interference can cause an attenuation in the signal loss of up to 24 dB (i.e., using a packet size of 256 bytes) and up to 35 dB (i.e., using a packet size of 1,399 bytes), and signal-to-interference and noise ratio (SINR) by up to 26 dB (i.e., using a packet size of 256 bytes) and up to 40 dB (i.e., using a packet size of 1,399 bytes). Furthermore, we analyzed the interference effects in different data rates such as 3, 4.5, 6, 9, 12, 18, 24, and 27 Mbps.

With the rapid increase in the number of vehicles, research on license plate recognition (LPR) is becoming increasingly important. However, most of the existing research fails to meet the requirement on fast recognition speed and high recognition accuracy concurrently. In this article, a delay-precision-balanced approach for LPR based on fog (DLPRF) computing is proposed, with the aim of reducing the response latency while ensuring the recognition accuracy. Specifically, a new license plate detection (LPD) framework YOLOv5LPD is proposed, which employs the bidirectional feature pyramid network (BiFPN) as an intermediate layer to effectively integrate features of different scales. By means of removing the detection head for large objects, YOLOv5LPD is able to simplify the network structure and reduce the complexity. Additionally, a lightweight convolutional recurrent neural network (CRNN) is presented for license plate character recognition without the operation of character segmentation. Furthermore, a fog computing-based LPR task offloading algorithm is proposed to significantly reduce the queuing delay in task transmission and execution. Experimental results demonstrate that with dataset Chinese City parking dataset (CCPD), YOLOv5LPD achieves an$mAP0.5:0.95$of 0.786 in LPD, with a speed improvement of 7% and 30% over YOLOv5s and YOLOv4, respectively. In addition, YOLOv5LPD still holds 95.2% accuracy in cross-data LPR experiments, demonstrating the robustness of the model. Meanwhile, the task offloading algorithm in DLPRF also greatly enhances response speed.

The Position information is essential for large-scale Internet of Things (IoT) devices and services. Multipath and non-line of sight (NLOS) effects introduce additional delays in pseudorange measurements in urban areas. It is one of the main unmodeled errors in Global Navigation Satellite Systems (GNSS). To mitigate interference, various techniques have been developed, including antenna design and sensor fusion. However, traditional estimation approaches often produce biased estimates under the additional path delays. To improve estimation accuracy and robustness, we present an Observation Space Representation Refinement (OSRR) algorithm. The initial position is estimated by least squares without the additional path error. Then, the multipath projection method is used to get possible compensation in pseudorange measurements. Subsequently, the Moving Horizontal Estimation (MHE) is leveraged to get the position with corrected observation space. Field experiments demonstrate that the proposed OSRR algorithm significantly reduces the impact of interference on positioning accuracy. There is no empirical constraint to easily adapt to real-time static and kinematic GNSS pseudorange positioning with unilateral obstruction scenarios.

The recently proposed multi-chirp waveform, affine frequency division multiplexing (AFDM), is considered as a potential candidate for integrated sensing and communication (ISAC). However, acquiring accurate target sensing parameter information becomes challenging due to fractional delay and Doppler shift occurrence, as well as effects introduced by the coexistence of near-field (NF) and far-field (FF) targets associated with large-scale antenna systems. In this paper, we propose a novel angle-delay-Doppler estimation scheme for AFDM-ISAC system in mixed NF and FF scenarios. Specifically, we model the received ISAC signals as a third-order tensor that admits a low-rank CANDECOMP/PARAFAC (CP) format. By employing the Vandermonde nature of the factor matrix and the spatial smoothing technique, we develop a structured CP decomposition method that guarantees the condition for uniqueness. We further propose a low-complexity estimation scheme to acquire target sensing parameters with fractional values, including angle of arrival/departure (AoA/AoD), delay and Doppler shift accurately. We also derive the Cramér-Rao Lower Bound (CRLB) as a benchmark and analyze the complexity of our proposed scheme. Finally, simulation results are provided to demonstrate the effectiveness and superiority of our proposed scheme.

In this paper, a novel three-stage data-aided dual-space (DADS) (i.e., beamspace and signal subspace) channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems subject to pilot contamination. By exploiting the orthogonality of signal subspace, the non-overlapping interference caused by pilot contamination is identified and mitigated in the coarse channel estimate via subspace projection. Thanks to the independence of transmitted data between users, the overlapping interference is suppressed through alternating iterative refinement of the channel estimate and detected data. Additionally, to initially address the under-determined estimation problem arisen from hybrid beamforming (HBF) structures, an improved matching pursuit algorithm is proposed for coarse sparse beamspace channel estimation by appropriately selecting the scaling factor and adjusting the step size in a piecewise manner, followed by enhancement via subspace projection. Furthermore, by accurately detecting the overlap level of interference in the beamspace, the proposed channel estimation scheme selects an appropriate channel enhancement or refinement strategy to address both non-overlapping and overlapping interference subject to several typical channels without significantly increasing computational complexity. Simulation results demonstrate that the proposed channel estimation scheme achieves higher channel estimation accuracy and exhibits stronger resilience to both interference intensity and the number of interference compared to existing channel estimation schemes.

Presents corrections to the paper, Coverage Rate Analysis for Integrated Sensing and Communication Networks.

This work studies the transceiver design and convergence performance analysis for the hierarchical modulation (HM) systems with protograph-based low-density parity-check (P-LDPC) codes. Specifically, we first conceive new source-constrained (SC) coding scheme and inter-layer-cascaded (ILC) decoding scheme tailored for the HM-based transmitter and receiver, respectively. Both the proposed SC coding and ILC decoding schemes form an enhanced version of bit-interleaved-coded HM (BIC-HM) systems, namely source-constrained HM (SC-HM) systems, providing a more reliable and flexible multi-data transmission solution. Moreover, according to the SC coding principle, we conceive a novel variable-node-degree-based (VND) multiplexing scheme to further improve the performance of the proposed SC-HM systems. Additionally, based on the ILC decoding framework, we devise a novel mutual information (MI) analysis tool, namely ILC-based extrinsic-information-transfer (ILC-EXIT) algorithm, to predict the decoding thresholds of the proposed P-LDPC-coded SC-HM systems. Theoretical and simulation results demonstrate that the proposed SC-HM systems significantly outperform the existing benchmarks in terms of error performance, decoding latency, and transmission-rate adaptation.

To enhance the adaptability and performance of multi-stream transmissions over non-ergodic block fading (BF) channels, this paper studies a multilevel-priority incremental-redundancy hybrid ARQ (MP-IR-HARQ) system with nested root-protograph-based low-density parity-check (NRP-LDPC) codes. Specifically, we analyze the performance of traditional RP-LDPC codes and reveal that they are unable to achieve full diversity in the IR-HARQ system over non-ergodic BF channels. To address this limitation, we propose a design methodology to construct a family of NRP-LDPC codes having the full-diversity property, throughput-limit-approaching property, outage-limit-approaching performance, and rate compatibility tailored for the IR-HARQ system. Additionally, we incorporate a multilevel-priority design composed of the adaptive modulation, adaptive detection, and successive decoding into the traditional IR-HARQ framework. The designed MP-IR-HARQ system can support the transmission of multiple data streams while providing unequal error protection (UEP) across them. The outage-boundary analyses, throughput analyses, and word-error-rate (WER) simulations demonstrate that the proposed NRP-LDPC-coded MP-IR-HARQ system can not only significantly outperform prior-art counterparts over non-ergodic BF channels, but also enable the multi-data-stream-based UEP transmission.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication systems. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through phase or amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block orthogonal matching pursuit (CBOMP) algorithm is proposed for decoding by leveraging block-structured sparse prior information. The upper bound of block error rate (BLER) performance over Rayleigh channels is derived to verify the decoding performance of the proposed CBOMP algorithm. Extensive simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of BLER, transmission latency and spectral efficiency over fading channels.

This paper presents generalized Arlery-Tan-Rabaste-Levenshtein lower bounds on the maximum aperiodic ambiguity function (AF) magnitude of unimodular sequences under certain delay-Doppler low ambiguity zones (LAZ). Our core idea is to explore the upper and lower bounds on the Frobenius norm of the weighted auto- and cross-AF matrices by introducing two weight vectors associated with the delay and Doppler shifts, respectively. As a second major contribution, we demonstrate that our derived lower bounds are asymptotically achievable with selected Chu sequence sets by analyzing their maximum auto- and cross-AF magnitudes within certain LAZ.

Integrated sensing and communication (ISAC) has emerged as a promising technology to facilitate high-rate communications and super-resolution sensing, particularly operating in the millimeter wave (mmWave) band. However, the vulnerability of mmWave signals to blockages severely impairs ISAC capabilities and coverage. To tackle this, an efficient and low-cost solution is to deploy distributed reconfigurable intelligent surfaces (RISs) to construct virtual links between the base stations (BSs) and users in a controllable fashion. In this paper, we model the generalized RIS-assisted mmWave ISAC networks considering the blockage effect, and examine the beneficial impact of RISs on the coverage rate utilizing stochastic geometry. Based on the proposed beam patterns and user association policies, we derive the conditional coverage probability and ergodic rate of communication and sensing dual functions for two association cases, as well as the marginal coverage rate using the distance-dependent thinning method. Taking into account the coupling effect of ISAC dual functions within the same network topology, we further calculate the joint coverage probability of ISAC performance. Simulation results verify the accuracy of derived theoretical formulations, and illustrate the impact of the RIS aperture, blockage, BS and RIS densities on ISAC coverage rates, which provide valuable guidelines for the practical network deployment. Specifically, our results indicate the superiority of the RIS deployment with the density of 40 km${}^{-2}$BSs, and that the joint coverage rate of ISAC performance exhibits potential growth from 62% to 97% with the deployment of RISs.

This paper investigates beamforming design with partial channel state information (CSI) feedback aimed at maximizing the energy efficiency (EE) of a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-user system. By leveraging the spatial reciprocity, which means that the path angle information (PAI) and power angular spectrum (PAS) of sparse paths are similar in the uplink and downlink channels, we can acquire the downlink PAI and PAS at the base station (BS) via uplink estimation. Subsequently, we select several paths that contribute to EE maximization from all the cascaded paths and define them as dominant paths. Consequently, only the small-scale fading coefficients (i.e., the normalized path gain information, NPGI) of these selected dominant paths need to be fed back from the user equipments (UEs), thereby significantly reducing the feedback overhead. Moreover, we update the active BS beamformer and passive RIS beamformer by using the feedback of partial NPGI to further improve the EE. Numerical results demonstrate the superiority of our proposed algorithms over conventional schemes.

As mobile networks progress towards a unified framework for integrated sensing and communication (ISAC), it is foreseeable to introduce new privacy concerns, particularly the potential exposure of position information to unintended receivers. In other words, the scope of physical-layer security (PLS) needs to be expanded to encompass both communication and sensing privacy. Therefore, in contrast to conventional PLS schemes that focus predominantly on preventing eavesdropping, this paper proposes a novel physical-layer privacy (PLP) design within ISAC frameworks, in order to guarantee the secrecy of data transmission while obscuring transmitter’s directional information. Specifically, we introduce a metric termed angular-domain peak-to-average ratio (ADPAR) to assess sensing resistance (SR) performance. Subsequently, three fundamental optimization problems are formulated under such ADPAR constraints to enhance communication secrecy, depending upon the integrity of illegitimate channel state information. These problems are then tackled using advanced strategies such as null-space projection and the cooperation with artificial noise. Additionally, closed-form solutions are further derived in a few specific cases by leveraging singular value decomposition (SVD) and generalized SVD. Finally, simulation results affirm the effectiveness of our design in safeguarding the twofold privacy within ISAC networks.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication networks. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the transmitted information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through quadrature amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block matching pursuit algorithm is proposed for accurate decoding by leveraging block-structured sparse prior information. Simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of packet error rate and spectral efficiency.

Staked intelligent metasurface (SIM) based techniques are developed to perform two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to conventional designs, an advanced SIM in front of a receiver array automatically performs the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. To arrange for the SIM to carry out this task, we design a gradient descent algorithm for iteratively updating the phase shift of each meta-atom in the SIM to minimize the fitting error between the SIM's response and the 2D DFT matrix. To further improve the DOA estimation accuracy, we configure the phase shifts in the input layer of the SIM to generate a set of 2D DFT matrices having orthogonal spatial frequency bins. Extensive numerical simulations verify the capability of a well-trained SIM to perform the 2D DFT. Specifically, it is demonstrated that a SIM having an optical computational speed achieves an MSE of 10–4in 2D DOA estimation.

Extensive research has been conducted to explore vehicle lane changes, while the study on cut-ins has not received sufficient attention. The existing studies have not addressed the fundamental question of why studying cut-ins is crucial, despite the extensive investigation into lane changes. To tackle this issue, it is important to demonstrate how cut-ins, as a special type of lane change, differ from other lane changes. In this paper, we explore to compare driving characteristics of cut-ins and other lane changes based on naturalistic driving data. The highD dataset is employed to conduct the comparison. We extract all lane-change events from the dataset and exclude events that are not suitable for our comparison. Lane-change events are then categorized into the cut-in events and other lane-change events based on various gap-based rules. Several performance metrics are designed to measure the driving characteristics of the two types of events. We prove the significant differences between the cut-in behavior and other lane-change behavior by using the Wilcoxon rank-sum test. The results suggest the necessity of conducting specialized studies on cut-ins, offering valuable insights for future research in this field.

The imperative to secure stable data transmission for mobile users within the heart of urban zones stands as a pivotal element in the evolution of future communication infrastructures. Utilizing the adaptable beamforming prowess of Reconfigurable Intelligent Surfaces (RISs) presents a viable strategy to aid mobile users in sustaining consistent beam tracking amidst the complex urban milieu. This study delves into the dynamic beamforming potential of RIS to facilitate real-time tracking and communication across convoluted wireless landscapes. In pursuit of refined spot beamforming capabilities under near-field scenarios, this work unveils an innovative 2bit RIS unit design approach, engineered to establish stable and adjustable phase shifts whilst minimizing amplitude loss. A tangible RIS experimental framework is devised, drawing from numerical analyses and comprehensive wave simulations. This framework enables each RIS unit to autonomously toggle between states, thereby not only governing cone beams but also materializing sophisticated structured beams, such as 3D spot beams, thus augmenting the versatility of communication designs. To augment the RIS’s beam tracking efficiency, an effective RIS management protocol alongside a spot beam scanning algorithm tailored for mobile users is introduced. Subsequent extensive simulations and empirical investigations, corroborate the prompt and adaptable beamforming proficiency of the proposed RIS prototype within demanding wireless contexts. The outcomes of this research significantly enrich the comprehension and application of RIS technology in the realm of forthcoming communication frameworks.

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functionalities coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage rate of sensing and communication performance under resource constraints. Further, we present theoretical results for the coverage rate of unified ISAC performance, taking into account the coupling effects of dual functions in coexistence networks. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from 1 km-2to 10 km-2can boost the ISAC coverage rate from 1.4% to 39.8%.

Intelligent Transportation Systems (ITS) are faced with two Vehicle-to-Everything (V2X) communication technologies, Dedicated Short Range Communication (DSRC) and Cellular Vehicle-to-Everything (C-V2X). These technologies use different physical layer as well as MAC layer, hence are not compatible. They use the same 5.9GHz frequency band, but they may be allocated different channels. Such co-existence of the two system raises questions of possible adjacent channel interference from one technology to another when they are both adopted in the same country. In order to investigate this issue, we carry out an evaluation of the performance of DSRC under the effect of adjacent channel interference from C-V2X signal. Commercial off-the-shelf V2X devices are used. Line-of-sight (LOS) and Non-LOS (NLOS) measurements are made on the road. The on-road field measurement results show that, when C-V2X interference with high Channel Busy Ratio (CBR) of around 55% is present, the Packet Delivery Ratio (PDR) of DSRC system may drop from 100% to 90% in line-of-sight (LOS) scenarios, and the communication range may be reduced by up to 60m in the NLOS scenarios.

This paper introduces an innovative approach to realizing adaptable secondary beamforming using a dual holo-graphic reflective meta-surface, which comprises a transmitting reflective meta-surface A and a transforming meta-surface B, both designed in accordance with holographic theory. Meta-surface A is specifically engineered to generate multi-mode vortex beams, serving as the initial stage of beamforming. Meanwhile, holographic meta-surface B is employed to convert the multi-mode vortex beams into two spherical waves with distinc-t directions. The holographic meta-surface system undergoes simulation, fabrication, and measurement at 10 GHz. Results demonstrate the efficacy of the proposed method in achieving robust and flexible beamforming. This system, characterized by its low profile, simplicity, and cost-effectiveness, holds significant promise for the transmission of multi-mode vortex beams in wireless communication scenarios.

Abandoned object Detection (Aod) is of critical importance in the field of public safety. However, the demand on detection accuracy and latency hinders the development of ubiquitous Aod in safety protection, especially for some surveillance devices with relatively low-computational capacity. To this end, a novel high-precision and low-latency Aod method under the hybrid cloud-fog computing architecture is proposed in this article. To be specific, a YOLO-various hidden (YOLO-VH) Aod network model, which is integrated with an efficient dynamic convolution-based ghost module and a Haar wavelet-based downsampling convolution module, is presented to improve the detection accuracy of Aod. In addition, a flexible task offloading strategy is proposed to offload some of the Aod tasks based on the expectation cursor, which is designed to determine the local optimal offloading amount at different times. Finally, extensive experiments are conducted to verify the performance of our proposal through simulations. Our proposal exhibits a reduction of approximately 5.31 million parameters and 30.4 GFLOPs in computation compared with YOLOv9, while demonstrating performance improvements of 25.0% and 38.8% relative to cloud and fog computing, respectively. Furthermore, the total latency for image acquisition, task offloading, and task processing has been observed to be approximately 60% and 15% lower than cloud and fog computing, respectively.

Wireless Sensor Networks (WSNs), which provide perception services for the Internet of Things (IoT) infrastructure, usually suffer from constrained energy resources. However, the fact that the data collected by WSNs often exhibit spatial-temporal correlation leads to the waste of energy. In addition, load imbalance among sensor nodes also makes energy efficiency low. To this end, a novel Energy-efficient approach based on Clustering using Grey Prediction (ECGP) is proposed in this paper. To be specific, a novel Dual-end Data Prediction Mechanism (DDPM) is presented based on the grey prediction model to cut down data redundancy. Furthermore, the prediction process of the grey model, namely a dynamic and fixed size prediction queue scheme, is optimized to enhance the prediction accuracy. A novel Energy-Distance Factor (EDF) and a novel Dual-Threshold-based Critical Condition (DTCC) are proposed with the aim of realizing load balance and alleviating the challenge resulted from random events occurrence. Finally, extensive experimental simulations have been carried out to demonstrate the energy efficiency of ECGP. It is compared with the classic and several latest clustering algorithms, namely, LEACH, R-LEACH, EAHA and ECPSO. The experimental results indicate that ECGP outperforms the others in terms of the network lifetime, the throughput, and the energy efficiency.

Task offloading (TO) is beneficial to reducing the delay and energy consumption for the prosperity of the applications in next generation (NG) wireless networks. However, existing TO approaches are inability to exhibit low complexity and stable performance. To this end, a novel federated hierarchical deep deterministic policy gradient (FHDDPG) algorithm for TO and resource allocation (RA) is proposed in this article. To be specific, three deep deterministic policy gradient (DDPG) modules are deployed in parallel to make offloading decision on the execution mode of tasks and the proportion allocation of the transmission rate. Subsequently, a federated learning method is proposed to collaboratively train the HDDPG model by means of sharing models’ weights. Meanwhile, the delay and the energy consumption are comprehensively considered as the average system consumption, which is defined as a reward metric of FHDDPG. Finally, extensive simulations are conducted to demonstrate the effectiveness of our proposal. The experimental results indicate that the average system consumption of FHDDPG is cut down by 11.4% and 18% compare with HDDPG and DDPG, respectively, which means FHDDPG can achieve a better performance effectively.

Meta-surfaces, also known as Reconfigurable Intelligent Surfaces (RIS), have emerged as a cost-effective, low power consumption, and flexible solution for enabling multiple applications in Internet of Things (IoT). However, in the context of meta-surface-assisted multi-pair IoT communications, significant interference issues often arise amount multiple channels. This issue is particularly pronounced in scenarios characterized by Line-of-Sight (LoS) conditions, where the channels exhibit low rank due to the significant correlation in propagation paths. These challenges pose a considerable threat to the quality of communication when multiplexing data streams. In this paper, we introduce a meta-surface-aided communication scheme for multi-pair interactions in IoT environments. Inspired by holographic technology, a novel compensation method on the whole meta-surface has been proposed, which allows for independent multi-pair direct data streams transmission with low interference. To further reduce correlation under LoS channel conditions, we propose a vortex beam-based solution that leverages the low correlation property between distinct topological modes. We use different vortex beams to carry distinct data streams, thereby enabling distinct receivers to capture their intended signal with low interference, aided by holographic meta-surfaces. Moreover, a prototype has been performed successfully to demonstrate two-pair multi-node communication scenario operating at 10 GHz with QPSK/16-QAM modulation. The experiment results demonstrate that, even under LoS conditions, the isolation between the two-pair channels exceeds 21 dB. This allows receiving users to undertake simultaneous, same-frequency multiplexed data transmission under extremely low interference conditions, with a real-time demodulation Bit Error Rate (BER) remaining below 3.8×10-3 at achievable Signal-to-Noise Ratio (SNR) conditions. Through the convergence of holographic meta-surfaces and vortex beams, we present a fresh perspective on achieving efficient, low-interference multi-pair IoT communications.

This article introduces a novel Line-of-Sight (LoS) multiple-input-multiple-output (MIMO) communication architecture leveraging nontraditional orbital angular momentum (OAM) beams. Challenging the conventional paradigm of hollow-emitting OAM beams, this study presents an innovative OAM generator that produces directional OAM beams without central energy voids, aligning their radiation patterns with those of conventional planar wave horn antennas. Within the main lobe of radiation patterns, the phase variation characteristics inherent to OAM beams are ingeniously maintained, linking different OAM modes to the linear wavefront variation gradients, thereby reducing channel correlation in LoS scenarios and significantly augmenting the channel capacity of LoS-MIMO frameworks. Empirical validations conducted through a meticulously designed LoS-MIMO experimental platform reveal significant improvements in channel correlation coefficients, communication stability, and bit error rate (BER) compared to systems utilizing traditional planar wave antennas. The experiment results underscore the potential of the novel OAM-based system to improve current LoS-MIMO communication protocols, and offer both academic and engineering guidance for the construction of practical communication infrastructures. Beyond its immediate contributions, this article underscores a pivotal shift in the field of communications, pointing out that traditional communication algorithms have primarily focused on baseband signal processing while often overlooking the electromagnetic (EM) characteristics of the physical world. This research highlights that, in addition to radiation patterns, the wavefront phase variations of traditional antennas represent a new degree of freedom that can be exploited. Consequently, future communication algorithms designed around reconfigurable EM wavefront properties hold the promise of ushering wireless communications into a new era.

Stacked intelligent metasurfaces (SIMs) are capable of emulating reconfigurable physical neural networks by utilizing electromagnetic (EM) waves as carriers. They can also perform various complex computational and signal processing tasks. An SIM is constructed by densely integrating multiple metasurface layers, each consisting of a large number of small meta-atoms that can control the EM waves passing through it. In this paper, we harness an SIM for two-dimensional (2D) direction-of-arrival (DOA) estimation. In contrast to conventional designs, an advanced SIM in front of a receiver array can be designed to automatically compute the 2D discrete Fourier transform (DFT) as the incident waves propagate through it. As a result, a receiver array can directly observe the angular spectrum of the incoming signal, and it can estimate the DOA by simply using probes to detect the energy distribution on the receiver array. This avoids the need for power inefficient radio frequency chains. To enable an SIM to perform the 2D DFT in the wave domain, we formulate an optimization problem that minimizes the mean square error (MSE) between the SIM’s EM response and the 2D DFT matrix. Then, a gradient descent algorithm is customized for iteratively updating the phase shift applied by each meta-atom of the SIM. To further improve the DOA estimation accuracy, we configure the phase shifts of the input layer of the SIM to generate a set of 2D DFT matrices associated with orthogonal spatial frequency bins. Additionally, we analytically evaluate the performance of the proposed SIM-based DOA estimator by deriving a tight upper bound for the MSE. Extensive numerical simulations verify the capability of an optimized SIM to perform DOA estimation and corroborate the theoretical analysis. Specifically, we show that an SIM is capable of performing DOA estimation with an MSE of the order of$10^{-4}$.

Integrated sensing and communication (ISAC) is increasingly recognized as a pivotal technology for next-generation cellular networks, offering mutual benefits in both sensing and communication capabilities. This advancement necessitates a re-examination of the fundamental limits within networks where these two functions coexist via shared spectrum and infrastructures. However, traditional stochastic geometry-based performance analyses are confined to either communication or sensing networks separately. This paper bridges this gap by introducing a generalized stochastic geometry framework in ISAC networks. Based on this framework, we define and calculate the coverage and ergodic rate of sensing and communication performance under resource constraints. Then, we shed light on the fundamental limits of ISAC networks by presenting theoretical results for the coverage rate of the unified performance, taking into account the coupling effects of dual functions in coexistence networks. Further, we obtain the analytical formulations for evaluating the ergodic sensing rate constrained by the maximum communication rate, and the ergodic communication rate constrained by the maximum sensing rate. Extensive numerical results validate the accuracy of all theoretical derivations, and also indicate that denser networks significantly enhance ISAC coverage. Specifically, increasing the base station density from$1~\text {km}^{-2}$to$10~\text {km}^{-2}$can boost the ISAC coverage rate from 1.4% to 39.8%. Further, results also reveal that with the increase of the constrained sensing rate, the ergodic communication rate improves significantly, but the reverse is not obvious.

In this paper, a Turbo basis expansion modeling (BEM) orthogonal time frequency space (OTFS) scheme is proposed for high-mobility communications subject to either Doppler-shift or Doppler-spread channel. It consists of superimposed pilot power and BEM order optimization and Turbo BEM OTFS receiver. The signal-to-interference-and-noise ratio (SINR) is derived as functions of OTFS system parameters and several channel information (i.e., channel correlation matrix, noise variance, etc.). The optimal superimposed pilot power ratio is then derived with a closed-form solution by calculating the first derivative of derived SINR. BEM order is specially optimized for each channel, instead of simply increasing or reducing it. The Turbo BEM OTFS receiver is proposed with an exchange of soft information between BEM channel estimation, signal detection, and data decoding, leading to high reliability. By using superimposed pilots for initial BEM channel estimation, the proposed Turbo BEM OTFS scheme has zero dedicated pilot overhead, resulting in high spectral efficiency. Simulation results confirm that the proposed OTFS scheme outperforms the existing OTFS schemes in terms of bit error rate (BER) and spectral efficiency. Extrinsic information exchange transfer (EXIT) chart analysis and simulation results also exhibit the fast convergence speed of proposed OTFS scheme.

This paper is centered on multiple-input multiple-output visible light communication (MIMO-VLC) systems with protograph-based low-density parity-check (P-LDPC) codes. We propose a two-step design method, which is composed of spatial-domain (SpD)-based Gray-like (GL) principle and signal-domain (SiD)-based energy-optimization (EO) principle, to construct a novel class of generalized spatial modulation (GSM) schemes, referred to asGLEOGSM schemes, to enhance the system performance. We also introduce a novel internal-stopping (IS) principle into the conventional joint detection and decoding (JDD) architecture, the resultant IS-aided JDD (IS-JDD) algorithm significantly reduces the computational overhead. Inspired by the proposed IS-JDD architecture, we further conceive a low-complexity IS-aided extrinsic-information-transfer (IS-EXIT) algorithm to analyze the convergence performance of P-LDPC-coded MIMO-VLC systems. Both theoretical and simulation results verify that our proposed designs can remarkably outperform the existing benchmark schemes in terms of capacity, convergence and complexity.

Orbital angular momentum (OAM) is a mode division multiplexing (MDM) technique enabling simpler implementation and higher capacity than conventional multiple-input multiple-output (MIMO) over line-of-sight (LoS) channels. This paper studies the joint design of protograph low-density parity-check (PLDPC) codes and shaping index modulation (IM) in OAM systems. To begin with, we analyze the distribution of extrinsic log-likelihood-ratios (LLRs) for coded bits output from channel detector, and then propose a customized Box-Cox transformation (CBCT) to make the distribution achieve symmetry and Gaussian features. We also devise a CBCT-based protograph extrinsic information transfer (CBCT-PEXIT) algorithm to predict the convergence performance of PLDPC-coded OAM systems. Furthermore, with the aid of such an algorithm, we construct two new types of improved PLDPC codes tailored for OAM systems, including unpunctured codes and rate-compatible punctured codes. In addition, we present a two-step design method, which seamlessly combines the shaping technique and index rule into a novel modulation scheme, calledshaping index. Both theoretical analyses and simulation results demonstrate that the proposed PLDPC-coded OAM systems with shaping index significantly outperform state-of-the-art counterparts.

Anytime code is a new class of error-correcting code that has very long coding memory and is decodable with flexible decoding window size (decoding delay). It is designed for real-time reliable applications, such as tracking and controlling unstable systems. In this paper, we construct a family of rate-compatible (RC) Anytime code ensembles with code rate from 0 to 1 based on spatially coupled repeat-accumulate (SC-RA) codes and apply them to the automatic-repeat-request (ARQ) communication with incremental redundancy. Using density evolution (DE) analysis, we prove that each element code within the RC family has superior Anytime-reliable properties over the binary erasure channel (BEC) and the binary-input additive white Gaussian noise (BI-AWGN) channel. In addition, we develop an expanding-window IR-HARQ (incremental redundancy hybrid automatic-repeat-request) scheme for the proposed RC Anytime codes. Simulation results testify that our proposed RC Anytime codes have better decoding performance than the prior-art RC Anytime codes. Furthermore, when combined with the proposed expanding-window IR-HARQ scheme, our proposed RC Anytime codes show significant advantages over the conventional RC low-density parity-check (LDPC) codes and RC polar codes in the conventional CRC-based IR-HARQ communication systems.

Combination with the Simultaneous Wireless Information and Power Transfer (SWIPT) technology is expected as a promising solution to the issue of energy constraint in Wireless Sensor Networks (WSN). However, little attention is paid to the energy sustainability in SWIPT-enabled WSN with limited Mobile Energy Access Point (MEAP) configurations. To this end, a novel Energy-sustainable approach for SWIPT-enabled WSN with Constraint MEAP (ESWCM) configurations is proposed in this paper. To be specific, an Optimal Ring Width Determination (ORWD) algorithm is proposed, with the aim to ensure all the sensor nodes lie in the energy coverage of limited MEAPs from the perspective of the entire network. Subsequently, a GA-based Clustering algorithm constrained by cluster Head (GCH) connectivity is presented to promote the survivability of Cluster Heads (CHs) and the effectiveness of data transmission through reasonable cluster size. Additionally, the optimal parameters of each sensor node are determined through an Energy-efficient Parameter Optimization (EPO) algorithm to achieve energy sustainability of each individual sensor node. Experimental results indicate that ESWCM exhibits higher node survival rate by 124.7%, 2%, 29.9%, and 40.5% compared with LEACH, S-LEACH, R-LEACH, and DEEC after 1200 iterations respectively. Moreover, it also shows a survival rate of 100% after adjustments.

The emergence of the Simultaneous Wireless Information and Power Transfer (SWIPT) technology makes it possible to achieve energy sustainability in the Wireless Sensor Networks (WSNs). However, little attention was paid to the large-scale SWIPT-enabled WSNs. To this end, we synthesize the network Energy Efficiency (EE), energy sustainability conditions, and network throughput in the large-scale SWIPT-enabled WSNs, and propose a Topology-Scale-Adaptive and Energy Efficient Clustering Scheme (TSA-EECS). To be specific, the EE maximization problem is formulated as a fractional programming problem, which jointly optimizes the transmission power and the power splitting ratio of sensors. Subsequently, a Dinkelbach-based iterative algorithm is proposed to transform the problem and a Lagrangian function is presented to obtain a near-optimal solution through the gradient descent method. In addition, a BFOA-based Cluster Head (CH) selection algorithm is proposed to adapt to the large-scale network and reduce energy consumption for CH selection. Finally, extensive simulations are conducted to evaluate the effectiveness of TSA-EECS. Experimental results demonstrate that the proposed iterative algorithm converges after 15 iterations on average. In addition, compared with recent energy harvesting schemes, the network throughput of TSA-EECS is improved by 3–8%.

Energy-efficiency (EE) optimization of long-term evolution (LTE) networks dedicated to vehicle-to-vehicle communications (LTE-V2V) is critical for connected vehicles. In this paper, we integrate perimeter control methodologies from transportation science into EE optimization to make vehicular communications adaptive to temporal-spatial dynamics of macroscopic traffic flows in multiple urban regions. Specifically, we develop a hierarchical framework of joint LTE-V2V EE optimization and perimeter traffic flow control. Its goal is to minimize the total traffic network delay, defined as the integral of the vehicle accumulations in the urban regions over a prediction horizon time, meanwhile maximizing the energy efficiency of the LTE-V2V communications in the same regions. We propose a model predictive perimeter controller at a low level, using a macroscopic fundamental diagram (MFD) to capture the relationship between the traffic density and the outflow of each urban region. We also propose a high-level EE optimization model and an iterative algorithm, considering the multi-region coordinated traffic dynamics, to jointly optimize vehicular transmission power and beacon frequency. Simulation results validate our proposed models and show that our method outperforms the latest solutions by improving at least 9.57% EE of the multiple regions. Our method can also provide 27.69% improvement in resource utilization fairness, indicating a fairer EE performance distribution among these regions.

In multiple-input multiple-output (MIMO) systems, channel estimation is of crucial importance to guarantee reliable recovery of ultra-high-speed MIMO signals. This paper proposes a novel channel estimation algorithm for the emerging MIMO-based orthogonal chirp-division multiplexing (OCDM) systems by utilizing the unique features of OCDM signals. In the proposed algorithm, a set of pilot signals is designed based on the Fresnel basis, which is essentially a family of orthogonal linear chirps. The pilots are assigned to different antennas for transmission occupying the same time slot and bandwidth. According to the convolution-preservation theorem of the Fresnel transforms, the transfer matrices of MIMO-OCDM systems can be readily estimated at the receiver without any inter-antenna interference, even if the pilots overlap in both the time and frequency domains. The proposed algorithm avoids bandwidth waste in conventional channel estimators, in which silent pilots will be required in time and/or frequency to ensure the received MIMO pilots separable. We show that the proposed algorithm is unbiased for the unique OCDM pilots and has better estimate accuracy and system performance. Finally, analysis and numerical results are provided to validate its advantages as a promising algorithm for emerging wireless access technology based on MIMO-OCDM.

A signal scrambling based joint blind channel estimation, activity detection, and data decoding (SS-JCAD) scheme is proposed for coded massive random access. This signal scrambling technique imposes symbol-wise phase rotation to each user’s modulated data, and the scrambling pattern serves as a user-specific signature which is free from any bandwidth expansion or pilot signaling overhead. Building on this scrambling signature, we further propose a simple yet efficient receiver design, which integrates the blind channel state information (CSI) estimation module with the forward error correction (FEC) decoder. Specifically, according to the scrambling signature, a user-specific posterior probability density function of the CSI is derived, based on which both the CSI and activity of each user can be blindly detected using a low-complexity single-user maximum a posteriori estimation. Given the estimated CSI asa prioriinformation, a joint CSI (including user activity) estimation and data decoding algorithm is proposed, where the soft information is iteratively updated between the FEC decoder and the CSI estimation module to refine the detection reliability. Simulation shows that for massive random access systems with moderate code length and system load factor less than 1.5, the SS-JCAD scheme achieves almost the same bit error rate as the ideal case aided with perfect CSI, implying the SS-JCAD scheme as a near-optimal solution to the massive random access scenario.

Continuous phase frequency shift keying (CPFSK) has been widely used in wireless communications, as its continuous phase and constant envelop lead to high bandwidth and power efficiency. In non-cooperative communication systems, the CPFSK parameters such as modulation index, pulse shape and pulse length are unknown to the receiver and need to be estimated. This is a challenging task especially when the transmission is bursty. In this paper, long short term memory (LSTM) neural networks are proposed to tackle this problem. Specifically, three LSTM networks with identical structure but trained separately are used to estimate three important CPFSK parameters (modulation index h, pulse length$L$and pulse shape$q$(t)) in parallel. Simulation results show that by suitably preprocessing the input data before the LSTM network, the proposed blind estimation scheme is able achieve high estimation accuracy of more than 95% in most cases.

Inter-satellite communication (ISC) is an essential part of LEO satellite communication systems, allowing satellites to communicate with each other in the same or different orbital planes. This paper presents a prediction model to estimate the distance and Doppler shift between two LEO satellites in different orbital planes. Using this prediction model, a channel model for rapidly changing inter-orbital ISC channels is established. Moreover, a novel dynamic symbol-level power and modulation pre-adaptation (SL-PMPA) algorithm is proposed to achieve power-efficient high throughput transmission under the rapidly changing ISC channel. Simulation results demonstrate that compared to conventional transmission modulation schemes, the proposed dynamic SL-PMPA algorithm not only significantly enhances the throughput under the same BER threshold, but also effectively reduces the transmission power.

In this paper, a novel dual-rate orthogonal time frequency space (OTFS) system resilient to oscillator frequency offset (OFO) and Doppler spread is presented for high-mobility communications. To the best of the authors' knowledge, this is the first work in OTFS which introduces dual-rate frame and addresses both OFO and Doppler spread. By multiplexing low-rate data blocks at the front and back of OTFS frame, OFO is estimated without requiring extra pilots. Thanks to subspace-based algorithm, OFO is estimated with high accuracy, and the residual small OFO can be jointly estimated with Doppler-spread channel by utilizing a few pilots, without deteriorating performance. Simulation results verify the superior performance of the proposed dual-rate system over existing single-rate systems, in terms of bit error rate (BER), mean-square-error (MSE) of OFO estimation, and MSE of equivalent channel estimation, while featuring lower pilot overhead and power. The BER of the proposed system also approaches its lower bound, which assumes perfect estimation of OFO and Doppler-spread channel.

Orthogonal time frequency space (OTFS) modulation has become an effective technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) multi-input multi-output (MIMO) OTFS systems. Specifically, first, an iterative data-aided channel estimation scheme is proposed to accurately acquire UL channels with the aid of specifically designed frame pattern. Then the discrete prolate spheroidal basis expansion model (DPS-BEM) is used to model the time-varying UL channel estimates, and the dynamic DPS-BEM coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels for all antenna pairs and paths by iteratively extrapolating the fitting coefficients. Simulation results verify that the proposed scheme outperforms the existing schemes in terms of normalized mean square error of channel prediction and DL sum-rate.

The Intelligent Reflection Surface (IRS) is a crucial technology for the development of next-generation wireless mobile networks. However, its deployment is usually limited to Line-of-Sight (LoS) transmission paths of Base Stations (BSs) due to path-loss and beamforming restrictions. LoS wireless channels are known to have low-rank characteristics, which significantly reduce the capacity of IRS-assisted communication links. To overcome this issue, we propose a new IRS transmission scheme using vortex beams. These beams have low correlation properties between different modes, thus improving the channel capacity for served users. In this paper, we detail the channel capacity comparison of IRS systems using vortex beams and conventional plane waves. Additionally, a 2-bit phase quantized IRS prototype is designed and tested through full-wave Electro-Magnetic (EM) simulations and actual transmission experiments, allowing for the conversion of mode 1 vortex beams and plane waves at a pre-determined reflection angle (e.g., 30°). Our simulation results and prototype tests show that this design is effective in manipulating reflected vortex beams and plane waves in 3D space, meeting the demands of future intelligent wireless communications.

This paper addresses the blind recovery of the parity check matrix of an (n, k) linear block code over noisy channels by proposing a fast recovery scheme consisting of 3 parts. Firstly, this scheme performs initial error position detection among the received codewords and selects the desirable codewords. Then, this scheme conducts Gaussian elimination (GE) on a k-by-k full-rank matrix and uses a threshold and the reliability associated to verify the recovered dual words, aiming to improve the reliability of recovery. Finally, it performs decoding on the received codewords with partially recovered dual words. These three parts can be combined into different schemes for different noise level scenarios. The GEV that combines Gaussian elimination and verification has a significantly lower recovery failure probability and a much lower computational complexity than an existing Canteaut-Chabaud-based algorithm, which relies on GE on n-by-n full-rank matrices. The decoding-aided recovery (DAR) and error-detection-&-codeword-selection-&-decoding-aided recovery (EDCSDAR) schemes can improve the code recovery performance over GEV for high noise level scenarios, and their computational complexities remain much lower than the Canteaut-Chabaud-based algorithm.

Vehicle-to-Vehicle (V2V) communication requires highly reliable wireless links to deliver safety messages effectively. Signal interference or blockages caused by big vehicles like buses and trucks can deteriorate communication quality, causing loss of packets containing safety information transmitted among vehicles. Hence big vehicle shadowing critically influences V2V communication quality. This paper investigates the impact of big vehicle shadowing on V2V communications, considering cellular network coverage of vehicles. Under big vehicle shadowing, we analyse and derive the reuse distance and outage probability of V2V communications. To mitigate the negative of big vehicle shadowing, beamforming-based reception and transmission along with relay approaches are shown. Simulation results show that the proposed approaches significantly improve the performance of V2V communications in the presence of big vehicles.

Energy efficiency (EE) problem has become an important and major issue in satellite communications. In this paper, we study the beamforming design strategy to maximize the EE of rate-splitting multiple access (RSMA) for the multibeam satellite communications by considering imperfect channel state information at the transmitter (CSIT). We propose an expectation-based robust beamforming algorithm against the imperfect CSIT scenario. By combining the successive convex approximation (SCA) with the penalty function transformation, the nonconvex EE maximization problem can be solved in an iterative manner. The simulation results demonstrate the effectiveness and superiority of RSMA over traditional space-division multiple access (SDMA). Moreover, our proposed beamforming algorithm can achieve better EE performance than the conventional beamforming algorithm.

To support emerging smart applications, joint communication and sensing (JCAS) integrates sensing into communications to save resources and provide a wider sensing service. Although there have been many works in this area, almost all of them are based on the sometimes impractical assumption that the position of the transmitter is known. In this paper, we propose a low-complexity multi-target-aided method for uplink MIMOOFDM sensing in a cluttered environment with an unknown transmitter’s position. Specially, we form the signal received at the base station (BS) as a fourth-order tensor, and after clutter suppression, we adopt a low-rank CANDECOMP/PARAFAC (CP) decomposition to perform parameter estimations. Through theory analysis, our CP decomposition satisfies the condition of uniqueness. Simulation results show that the proposed method can achieve better normalized mean square error performance than the existing methods and even approach a similar performance with the ideal case benchmark.

Using DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process. In this work, we first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.

In our early work, we proposed a class of joint source channel anytime coding (JSCAC) scheme based on spatially coupled repeat-accumulate (SC-RA) codes, in addition to an improved partial joint expanding window decoding (PJEWD) algorithm. In this paper, we extend our preliminary results and make a comprehensive theoretical analysis for this system. First, we propose an improved density evolution (DE) algorithm for the SC-RA based JSCAC with PJEWD scheme. Based on the proposed DE algorithm, we systematically analyze the anytime property, the belief propagation (BP) decoding threshold, and the decoding complexity of this system over binary additive white Gaussian noise channel (BIAWGN), as well as the effects of code parameters on performances of the above three aspects. Moreover, we explore some application scenarios for JSCAC schemes, where it could maximize its advantages over the prior-art JSCC schemes. Both numerical and simulation results demonstrate the potentials of the proposed JSCAC scheme for high reliability and low latency communications. The proposed DE algorithm could also provide a good reference for the analysis of other JSCAC systems.

Matching landmark patches from a real-time image captured by an on-vehicle camera with landmark patches in an image database plays an important role in various computer perception tasks for autonomous driving. Current methods focus on local matching for regions of interest and do not take into account spatial neighborhood relationships among the image patches, which typically correspond to objects in the environment. In this paper, we construct a spatial graph with the graph vertices corresponding to patches and edges capturing the spatial neighborhood information. We propose a joint feature and metric learning model with graph-based learning. We provide a theoretical basis for the graph-based loss by showing that the information distance between the distributions conditioned on matched and unmatched pairs is maximized under our framework. We evaluate our model using several street-scene datasets and demonstrate that our approach achieves state-of-the-art matching results.

In this paper, a near-optimal Karhunen-Loeve basis expansion modeling (KL-BEM) orthogonal time frequency space (OTFS) receiver with superimposed pilots has been proposed for high-mobility communications over time-varying channels with Doppler and delay spread. First, an initial KL-BEM channel estimation is conducted using superimposed pilots, followed by the removal of superimposed pilots from the received OTFS signal and equalization based on message passing (MP) algorithm. After that, the detected data symbols are utilized as pseudo pilots together with the superimposed pilots to refine both KL-BEM channel estimation and equalization in an iterative manner. Simulation results confirm the superior performance of the proposed KL-BEM OTFS receiver over prior art in terms of bit error rate (BER). The resulting BER performance is close to the lower bound obtained by assuming perfect channel estimation. Besides, the proposed scheme provides high spectral efficiency, while featuring fast convergence and affordable complexity.

With the complication of future communication scenarios, most conventional signal processing technologies of multi-user multiple-input multiple-output (MU-MIMO) become unreliable, which are designed based on ideal assumptions, such as Gaussian signaling and independent identically distributed (IID) channel matrices. As a result, this paper considers a generalized MU-MIMO (GMU-MIMO) system with more general assumptions, i.e., arbitrarily fixed input distributions, and general unitarily-invariant channel matrices. However, there is still no accurate capacity analysis and capacity optimal transceiver with practical complexity for GMU-MIMO under the constraint of coding. To address these issues, inspired by the replica method, the constrained sum capacity of coded GMU-MIMO with fixed input distribution is calculated by using the celebrated mutual information and minimum mean-square error (MMSE) lemma and the MMSE optimality of orthogonal/vector approximate message passing (OAMP/VAMP). Then, a capacity optimal multi-user OAMP/VAMP receiver is proposed, whose achievable rate is proved to be equal to the constrained sum capacity. Moreover, a design principle of multi-user codes is presented for the multi-user OAMP/VAMP, based on which a kind of practical multi-user low-density parity-check (MU-LDPC) code is designed. Numerical results show that finite-length performances of the proposed MU-LDPC codes with multi-user OAMP/VAMP are about 2 dB away from the constrained sum capacity and outperform those of the existing state-of-art methods.

Current strategies for joint radar-communication (JRC) rely on prior knowledge of the communication and radar systems within the vehicle network. In this paper, we propose a framework for intelligent vehicles to conduct JRC, with minimal prior knowledge, in an environment where surrounding vehicles execute radar detection periodically, which is typical in contemporary protocols. We introduce a metric on the usefulness of data to help the vehicle decide what, and to whom, data should be transmitted. The problem framework is cast as a Markov Decision Process (MDP). We show that deep reinforcement learning results in superior performance compared to nonlearning algorithms. In addition, experimental results show that the trained deep reinforcement learning agents are robust to changes in the number of vehicles in the environment.

Conventional multi-user multiple-input multiple-output (MU-MIMO) mainly focused on Gaussian signaling, independent and identically distributed (IID) channels, and a limited number of users. It will be laborious to cope with the heterogeneous requirements in next-generation wireless communications, such as various transmission data, complicated communication scenarios, and unprecedented massive user access. Therefore, this paper studies a generalized MU-MIMO (GMU-MIMO) system with more generalized and practical constraints, i.e., practical channel coding, non-Gaussian signaling, right-unitarily-invariant channels (covering Rayleigh fading channel matrices, certain ill-conditioned and correlated channel matrices, etc.), and massive users and antennas. These generalized assumptions bring new challenges in theory and practice. For example, there is no accurate constrained capacity region analysis for GMU-MIMO. In addition, it is unclear how to achieve constrained-capacity-optimal performance with practical complexity. To address these challenges, a unified framework is proposed to derive the constrained capacity region of GMU-MIMO and design a constrained-capacity-optimal transceiver, which jointly considers encoding, modulation, detection, and decoding. Group asymmetry is developed to group users according to their rates, which makes a tradeoff between user rate allocation and implementation complexity. Specifically, the constrained capacity region of group-asymmetric GMU-MIMO is characterized by using the minimum mean-square error (MMSE) optimality of orthogonal/vector approximate message passing (OAMP/VAMP) and the relationship between mutual information and MMSE. Furthermore, a theoretically optimal multi-user OAMP/VAMP receiver and practical multi-user low-density parity-check (MU-LDPC) codes are proposed to achieve the constrained capacity region of group-asymmetric GMU-MIMO. Numerical results demonstrate that the proposed MU-LDPC coded GMU-MIMO systems achieve asymptotic performance within 0.2 dB from the theoretical sum capacity. Moreover, their finite-length performances are about 1~2 dB away from the associated sum capacity of GMU-MIMO.

In this paper, a new receiver design based on basis expansion model (BEM) orthogonal time frequency space (OTFS) is presented for high-mobility communications with Doppler-spread channel. By deriving an analytical BEM OTFS system model, a low-order generalized complex exponential BEM (GCE-BEM) aided rough channel estimation is proposed at the initial stage with low pilot overhead, followed by equalization. Then, the refinement of channel estimation and equalization is conducted iteratively, in which a high-resolution GCE-BEM model with a large BEM order is adopted and the detected data symbols are exploited as pseudo-pilots, leading to higher estimation accuracy. Simulation results show that the proposed BEM OTFS receiver significantly outperforms the existing OTFS receivers in terms of the mean square error (MSE) of channel estimation and bit error rate (BER), while featuring low pilot overhead. Results also show the near-optimal performance of the novel solution,i.e., achieved BER is very close to the case of perfect channel estimation. The theoretical lower bound on MSE of channel estimation is derived to verify the effectiveness of the proposed BEM OTFS receiver, which is shown to be close to simulation results.

This paper proposes an effective and novel multi-agent deep reinforcement learning (MADRL)-based method for solving the joint virtual network function (VNF) placement and routing (P&R), where multiple service requests with differentiated demands are delivered at the same time. The differentiated demands of the service requests are reflected by their delay- and cost-sensitive factors. We first construct a VNF P&R problem to jointly minimize a weighted sum of service delay and resource consumption cost, which is NP-complete. Then, the joint VNF P&R problem is decoupled into two iterative subtasks: placement subtask and routing subtask. Each subtask consists of multiple concurrent parallel sequential decision processes. By invoking the deep deterministic policy gradient method and multi-agent technique, an MADRL-P&R framework is designed to perform the two subtasks. The newjoint reward and internal rewardsmechanism is proposed to match the goals and constraints of the placement and routing subtasks. We also propose the parameter migration-based model-retraining method to deal with changing network topologies. Corroborated by experiments, the proposed MADRL-P&R framework is superior to its alternatives in terms of service cost and delay, and offers higher flexibility for personalized service demands. The parameter migration-based model-retraining method can efficiently accelerate convergence under moderate network topology changes.

In limited space scenarios, the antennas in a multiantenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrarily correlated antennas over the Nakagami-$m$fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with arbitrarily correlated triple diversity branches is derived as a special case. Moreover, to characterize the performance of an energy detector under mobility, the area under the curve of a receiver operating characteristic is analysed. Furthermore, as the sensing decisions can be utilized to improve the sensing performance, a comprehensive analysis of sensing performance of the mobile secondary user conditioned on the primary user (PU) activity statistics is carried out. We assume the PU traffic to be following the Discrete-Time Markov Chain (DTMC) model, and its$idle$and$busy$periods to be following generalised Pareto distribution. The analytical framework is substantiated by Monte Carlo simulations. Results indicate that antenna correlation deteriorates detection performance. Moreover, the detection performance deteriorates further due to the mobility of CR users, especially in the deep fading channel scenarios. Furthermore, findings also suggest that under mobility, the sensing performance improves if the duty cycle of the PU channel is low. This work provides a realistic sensing framework for CR enabled vehicles.

Increasing concerns on intelligent spectrum sensing call for efficient training and inference technologies. In this paper, we propose a novel federated learning (FL) framework, dubbed federated spectrum learning (FSL), which exploits the benefits of reconfigurable intelligent surfaces (RISs) and overcomes the unfavorable impact of deep fading channels. Distinguishingly, we endow conventional RISs with spectrum learning capabilities by leveraging a fully-trained convolutional neural network (CNN) model at each RIS controller, thereby helping the base station to cooperatively infer the users who request to participate in FL at the beginning of each training iteration. To fully exploit the potential of FL and RISs, we address three technical challenges: RISs phase shifts configuration, user-RIS association, and wireless bandwidth allocation. The resulting joint learning, wireless resource allocation, and user-RIS association design is formulated as an optimization problem whose objective is to maximize the system utility while considering the impact of FL prediction accuracy. In this context, the accuracy of FL prediction interplays with the performance of resource optimization. In particular, if the accuracy of the trained CNN model deteriorates, the performance of resource allocation worsens. The proposed FSL framework is tested by using real radio frequency (RF) traces and numerical results demonstrate its advantages in terms of spectrum prediction accuracy and system utility: a better CNN prediction accuracy and FL system utility can be achieved with a larger number of RISs and reflecting elements.

Wireless relay network is a solution for transmitting information from a source node to a sink node far away by installing a relay in between. The broadcasting nature of wireless communication allows the sink node to receive part of the data sent by the source node. In this way, the relay does not need to receive the whole piece of data from the source node and it does not need to forward everything it received. In this paper, we consider the application of batched network coding, a practical form of random linear network coding, for a better utilization of such a network. The amount of innovative information at the relay which is not yet received by the sink node, called the innovative rank, plays a crucial role in various applications including the design of the transmission scheme and the analysis of the throughput. We present a visualization of the innovative rank which allows us to understand and derive formulae related to the innovative rank with ease.

Radar detection and communication are two of several sub-tasks essential for the operation of next-generation autonomous vehicles (AVs). The former is required for sensing and perception, more frequently so under various unfavorable environmental conditions such as heavy precipitation; the latter is needed to transmit time-critical data. Forthcoming proliferation of faster 5G networks utilizing mmWave is likely to lead to interference with automotive radar sensors, which has led to a body of research on the development of Joint Radar Communication (JRC) systems and solutions. This paper considers the problem of time-sharing for JRC, with the additional simultaneous objective of minimizing the average age of information (AoI) transmitted by a JRC-equipped AV. We formulate the problem as a Markov Decision Process (MDP) where the JRC agent determines in a real-time manner when radar detection is necessary, and how to manage a multiclass data queue where each class represents different urgency levels of data packets. Simulations are run with a range of environmental parameters to mimic variations in real-world operation. The results show that deep reinforcement learning allows the agent to obtain good results with minimal a priori knowledge about the environment.

Blockchain applications in vehicular networks can offer many advantages, including decentralization and improved security. However, most of the consensus algorithms in blockchain are difficult to be implemented in vehicular ad hoc networks (VANETs) without the help of edge computing services. For example, the connectivity in VANET only remains for a short period of time, which is not sufficient for highly time-consuming consensus algorithms, e.g., Proof of Work, running on mobile-edge nodes (vehicles). Other consensus algorithms also have some drawbacks, e.g., Proof of Stake (PoS) is biased toward nodes with a higher amount of stakes and Proof of Elapsed Time (PoET) is not highly secure against malicious nodes. For these reasons, we propose a voting blockchain based on the Proof-of-Quality-Factor (PoQF) consensus algorithm, where the threshold number of votes is controlled by edge computing servers. Specifically, PoQF includes voting for message validation and a competitive relay selection process based on the probabilistic prediction of channel quality between the transmitter and receiver. The performance bounds of failure and latency in message validation are obtained. This article also analyzes the throughput of block generation, as well as the asymptotic latency, security, and communication complexity of PoQF. An incentive distribution mechanism to reward honest nodes and punish malicious nodes is further presented and its effectiveness against the collusion of nodes is proved using the game theory. Simulation results show that PoQF reduces failure in validation by 11% and 15% as compared to PoS and PoET, respectively, and is 68 ms faster than PoET.

Internet of Vehicles (IoV) is an emerging concept that is believed to help realize the vision of intelligent transportation systems (ITSs). IoV has become an important research area of impactful applications in recent years due to the rapid advancements in vehicular technologies, high throughput satellite communication, the Internet of Things, and cyber-physical systems. IoV enables the integration of smart vehicles with the Internet and system components attributing to their environments, such as public infrastructures, sensors, computing nodes, pedestrians, and other vehicles. By allowing the development of a common information exchange platform between vehicles and heterogeneous vehicular networks, this integration aims to create a better environment and public space for the people as well as to enhance safety for all road users. Being a participatory data exchange and storage, the underlying information exchange platform of IoV needs to be secure, transparent, and immutable in order to achieve the intended objectives of ITS. In this connection, the adoption of blockchain as a system platform for supporting the information exchange needs of IoV has been explored. Due to their decentralized and immutable nature, IoV applications enabled by blockchain are believed to have a number of desirable properties, such as decentralization, security, transparency, immutability, and automation. In this article, we present a contemporary survey on the latest advancement in blockchain for IoV. Particularly, we highlight the different application scenarios of IoV after carefully reviewing the recent literature. We also investigate several key challenges where blockchain is applied in IoV. Furthermore, we present the future opportunities and explore further research directions of IoV as a key enabler of ITS.

In this paper, the problem of blind equalization of high-order quadrature amplitude modulation (QAM) signals is tackled by using a batch equalizer based on support vector regression (SVR). Anew set of error functions weighted by neighborhood symbol decisions and augmented by generalized power factors p and q, are proposed to be used as the penalty terms in SVR, and the optimal values of p and q are determined. In addition, we propose a method to remove the high online computational complexity incurred by the inclusion of neighborhood terms in the new error function. Simulation results show that with about the same complexity, the optimized SVR-NA-SBD-(p, q) attain much lower residual inter-symbol-interference and higher probability of convergence than the best known SVR-MMA, and it needs only about 1400 symbols to achieve a BER of 10-4 for 256QAM in a multipath channel. In contrast, the conventional SVR-MMA needs more than 4000 symbols to achieve such BER.

Algebraic codes such as BCH code are receiving renewed interest as their short block lengths and low/no error floors make them attractive for ultra-reliable low-latency communications (URLLC) in 5G wireless networks. This article aims at enhancing the traditional adaptive belief propagation (ABP) decoding, which is a soft-in-soft-out (SISO) decoding for high-density parity-check (HDPC) algebraic codes, such as Reed-Solomon (RS) codes, Bose-Chaudhuri-Hocquenghem (BCH) codes, and product codes. The key idea of traditional ABP is to sparsify certain columns of the parity-check matrix corresponding to the least reliable bits with small log-likelihood-ratio (LLR) values. This sparsification strategy may not be optimal when some bits have large LLR magnitudes but wrong signs. Motivated by this observation, we propose a Perturbed ABP (P-ABP) to incorporate a small number of unstable bits with large LLRs into the sparsification operation of the parity-check matrix. In addition, we propose to apply partial layered scheduling or hybrid dynamic scheduling to further enhance the performance of P-ABP. Simulation results show that our proposed decoding algorithms lead to improved error correction performances and faster convergence rates than the prior-art ABP variants.

In contrast with the traditional cryptography, physical layer security has attracted the attention of many researchers having aim at reinforcing the security of communication systems. As far as vehicular communication is concerned, it is challenging to maintain secure and reliable communication between the connected vehicles due to the density, mobility, and dynamic network topology. This paper considers a vehicle to infrastructure communication in which a legitimately fixed transmitter equipped with a single antenna transmits a confidential message to a legitimate mobile receiver equipped with multiple antennas in the presence of a passive mobile eavesdropper. In such a single input multiple output wireless system, the receiver performs the maximal ratio combining technique assuming constant vehicle speed. We assume that the antennas are closely spaced and depending upon the imperfect channel state information (CSI), we derive the closed-form expressions for the average outage probability, secrecy outage probability, and average secrecy outage rate over the uniform, exponential, and arbitrary correlated Nakagami- m channels for dual antenna branches. In order to gain insight we also perform the high SNR asymptotic analysis of the outage probability and secrecy outage probability. Simulations are conducted to validate the accuracy of our derived analytic expressions. The computation error analysis is carried out to provide the suitability of the correlation type at the legitimate receiver side. Our findings suggest that the performance of the case with exponential channel correlation is better than those for the uniform and arbitrary. Numerical results show the joint effect of vehicle mobility and the antenna correlation on secrecy performance. Moreover, we also observed that the imperfect knowledge of the CSI degrades the security of the confidential messages severely under the effect of mobility.

With the rapid development of autonomous driving technologies, it becomes difficult to reconcile the conflict between ever-increasing demands for high process rate in the intelligent automotive tasks and resource-constrained on-board processors. Fortunately, vehicular edge computing (VEC) has been proposed to meet the pressing resource demands. Due to the delay-sensitive traits of automotive tasks, only a heterogeneous vehicular network with multiple access technologies may be able to handle these demanding challenges. In this article, we propose an intelligent task offloading framework in heterogeneous vehicular networks with three Vehicle-to-Everything (V2X) communication technologies, namely Dedicated Short Range Communication (DSRC), cellular-based V2X (C-V2X) communication, and millimeter wave (mmWave) communication. Based on stochastic network calculus, this article firstly derives the delay upper bounds of different offloading technologies with certain failure probabilities. Moreover, we propose a federated Q-learning method that optimally utilizes the available resources to minimize the communication/computing budgets and the offloading failure probabilities. Simulation results indicate that our proposed algorithm can significantly outperform the existing algorithms in terms of resource cost and offloading failure probability.

This paper investigates the performance of non-orthogonal multiple access (NOMA) in vehicular networks where a base station (BS) communicates with the vehicles moving away from the BS with single-input multiple-output. To combine the signals received at the antennas, diversity combining techniques such as maximal ratio combining (MRC) and selection combining (SC) are performed at the receiver of each vehicle. However, in practice, the expected performance from the diversity techniques may not be achieved due to the fact that all the diversity branches are not independent and identically distributed (i.i.d) all the time. In this context, analytical expressions of the outage probability and ergodic sum rate are derived for the considered vehicular networks with the assumption of independent but not necessarily identically distributed (i.n.i.d) Nakagami-${m}$fading channels. The performance analysis of NOMA vehicular networks is also extended for multiple-input multiple-output antenna configurations and evaluated in the presence of successive interference cancellation (SIC) error propagation. The obtained analytical results are validated by Monte Carlo simulations. Furthermore, the performance of NOMA is verified with conventional orthogonal multiple access (OMA) for fading parameter$m=1$and$m=2$with perfect channel knowledge and channel estimation. Numerical results show that NOMA outperforms the conventional OMA by approximately 20% and has high sum rate with i.n.i.d as well as i.i.d channel consideration. However, i.n.i.d consideration degrades the performance of NOMA and OMA as the diversity gain achieved with i.n.i.d consideration is less as compared to i.i.d consideration. The performance is further deteriorated with SIC error and channel estimation.

Joint source channel coding (JSCC) is an effective technique in the non-asymptotic and low latency regime, while suffers from high error floor for sequences with high source probabilities and short block-lengths (HSP-SB). Aiming to address this issue, a joint source channel anytime coding (JSCAC) based on the anytime spatially coupled repeat-accumulate (ASC-RA) codes is presented. In the proposed JSCAC scheme, the adopted exponential distributed coupling (EDC) and partial joint expanding window decoding (PJEWD) can efficiently recover the early transmitted HSP-SB messages that are not fully corrected. Meanwhile, the updating mechanisms in the proposed PJEWD mitigate the complexity of expanding window decoding and the error propagation between the source and channel decoders, attributing to a better error performance. The proposed JSCAC is suitable for HSP-SB source transmission, which is a competitive candidate for communications with high reliability and low delay demands, such as streaming source and control applications, etc.

In limited space scenarios, the antennas in the multi-antenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrary correlated antennas over Nakagami-m fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with triple arbitrary correlated diversity branches is derived as a special case. Furthermore, to characterize the performance of energy detector under mobility, the area under the curve of receiver operating characteristic is analysed. The derived expressions converge quickly due to the monotonically decreasing hypergeometric function of two variables. The Monte Carlo simulations substantiate the analytical expressions. Results indicate that antenna correlation deteriorates detection performance. Moreover, the high speed of CR users further decreases the detection performance, especially in the deep fading channel scenarios. This work provides a realistic sensing framework for the CR enabled vehicles.

Secure message dissemination is an important requirement of intelligent transportation systems (ITS). Existing solutions, such as broadcasting, are effective in flooding a message to a wider area, however, they are inherently unreliable and bandwidth inefficient. Furthermore, it is difficult to both assess the authenticity of a message and maintain the privacy of sender in a single solution. Moreover, as a practical solution, there is a need of economic modeling to incentivise vehicles for safe driving and cooperation. This paper proposes a blockchain based message dissemination approach which utilises incentive distribution and reputation management to overcome these challenges. Specifically, with the proposed voting based consensus algorithm, it can assess the authenticity of a message and select the most suitable relay node for its dissemination in a completely decentralised fashion. Meanwhile, the blockchain based integrated incentive and reputation scheme encourages the cooperation among vehicles and strengthens its ability to deliver authentic messages. The security capacity of the proposed solution is demonstrated by a game theoretic analysis. Simulation results show that the proposed approach can save average consensus time by 11% and improve success rate of authentic message dissemination by 17% with less number of hops as compared to the existing solutions.

Spatial modulation (SM) is a new multiple-input multiple-output (MIMO) paradigm in which only one transmit antenna is activated over every symbol duration. So far, efficient SM training sequences (different from the existing design for conventional MIMO systems) remain largely open. Motivated by this research problem, we introduce a novel class of sequence pairs, called "cross Z-complementary pairs (CZCPs)", each displaying zero-correlation zone (ZCZ) properties for both their aperiodic autocorrelation sums and cross-correlation sums. A CZCP may be transmitted in two non-orthogonal SM channels and hence proper design should be conducted to minimize the cross-interference of the two constituent sequences. We construct perfect CZCPs based on selected Golay complementary pairs. We show that the training sequences derived from our proposed CZCPs lead to optimal channel estimation performance over frequency-selective SM channels.

DNA code design aims to generate a set of DNA sequences (codewords) with minimum likelihood of undesired hybridizations among sequences and their reverse-complement (RC) pairs (cross-hybridization). Inspired by the distinct hybridization affinities (or stabilities) of perfect double helix constructed by individual single-stranded DNA (ssDNA) and its RC pair, we propose a novel similarity significance (SS) model to measure the similarity between DNA sequences. Particularly, instead of directly measuring the similarity of two sequences by any metric/approach, the proposed SS works in a way to evaluate how more likely will the undesirable hybridizations occur over the desirable hybridizations in the presence of the two measured sequences and their RC pairs. With this SS model, we construct thermodynamically stable DNA codes subject to several combinatorial constraints using a sorting-based algorithm. The proposed scheme results in DNA codes with larger code sizes and wider free energy gaps (hence better cross-hybridization performance) compared to the existing methods.

High mobility and high spectrum efficiency are two main challenges for 5G communication. Many new technologies are proposed to meet these requirements. Orthogonal time frequency space (OTFS) is a novel scheme to combat the high Doppler effect under time-varying channel. In this paper, an index modulation based orthogonal time frequency space (OTFS-IM) system is proposed for improving BER performance under both high mobility and high spectrum efficiency. In the scheme, the information is conveyed by both constellation symbols and indices bits on Delay-Doppler domain. A low complexity detection strategy based on integrated MMSE-ML detection is proposed, which is very flexible and suitable to high spectrum efficiency signal. By simulation, compared with the conventional OTFS, the proposed OTFS-IM system can achieve better BER performance, maintaining good tolerability against Doppler effect, especially for higher spectral efficiency.

In this work, energy detection‐based spectrum sensing for multiple antenna receiver under the effect of mobility is investigated by considering L number of correlated antenna branches. The authors consider the uniform, exponential and arbitrarily correlation among the antenna branches based on the spacing between them. The moment generating function (MGF) approach is applied to obtain the statistical knowledge of the received signal to noise ratio because the Laplace domain behaviour will help to derive the closed‐form expressions using simple algebraic operations. They derived the closed‐form expressions for the detection probability over Nakagami‐ m fading, in terms of Lauricella and Confluent Hypergeometric function for maximal ratio combining (MRC) and equal gain combining (EGC) diversity techniques under the effect of vehicle mobility. Monte‐Carlo simulation is carried out to validate the derived analytical expressions. The results show that the degradation in detection performance due to fading correlation can be reduced by choosing the appropriate diversity scheme and by increasing the number of antennas. Furthermore, they also found that at high fading parameter ( ) value, the low value of the probability of false alarm and highly correlated fading, MRC works better than EGC for high relative velocity.

Simultaneous localization and mapping (SLAM) has been extensively researched in past years particularly with regard to range-based or visual-based sensors. Instead of deploying dedicated devices that use visual features, it is more pragmatic to exploit the radio features to achieve this task, due to their ubiquitous nature and the widespread deployment of the Wi-Fi wireless network. This article presents a novel approach for collaborative simultaneous localization and radio fingerprint mapping (C-SLAM-RF) in large unknown indoor environments. The proposed system uses received signal strengths (RSS) from Wi-Fi access points (APs) in the existing infrastructure and pedestrian dead reckoning (PDR) from a smartphone, without a prior knowledge about map or distribution of AP in the environment. We claim a loop closure based on the similarity of the two radio fingerprints. To further improve the performance, we incorporate the turning motion and assign a small uncertainty value to a loop closure if a matched turning is identified. The experiment was done in an area of 130 m by 70 m and the results show that our proposed system is capable of estimating the tracks of four users with an accuracy of 0.6 m with Tango-based PDR and 4.76 m with a step counter-based PDR.

With recent advances on the dense low-Earth orbit (LEO) constellation, the LEO satellite network has become one promising solution for providing global coverage for Internet-of-Things (IoT) services. Confronted with the sporadic transmission from randomly activated IoT devices, we consider the random access (RA) mechanism and propose a grant-free RA (GF-RA) scheme to reduce the access delay to the mobile LEO satellites. A Bernoulli–Rician message passing with expectation–maximization (BR-MP-EM) algorithm is proposed for this terrestrial–satellite GF-RA system to address the user activity detection (UAD) and channel estimation (CE) problem. This BR-MP-EM algorithm is divided into two stages. In the inner iterations, the Bernoulli messages and Rician messages are updated for the joint UAD and CE problem. Based on the output of the inner iterations, the expectation–maximization (EM) method is employed in the outer iterations to update the hyperparameters related to the channel impairments. Finally, simulation results show the UAD and CE accuracy of the proposed BR-MP-EM algorithm, as well as the robustness against the channel impairments.

DNA has become an attractive medium for long-term data archiving due to its extremely high storage density and longevity. Short single-stranded DNAs, called oligonucleotides (oligos), have been designed and synthesized to store digital data. Previous works designed the oligos with a pair of primer binding sites (PBSs) (each with a length of around 200) attached at the two ends of each basic readable data block. The addition of PBSs decreases the data density significantly because in the current DNA synthesis, the maximum length of a synthesized oligo in good quality is around 200. Furthermore, the maximum homopolymer run allowed by the existing experiments has been reported to be three nucleotides. In this work, to increase the data density, we have devised and tested an oligo design for DNA-based storage with the basic readable data block appended by a single PBS at one end only, while allowing the maximum homopolymer run to be increased to 4. We also present an oligo assembly algorithm that can reconstruct oligos with a single PBS from the error-prone raw readouts obtained from the sequencing process. We have conducted a wet lab experiment to validate the proposed design, where we tested with 398KB of data stored into 10,750 oligos. The experimental results show that it is possible to recover over 99 percent of the oligo sequences without error, which proves that one PBS is sufficient for implementing a DNA-based data storage system with maximum homopolymer run relaxed to 4. The use of single PBS leads to a significant data density gain from 14.3 to 140.2 percent over the existing short-strand DNA data storage schemes by reserving more nucleotides for storing information bits.

In this paper, we propose efficient wireless power transfer (WPT) policies for various practical scenarios in wirelessly powered communication networks (WPCNs). First, we consider WPT from an energy access point (E-AP) to multiple energy receivers (E-Rs). We formulate the problem of maximizing the total average received power of the E-Rs subject to power constraints of the E-AP, which is a non-convex stochastic optimization problem. Using eigenvalue decomposition techniques, we derive a closed-form expression for the optimal policy, which requires the distribution of the channel state information (CSI) in the network. We then propose a near-optimal policy that does not require this knowledge and prove that its optimality gap can be decreased at the cost of increment in its convergence time. Next, we consider fairness among the E-Rs and propose a quality of service (QoS) aware fair policy that provides fairness and guarantees the required QoS of each E-R. Finally, we study a WPCN where the E-Rs utilize their received energy to transmit information to the E-AP. We maximize a generic fair network utility under the E-Rs' QoS constraints and the E-AP's power constraints. Numerical results show a significant improvement of O(log N) in the total throughput compared to the state-of-theart baselines.

Orthogonal Frequency Division Multiple Access (OFDMA) with Dynamic spectrum allocation (DSA) is able to provide a wide range of data rate requirements. This paper is focused on the design of preamble sequences in OFDMA systems with low peak-to-mean envelope power ratio (PMEPR) property in the context of DSA. We propose a systematic preamble sequence design which gives rise to low PMEPR for possibly non-contiguous spectrum allocations. With the aid of Golay-Davis-Jedwab (GDJ) sequences, two classes of preamble sequences are presented. We prove that their PMEPRs are upper bounded by 4 for any DSA over a chunk of four contiguous resource blocks.

A pair of sequences is called a Z-complementary pair (ZCP) if it has zero aperiodic autocorrelation sums (AACSs) for time-shifts within a certain region, called zero correlation zone (ZCZ). Optimal odd-length binary ZCPs (OB-ZCPs) display closest correlation properties to Golay complementary pairs (GCPs) in that each OB-ZCP achieves maximum ZCZ of width (N + 1)/2 (where N is the sequence length) and every out-of-zone AACSs reaches the minimum magnitude value, i.e. 2. Till date, systematic constructions of optimal OB-ZCPs exist only for lengths 2α± 1, where α is a positive integer. In this paper, we construct optimal OB-ZCPs of generic lengths 2α10β26γ+ 1 (where α, β, γ are non-negative integers and α ≥ 1) from inserted versions of binary GCPs. The key leading to the proposed constructions is several newly identified structure properties of binary GCPs obtained from Turyn's method. This key also allows us to construct OB-ZCPs with possible ZCZ widths of 4 × 10β-1+ 1, 12 × 26γ-1+ 1 and 12 × 10β26γ-1+ 1 through proper insertions of GCPs of lengths 10β, 26γ, and 10β26γ, respectively. Our proposed OB-ZCPs have applications in communications and radar (as an alternative to GCPs).

Complementary set sequences (CSSs) are useful for dealing with the high peak-to-average power ratio (PAPR) problem in orthogonal frequency division multiplexing (OFDM) systems. In practical OFDM transmission, however, certain sub-carriers maybe reserved and/or prohibited to transmit signals, leading to the so-called spectral null constraint (SNC) design problem. For example, the DC sub-carrier is reserved to avoid the offsets in D/A and A/D converter in the LTE systems. While most of the current research focus on the design of low PAPR CSSs to improve the code-rate, few works address the aforementioned SNC in their designs. This motivates us to investigate CSSs with SNC as well as low PAPR property. In this article, we present systematic constructions of CSSs under SNCs and low PAPR. First, we show that mutually orthogonal complementary sets (MOCSs) can be used as seed sequences to generate new CSSs with SNC and low PAPR, and then provide an iterative technique for the construction of MOCSs which can be further used to generate complementary sets (CSs) with low PAPRs and spectral nulls at varying positions in the designed sequences. Next, inspired by a recent idea of Chen, we propose a novel construction of these seed MOCSs with non-power-of-two lengths from generalized Boolean functions.

In this paper, we consider pilot-aided channel estimation and equalization for single-carrier and single-carrier frequency division multiple-access (SC-FDMA) transmission over doubly-selective channels (DSC). To reduce the channel estimation (CE) parameters, the DSC is modelled using a complex-exponential basis expansion model (CX-BEM) with non-uniform BEM frequencies. We optimize the CX-BEM basis functions using CE error minimization as the objective. As a result, the channel modelling error is greatly reduced. Next, we propose a BEM-based per-survivor processing (PSP) technique and combine it with a decision-directed channel estimator to obtain a channel-tracking equalizer at the receiver. The resultant BEM-PSP receiver significantly improves the channel estimation mean square error (MSE) and the bit error rates (BER) performance in fast fading multi-path channel, thanks to the channel tracking capability embedded within its Viterbi equalizer. Finally, we employ cross-frame channel interpolation, and power distribution between the data and pilot symbols to further improve the system performance at high fading rates. Extensive simulation results show that our BEM-PSP receiver outperforms many existing methods and approaches close to an ideal receiver with perfectly known CSI under various fading scenarios.

In this paper, we study the secrecy throughput in a massive Multiple-Input-Multiple-Output (MIMO) full-duplex wireless powered communication network (WPCN). The network consists of a massive MIMO base station (BS) and two groups of single-antenna sensor nodes which harvest energy from the BS. The first group, referred to as information transmitters (ITs), use the harvested energy to transmit information back to the BS; the second group, referred to as energy receivers (ERs), use the harvested energy for non-transmission related operations. We consider a two-slot protocol. In the first time slot, all nodes harvest energy from the BS. In the second slot, ITs transmit information to the BS, while BS use one set of its antennas to receive the signals and the other set of antennas to transmit artificial noise (AN) to the ERs. The AN serves two purposes: wireless power transfer to the ERs, and information security for the ITs with ERs, which are considered as potential eavesdroppers. We aim to maximize the total secrecy throughput of ITs subject to the ERs' received energy constraints. The problem is shown to be non-convex. To tackle the problem, we propose a twostage suboptimal approach, referred to as Maximizing Received AN aided Secrecy Throughput Maximization (MRAN-STM). In the first stage, the transmitted AN is optimized to maximize the minimum received AN of all the ERs. Then, in the second stage, the power allocation and time slot duration are optimized to maximize the total secrecy throughput. Numerical results show the improvement of the proposed algorithm.

In this paper, we investigate the secrecy throughput for a full-duplex wireless powered communication network. A multi-antenna base station (BS) transmits energy towards nodes all the time and each node harvests energy prior to its transmission time slot. Nodes sequentially transmit their confidential information to the BS in presence of other nodes which are considered as potential eavesdroppers. We derive the secrecy rate and formulate the sum secrecy throughput optimization of all nodes. The optimization variables are the time slot duration and the BS beamforming during different transmission phases. The problem is non-convex and non-trivial. We propose a suboptimal approach in which the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during the information transmission phase, with which we then obtain the optimum beamforming in each time slot and its duration. We compare our algorithm with uniform time slot and uniform beamforming in different settings and demonstrate its superior performance.

BATS codes are a class of random linear network coding scheme which have close-to-optimal achievable rates, while adaptive recoding is a packet combining scheme which adds a boost to the throughput by adapting the packet combining at the relay to the random fluctuations in the number of erasures in individual batches. In this paper, we apply BATS code with adaptive recoding for the transmission on wireless relay network. In contrast to the existing adaptive recoding schemes, we propose a model which can make use of the broadcast nature of wireless communication to enhance the achievable rates via overhearing. We also optimize the packet efficiency, i.e., minimizing the average number of channel use for each input packet for a successful decoding.

An ambiguity function is widely used in radar and communication. It is highly desirable that an ambiguity function should have low range- and Doppler- sidelobe as the good resolution in both range and Doppler domain. In this paper, a method which combines time-domain binomial design and frequency-domain binomial design via a Point-wise Minimum Processor (PMP) is proposed. The time-domain binomial design is designed by [7] to clear the range sidelobe in a very large Doppler area and the frequency-domain binomial design is proposed by this paper to avert the Doppler sidelobe in a very large range area. It is shown that the new method produces a "thumbtack-like" ambiguity function with a good range/doppler sidelobe suppression and results in good distinction of two moving targets.

Orthogonal time frequency space (OTFS) modulation scheme is based on a novel 2-dimensional modulation technique designed to transform the time-varying multi-path channel into a time invariant Delay- Doppler channel. In general, the Message Passing (MP) algorithm works well for data detection in OTFS, but its complexity is quite large. In this paper, a low complexity MP algorithm for data detection in OTFS is presented based on a Doppler compensation precoder and a modified stopping criterion. Besides, an improved Approximate Message Passing (AMP) algorithm based on covariance processing, having almost the same complexity as that of conventional AMP algorithm, but exhibiting better bit error rate(BER) performance is presented, as demonstrated by simulation results.

This paper focuses on designing a set of dedicated sequences for Multi-Static Primary Surveillance Radar (MSPSR) systems. An efficient algorithm to generate a set of unimodular sequences with good local auto-ambiguity functions (AFs) and cross-AFs over specific Doppler bins and delay bins of interest is proposed. The performance of the proposed algorithm is evaluated in comparison with a prior art called the energy gradient method via numerical simulation. The numerical results show that the newly generated sequences possess lower maximum sidelobe in the area of interest.

Vehicle to vehicle (V2V) communications using dedicated short range communications (DSRC) are considered as promising technology for enhancing road safety. However, the V2V communications see a challenge from obstruction of big vehicle such as big bus or truck. Based on our measurement, the big vehicle can cause signal loss from 10 to 15 dB. It makes the communication range shorter and reduces the safety message dissemination capability. In this paper, we analyze the impact of multiple big vehicles shadowing on the V2V communication. We propose a model which takes into account both geometric and stochastic shadowing of multiple big vehicles. Based on the proposed model, we derive the average length of the shadow region and the shadowing loss caused by multiple big vehicles. It is revealed that when the number of big vehicles increases from 1 to 25 vehicle per km, the shadow region increases from 50 m to more than 400 m on the road. Furthermore, the shadowing loss causes the packet delivery ratio of a typical car reducing from 90% to 20% or 50%, depending on whether the big vehicles are on the same or the next lane of this typical car.

Dynamic Spectrum Access (DSA)/Cognitive Radio (CR) systems access the channel in an opportunistic, noninterfering manner with the primary network, thus being a promising approach to solve the problem of spectrum scarcity. Energy Detection, a spectrum sensing technique for DSA/CR systems, is widely used for blind sensing of unused frequency bands due to its non-parametric sensing ability and computationally low complexity. However, spectrum sensing becomes more challenging in Cognitive Vehicular Networks (CVNs) due to Secondary User's (SU's) mobility and often yields a detection performance loss as compared to static scenarios. In order to mitigate the impact of reduced detection performance due to mobility, the usage of an improved version of energy detection technique is proposed in this paper. Usage of Improved Energy Detection (IED) technique in CVNs results more than 10% increment in DSA/CR system performance. In this paper, we study the joint impact of SU's sensing range, PU's protection range and SU's mobility model on the PU Activity using IED technique in CVNs, with detection probability and probability of false alarm as the performance metrics. Also, we derive a closed form expression for the probability of PU being inside SU's sensing range. Based on the proposed framework, numerical results show great agreement with analysis, yielding a superior performance.

In this paper, we consider safety message transmission in a dense vehicular network. With increasing vehicular network density, the collision rate increases when multiple vehicles transmit safety messages simultaneously. To address this issue, we propose a request-transmission split time division multiple access (TDMA) scheme, referred to as RTS-TDMA. In our scheme, we divide a frame into three phases, i.e., a contention access phase, a broadcast feedback phase, and a contention-free transmission phase. Each vehicle selects a repetition rate according to a given probability distribution and repeats the transmission of its request packet to improve the reliability of the request. In addition, a roadside unit acts as the coordinator and uses a successive interference cancellation technique to resolve request collisions. RTS-TDMA also reduces the request time percentage by containing only the vehicle identity in each request packet. Both theoretical analysis and numerical results verify that the RTS-TDMA scheme can provide higher throughput than the coded slotted ALOHA scheme.

This study investigates the performance of a bidirectional/two‐way relaying scheme with an energy‐harvesting relay while amplify‐and‐forward relaying is performed. With multiple antennas at the relay, the authors propose the use of the max–min antenna selection scheme in the context of an energy harvesting scenario. They show that the proposed scheme attains diversity gain, whereby its end‐to‐end symbol error probability performance improves with increments in the number of energy harvesting relay antennas. For limited battery capacity at the energy harvesting node, they derive the lower bound of the outage probabilities and observe a good match between the analytical and simulated results. For this scenario, a simple power provisioning technique is proposed to optimise the end nodes' transmission powers. It is observed that the proposed technique saves significantly more transmission energy than the conventional equal power provisioning technique.

In this paper, we study the secrecy throughput of a full-duplex wireless powered communication network (WPCN) for Internet of Things (IoT). The WPCN consists of a full-duplex multiantenna base station (BS) and a number of sensor nodes. The BS transmits energy all the time, and each node harvests energy prior to its transmission time slot. The nodes sequentially transmit their confidential information to the BS, and the other nodes are considered as potential eavesdroppers. We first aim to optimize the sum secrecy throughput of the nodes. The optimization variables are the duration of the time slots and the BS beamforming vectors in different time slots. The optimization problem is shown to be nonconvex. To tackle the problem, we propose a suboptimal two stage approach, referred to as sum secrecy throughput maximization (SSTM). In the first stage, the BS focuses its beamforming to blind the potential eavesdroppers (other nodes) during information transmission time slots. Then, the optimal beamforming vector in the initial noninformation transmission time slot and the optimal time slots are derived. We then consider secrecy throughput fairness among the nodes and propose max-min fair (MMF) and proportional fair (PF) algorithms. The MMF algorithm maximizes the minimum secrecy throughput of the nodes, while the PF achieves a good tradeoff between the sum secrecy throughput and fairness among the nodes. Through the numerical simulations, we first demonstrate the superior performance of the SSTM to uniform time slotting and beamforming in different settings. Then, we show the effectiveness of MMF and PF algorithms.

In this paper, we propose a novel framework of low-cost link adaptation for spatial modulation multiple-input multiple-output (SM-MIMO) systems-based upon the machine learning paradigm. Specifically, we first convert the problems of transmit antenna selection (TAS) and power allocation (PA) in SM-MIMO to ones-based upon data-driven prediction rather than conventional optimization-driven decisions. Then, supervised-learning classifiers (SLC), such as the K -nearest neighbors (KNN) and support vector machine (SVM) algorithms, are developed to obtain their statistically-consistent solutions. Moreover, for further comparison we integrate deep neural networks (DNN) with these adaptive SM-MIMO schemes, and propose a novel DNN-based multi-label classifier for TAS and PA parameter evaluation. Furthermore, we investigate the design of feature vectors for the SLC and DNN approaches and propose a novel feature vector generator to match the specific transmission mode of SM. As a further advance, our proposed approaches are extended to other adaptive index modulation (IM) schemes, e.g., adaptive modulation (AM) aided orthogonal frequency division multiplexing with IM (OFDM-IM). Our simulation results show that the SLC and DNN-based adaptive SM-MIMO systems outperform many conventional optimization-driven designs and are capable of achieving a near-optimal performance with a significantly lower complexity.

A pair of sequences is called odd-length binary Z-complementary pair (OB-ZCP) if it is of odd-length and has zero aperiodic autocorrelation sums (AACSs) for all time-shifts within a certain region around the in-phase position, commonly known as zero correlation zone (ZCZ). There are two types of OB-ZCPs, namely Type-I OB-ZCPs and Type-II OB-ZCPs. Type-I OB-ZCPs have ZCZ around the in-phase position. Type-II OB-ZCPs have the ZCZ around the end-shift position. An OB-ZCP (Type-I or Type-II) of odd-length N is called Z-optimal if it achieves a maximum ZCZ width of (N + 1)/2. To date, a systematic construction of Type-II Z-optimal OB-ZCPs exist only for very limited lengths of the form 2m± 1, where m is a positive integer. It employs insertion method and delete method on binary Golay complementary pairs (GCPs) of length 2mderived from second order Reed-Muller codes. In this article, based on iterative insertion method, we construct Type-II Z-optimal OB-ZCPs of lengths 2m+ 3.

Real-time traffic updates, safety and comfort driving, infotainment, and so on, are some envisioned applications in vehicular networks. Unlike traditional broadcast, network-coding-assisted broadcast can satisfy multiple vehicles with different data items in a coded form. However, server side encoding requires the prior knowledge about vehicles’ cache information for the successful decoding at the vehicles’ sides. The explicit cache upload from vehicles to Road Side Unit (RSU) wastes upload bandwidth. In multi-RSU vehicular networks, we propose a Cooperative Cache Transfer-based On-demand Network Coded Broadcast called CCTCB. In the proposed CCTCB approach, vehicles do not need to upload their cache information to the server, rather the RSU server learns the vehicles’ cache intrinsically. We derive a probabilistic model to analyze the coding opportunity in the proposed cooperative cache transfer mechanism incorporating vehicle mobility. The comprehensive simulation results validate the superiority of the proposed approach.

This paper considers a low-complexity Gaussian message passing (GMP) Multi-User Detection (MUD) scheme for a coded massive multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (massive MIMO-NOMA), in which a base station with$N_{s}$antennas serves$N_{u}$sources simultaneously in the same frequency. Both$N_{u}$and$N_{s}$are large numbers, and we consider the overloaded cases with$N_{u}>N_{s}$. The GMP for MIMO-NOMA is a message passing algorithm operating on a fully-connected loopy factor graph, which is well understood to fail to converge due to the correlation problem. The GMP is attractive as its complexity order is only linearly dependent on the number of users, compared to the cubic complexity order of linear minimum mean square error (LMMSE) MUD. In this paper, we utilize the large-scale property of the system to simplify the convergence analysis of the GMP under the overloaded condition. We prove that thevariancesof the GMP definitely converge to the mean square error (MSE) of the LMMSE multi-user detection. Second, themeansof the traditional GMP will fail to converge when$N_{u}/N_{s}< (\sqrt {2}-1)^{-2}\approx 5.83$. Therefore, we propose and derive a new convergent GMP called scale-and-add GMP (SA-GMP), which always converges to the LMMSE multi-user detection performance for any$N_{u}/N_{s}>1$, and show that it has a faster convergence speed than the traditional GMP with the same complexity. Finally, the numerical results are provided to verify the validity and accuracy of the theoretical results presented.

Recently, much attentions have been paid to network coding-assisted data broadcast in vehicular networks. However, majority of the works consider all the accessed data items are the same size. In this work, we have studied the network coding-assisted heterogeneous on-demand data access in vehicular networks. Firstly, we have investigated the less efficiency of conventional coding assisted approach in accessing heterogeneous data items in real-time vehicular environment. Due to ignoring the impact of heterogeneous data items in decoding, the conventional coding does not achieve expected performance in accessing data items with diverse size. Secondly, based on our observations, we have proposed a new algorithm. The proposed network coding assisted approach exploits the different MCSs (Modulation and coding schemes) of IEEE 802.11p physical layer for leveraging the variable serving rate considering the dynamic positions' of vehicles along with the vehicle mobility. Thirdly, we have built a vehicular simulation environment and evaluated the performance of the proposed approach along with competitive no-coding and coding-assisted approaches under various circumstances. The results show that the proposed approach outperforms the state-of-the-art approaches in terms of improving the on-demand requests serving capability and reducing the data access time.

In this paper, we investigate the optimal scheduling schemes for multi-hop video streaming with generation-based random linear network coding in vehicular networks. With the proposed scheduling algorithm, each vehicle in the network is guaranteed to decode the video block from the dedicated transmissions and the overheard packets with high probability. We derive explicit expression for the number network coded packets that need to be generated at each vehicle in order to ensure successful decoding at its downstream vehicles, by considering the overheard packets due to wireless broadcasting. Furthermore, the distance beyond which the channel can be reused is optimized to maximize the achievable video generating rate at the source node. Simulation results show that the proposed coding and scheduling scheme achieves higher end-to-end throughput than the conventional packet erasure correction coding scheme that is only optimized for one-hop communications.

The low‐density parity‐check (LDPC) code design for three‐terminal (namely, source, relay, and destination) relay network while considering decode‐and‐forward protocol is studied. Numerous works have been done on LDPC relay code design with parity bits forwarding approach, where additional parity bits are generated at the relay and forwarded to the destination. Most of the previous works assume that the forwarded parity bits are received perfectly at the destination and hence ignore the impact of relay–destination channel. This assumption is unrealistic in practical/finite‐length codeword scenario. In this study, a protograph based LDPC code design is proposed while lifting the above unrealistic assumption. A Gaussian approximated density evolution is presented for the proposed scheme, which considers that the overall codeword experiences source–destination and relay–destination channels. For various designed codes with different rates, the authors show that the asymptotic thresholds of the designed codes are very close to the corresponding capacity bounds. Asymptotically, our designed code gives similar decoding threshold compared to the existing optimised irregular LDPC codes, while protograph codes are simpler to optimise and encode than the irregular LDPC codes. More importantly, proposed code performs better than the existing LDPC‐based relay codes for finite‐length scenarios.

Through the embedded processors and communication technologies, vehicles are increasingly being connected with the Internet of Things. Recently, much attentions have been paid to network-coding-assisted data broadcast in vehicular networks. However, majority of the works consider all the accessed data items are the same size. In this paper, we have studied the network coding-assisted heterogeneous on-demand real-time data access in vehicular networks. First, we have investigated the less efficiency of conventional coding assisted approach in accessing heterogeneous data items in real-time vehicular environment. Due to ignoring the impact of heterogeneous data items in decoding, the conventional coding does not achieve expected performance in accessing data items with diverse size. Second, based on our observations, for efficiently serving heterogeneous data items, we have proposed a dynamic threshold-based coding-assisted real-time data broadcast approach called earliest deadline firstΘ. Third, we have derived the probabilistic analysis of the system performance of the proposed approach and the state-of-the-art approaches. Fourth, based on our further investigations, we have proposed another approach, called inverse of slack time multiply distance with THRESHOLD (Θ) (ISXDΘ). The proposed network coding-assisted ISXDΘexploits the different modulation and coding scheme of IEEE 802.11p physical layer for leveraging the variable serving rate considering the dynamic positions of vehicles along with the vehicle mobility. The comprehensive simulation results demonstrate the efficacy of the proposed approaches over the state-of-the-art approaches in terms of improving the on-demand requests serving capability and reducing the system response time.

Wireless communication enabled by unmanned aerial vehicles (UAVs) has emerged as an appealing technology for many application scenarios in future wireless systems. However, the limited endurance of UAVs greatly hinders the practical implementation of UAV-enabled communications. To overcome this issue, this paper proposes a novel scheme for UAV-enabled communications by utilizing the promising technique of proactive caching at the users. Specifically, we focus on content-centric communication systems, where a UAV is dispatched to serve a group of ground nodes (GNs) with random and asynchronous requests for files drawn from a given set. With the proposed scheme, at the beginning of each operation period, the UAV pro-actively transmits the files to a subset of selected GNs that cooperatively cache all the files. As a result, when requested, a file can be retrieved by each GN either directly from its local cache or from its nearest neighbor that has cached the file via device-to-device communications. It is revealed that there exists a fundamental trade-off between the file caching cost, which is the total time required for the UAV to transmit the files to their designated caching GNs, and the file retrieval cost, which is the average time required for serving one file request. To characterize this trade-off, we formulate an optimization problem to minimize the weighted sum of the two costs, via jointly designing the file caching policy, the UAV trajectory, and communication scheduling. As the formulated problem is NP-hard in general, we propose efficient algorithms to find high-quality approximate solutions for it. Numerical results are provided to corroborate our study and show the great potential of proactive caching for overcoming the endurance issue in UAV-enabled communications.

This letter is focused on increasing the zero correlation zone (ZCZ) of even-length binary Z-complementary pairs (EB-ZCPs). Till date, the maximum ZCZ ratio (i.e., ZCZ width over the sequence length) for systematically constructed EB-ZCPs is 2/3. In this letter, we give a construction of EB-ZCPs with lengths 2α+210β26γ+ 2 (where α, β, and γ are nonnegative integers) and ZCZ widths 3 × 2α10β26γ+ 1, thus achieving asymptotic ZCZ ratio of 3/4. The proposed EB-ZCPs are constructed via proper insertion of concatenated odd-length binary ZCPs. The ZCZ width is proved by exploiting several newly identified intrinsic structure properties of binary Golay complementary pairs, obtained from Turyn's method. The proposed EB-ZCPs have aperiodic autocorrelation sums (AACS) magnitude of 4 outside the ZCZ region (except for the last time-shift taking AACS value of zero).

Anytime codes are known to be useful for tracking and controlling unstable systems over noisy channels. Although practical anytime codes have been developed for point-to-point communication, to date no practical code constructions and tractable analysis of anytime codes are known to exist for relay networks. In this paper, we study the design and theoretical analysis of anytime codes for a three-terminal relay network. We propose bilayer codes for anytime transmission over the relay network and consider two transmission schemes for the relay-destination link. For both schemes, we analytically derive the delay-exponents and show that they closely match the corresponding numerical results obtained from density evolution. Through simulation, we also show the finite-length performance of the proposed bilayer anytime code.

In this paper, we study the achievable throughput of batched temporal network coding in multi-hop erasure channels, where network coding is applied only within small coding blocks and each communication hop is modeled as a Gilbert-Elliott (GE) packet erasure channel. The GE channel is a 2-state Markov model that is commonly used for channels with memory. While channel memory does not affect the end-to-end capacity of multi-hop erasure channels, we show that it degrades the end-to-end throughput, when batched network coding with finite batch size is applied, due to the higher variance in erasures within one coding block. On the other hand, if the initial channel state information is available, the channel variance can be significantly reduced. We show that this fact can be utilized for improving the efficiency of the recoding operations at the intermediate nodes, and hence improve the end-to-end throughput of batched network coding schemes. Specifically, we propose adaptive recoding operations, where the network coded packets are adaptively generated based on the number of received packets and the initial channel state for each coding block. The simulation results show that the proposed adaptive recoding scheme significantly enhances the end-to-end throughput of batched network coding over multi-hop GE channels.

We investigate spectrally-constrained sequences (SCSs), which are applicable to the communication and radar systems operating over non-contiguous carriers or frequency slots. Typical examples of such systems are overlay cognitive radio or cognitive radar networks. First, we derive the periodic- and aperiodic-correlation lower bounds for single-channel SCSs and multi-channel SCSs by convex optimization in the frequency domain. Each of these bounds reduces to a Welch bound when the number of forbidden carriers is set to zero. We then propose systematic constructions of optimal unimodular single-channel SCSs with the aid of cyclic difference sets and the theory of maximal-length shift register sequences.

In this paper we consider scalable video multicasting over erasure networks with heterogeneous video quality requirements. With random linear network coding (RLNC) applied at the intermediate nodes the information received by the destinations is determined by the associated channel rank distributions based on which we obtain the optimal achievable code rate at the source node. We show that although a concatenation of priority encoded transmission (PET) with RLNC achieves the optimal code rate it incurs prohibitive high coding complexity. On the other hand batched sparse (BATS) code has been recently proposed for unicast networks which has low coding complexity with near-optimal overhead. However the existing BATS code design cannot be applied for multicast networks with heterogeneous channel rank distributions at different destinations. To this end we propose a novel expanding window BATS (EW-BATS) code where the input symbols are grouped into overlapped windows according to their importance levels. The more important symbols are encoded with lower rate and hence they can be decoded by more destinations while the less important symbols are encoded with higher rate and are only decoded by the destinations with high throughput for video quality enhancement. Based on asymptotical performance analysis we formulate the linear optimization problems to jointly optimize the degree distributions for each window and the window selection probabilities. Simulation results show that the proposed EW-BATS code satisfies the decoding requirements with much lower transmission overhead compared with separate BATS code where the degree distributions are separately optimized for each destination.

MIMO-NOMA combines multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) techniques to address heterogeneous challenges, such as massive connectivity, low latency, and high reliability in the 5G cellular communication system and beyond. In this paper, a coded MIMO-NOMA system with capacity-approaching performance and low implementation complexity is proposed. Specifically, the proposed MIMO receiver consists of a linear minimum mean-square error (LMMSE) multi-user detector and a bank of single-user message-passing decoders, which decompose the overall NOMA signal recovery into distributed low-complexity computations with iterative processing. An asymptotic extrinsic information transfer analysis is employed to model the overall performance, and a novel class of multi-user irregular repeat-accumulate channel codes that match with the LMMSE multi-user detector in the iterative decoding process are constructed for the system. As a result, the proposed coded MIMO-NOMA system achieves asymptotic performance within 0.2 dB from the theoretical capacity. Simulation results validate the reliability and robustness of the proposed system in practical settings that include different system loads, iteration numbers, code lengths, fast/block fading, and imperfect channel estimation.

Owing to their ideal correlation properties, complete complementary codes (CCC) have found numerous applications in wireless engineering, in particular they have been employed to support interference-free multi-carrier code-division multiple access systems with improved spectral efficiency. In this paper, we propose a simple construction of CCCs of length Nn(n e N) based on paraunitary matrices of size N × N. This algorithm can generate CCCs from N-shift cross-orthogonal sequence sets for n > 1. Then, we introduce an easy implementation of the proposed algorithm by multiplexers and read-only memories (ROMs), i.e., a multiplier-free implementation. As multipliers are avoided, substantial reduction of construction complexity for CCCs is obtained as compared to the existing works.

In cloud radio access network (C-RAN), remote radio heads (RRHs) and users are uniformly distributed in a large area such that the channel matrix can be considered as sparse. Based on this phenomenon, RRHs only need to detect the relatively strong signals from nearby users and ignore the weak signals from far users, which is helpful to develop low-complexity detection algorithms without causing much performance loss. However, before detection, RRHs require to obtain the realtime user activity information by the dynamic grant procedure, which causes the enormous latency. To address this issue, in this paper, we consider a grant-free C-RAN system and propose a low- complexity Bernoulli-Gaussian message passing (BGMP) algorithm based on the sparsified channel, which jointly detects the user activity and signal. Since active users are assumed to transmit Gaussian signals at any time, the user activity can be regarded as a Bernoulli variable and the signals from all users obey a Bernoulli-Gaussian distribution. In the BGMP, the detection functions for signals are designed with respect to the Bernoulli-Gaussian variable. Numerical results demonstrate the robustness and effectivity of the BGMP. That is, for different sparsified channels, the BGMP can approach the mean-square error (MSE) of the genie-aided sparse minimum mean-square error (GA- SMMSE) which exactly knows the user activity information. Meanwhile, the fast convergence and strong recovery capability for user activity of the BGMP are also verified.

Low-cost message passing (MP) algorithm has been recognized as a promising technique for sparse vector recovery. However, the existing MP algorithms either focus on mean square error (MSE) of the value recovery while ignoring the sparsity requirement, or support error rate (SER) of the sparse support (non-zero position) recovery while ignoring its value. A novel low-complexity Bernoulli-Gaussian MP (BGMP) is proposed to perform the value recovery as well as the support recovery. Particularly, in the proposed BGMP, support-related Bernoulli messages and value- related Gaussian messages are jointly processed and assist each other. In addition, a strict lower bound is developed for the MSE of BGMP via the genie-aided minimum mean-square-error (GA-MMSE) method. The GA-MMSE lower bound is shown to be tight in high signal-to-noise ratio. Numerical results are provided to verify the advantage of BGMP in terms of final MSE, SER and convergence speed.

Road Side Units (RSUs)-based data communication (V2I) is an increasingly popular technique in vehicular networks to alleviate the frequent vehicle-to-vehicle (V2V) communication problem. On-demand broadcasting is a scalable approach for supporting large clients with dynamic demands. However, the performance of the traditional on-demand broadcast can be improved significantly with the incorporation of network coding in server side broadcast. Although majority of the coding based studies assume the sizes of the server's accessed data items are the same, in reality, the sizes of the accessing data items are different. Keeping in mind of two important characteristics, from both the the vehicles' submitted requests point of view and the server's serving time point of view, we propose an efficient dynamic threshold based coding based data broadcast approach. The proposed approach considers both the deadline of request and the heterogeneity of accessed data item in scheduling, which can minimize the number of deadline missed requests and reduce the overall data access time. Simulation results verify the efficiency of our proposed approach over the traditional approaches.

In vehicular ad hoc networks, the basic safety message (BSM) is periodically broadcast by every vehicle on the road. By exchanging BSMs with neighbors, vehicles aggregate the collected information to get aware of driving environment and react promptly in case of any danger. The BSM dissemination is particularly crucial for vehicles at urban road intersections to avoid car collisions. However, blocking from buildings and obstacles at road corners creates a non-line-of-sight (NLOS) condition for vehicle-to-vehicle communications. The direct broadcasting cannot guarantee a reliable BSM delivery. To solve this problem, we consider the relay-assisted scheme and apply the repetitive transmission pattern to increase the successful reception probability. By exploiting the benefit of network coding, we propose two promising network coded relaying schemes, coding at one relay node (RT-NCI) and coding at all relay nodes (RT-NC), to enhance the reliability of BSM dissemination and improve the system scalability. The superiorities of the proposed schemes are verified by simulation results.

In this paper, we propose a design of spatially coupled repeat-accumulate (SC-RA) codes over a three-phase bidirectional relay. The channels between the nodes are assumed binary input additive white Gaussian noise (BIAWGN) channels, while decode-and-forward (DF) relaying is considered at the relay. We present low complexity density evolution analysis for the proposed bidirectional relay code, which leads to a simple code design procedure. The performance of the proposed relay coding scheme significantly outperforms the performance of the relay code based on regular RA block code. We show that the decoding threshold of the proposed relay code approaches the theoretical limit for a wide range of design rates. We also show the finite-length performance of the proposed bidirectional relay code through simulation results.

Generalized Levenshtein bound (GLB) is a lower bound on the maximum aperiodic correlation sum of quasi-complementary sequence set (QCSS) which refers to a set of two-dimensional matrices with low non-trivial aperiodic auto- and cross-correlation sums. GLB is an indefinite fractional quadratic function of a “simplex” weight vector w and three additional parameters associated with QCSS. We present a novel approach to analytically conduct fractional quadratic optimization for the tightening of the GLB. Our key idea is to apply the frequency-domain decomposition of the relevant circulant matrix (i.e., the numerator term of GLB) to convert the non-convex problem into a convex one. We derive a new weight vector which asymptotically leads to a tighter GLB (over the Welch bound) for all possible (K, M) cases, where K, M denote the set size, the number of channels, of QCSS, respectively.

In this paper, a novel multiple-input multiple-output (MIMO) transmission scheme, termed as receive antenna selection (RAS)-aided spatial modulation MIMO (SM-MIMO), is proposed for millimeter-wave (mmWave) communications. It employs the spatial modulation (SM) concept and the RAS technique to tackle the costs of the multiple radio frequency (RF) chains at both link ends. Moreover, we develop a pair of RAS algorithms for the proposed mmWave RAS-SM scheme based on the capacity maximization (max-capacity) and the bit-error rate (BER) minimization criteria, which are formulated as two combinatorial optimization problems. The theoretical gradients of the capacity and the BER with respect to RAS variables are derived and the convexities of these problems are discussed. Furthermore, a novel iterative algorithm through jointly designing the log-barrier algorithm (LbA) and the simplified conjugate gradient method is proposed for RAS optimization. Our simulation results show that the proposed RAS-SM schemes are capable of achieving considerable performance gains over conventional norm-based and eigenvalue-based schemes in mmWave MIMO channels, while avoiding an overwhelming complexity imposed by exhaustive search.

We present a novel transform for periodic complementary sets (PCSs) over two-valued alphabets from a large set of difference families. This is achieved by generalizing Golomb's idea in 1992, which was for transformed perfect sequences with zero autocorrelations only. Based on the properties of difference family, a sufficient condition for such two-valued PCSs is derived. Systematic constructions of two-valued periodic complementary pairs are presented. It is shown that many lengths for which binary PCSs do not exist become admissible for our proposed two-valued PCSs.

With the proliferation of Internet-of-Things (IoT), the chirp spread spectrum (CSS) technique is re-emerging for communications. Although CSS can offer high processing gain, its poor spectral efficiency and the lack of orthogonality among different chirps tend to limit its potential. In this paper, we derive the condition to orthogonally multiplex an arbitrary number of linear chirps. For the first time in the literature, we show that the maximum modulation rate of the linear continuous-time chirps satisfying the orthogonality condition can approach the Nyquist signaling rate, the same as single-carrier waveforms with Nyquist signaling or orthogonal frequency-division multiplexing signals. The performance of the proposed orthogonal CSS is analyzed in comparison to the emerging LoRa systems for IoT applications with power constraint, and its capability for high-speed communications is also demonstrated in the sense of Nyquist signaling.

This paper presents two low-complexity edge-based scheduling schemes, referred to as the e-Flooding and e-Shuffled schedules, for the belief-propagation (BP) decoding of low-density parity-check and Reed-Solomon codes. The proposed schedules selectively update the edges of the code graph based on the run-time reliability of variable and check nodes. Specifically, new message update is propagated exclusively along the unreliable edges of the code graph. This reduces the decoding complexity of BP algorithm as only a partial set of message updates is computed per decoding iteration. Besides, restricting the flow of message updates may also precludes the occurrence of some short graph cycles, which helps to preserve the BP message independence at certain variable and check nodes. Using numerical simulations, it is shown that the proposed edge-based schedules reduce the BP decoding complexity by more than 90% compared with the prior-art BP schedules, while simultaneously improving the error-rate performance, at medium-to-high signal-to-noise ratio over additive white Gaussian noise channel.

The multilevel-cell NAND flash memory experiences permanent hard errors due to cell defects (stuck cells). To overcome this problem, stuck cells are either regarded as erasures by the decoder based upon the knowledge of stuck cell location, or the entire memory block containing the stuck cells is marked as bad block and made unavailable for future usage. In this paper, a multiround inferred stuck-cell erasure belief-propagation (BP) decoding (ISED) is proposed in which the stuck cell locations are assumed to be unknown to the decoder. To perform the inferred erasure decoding, the input channel log-likelihood ratio (LLR) information is attenuated before BP decoding by modifying the threshold voltage distribution functions. In case of decoding failure, the probable stuck cell locations are inferred by using the flash's read-back voltage signal and the decoded code-word bits. For all such inferred stuck cells, the input LLRs are set to zero for subsequent rounds of BP decoding. As the likely incorrect LLRs corresponding to the stuck cells are erased, the performance of BP decoder is substantially improved. Simulation results show that the error-rate performance is improved by more than two orders of magnitude with a moderate increase in decoding complexity under the proposed ISED scheme.

This paper considers a low-complexity Gaussian Message Passing Iterative Detection (GMPID) algorithm for Multiple-Input Multiple-Output systems with Non-Orthogonal Multiple Access (MIMO-NOMA), in which a base station with $N_r$ antennas serves $N_u$ sources simultaneously. Both $N_u$ and $N_r$ are very large numbers and we consider the cases that $N_u>N_r$. The GMPID is based on a fully connected loopy graph, which is well understood to be not convergent in some cases. The large-scale property of the MIMO-NOMA is used to simplify the convergence analysis. Firstly, we prove that the variances of the GMPID definitely converge to that of Minimum Mean Square Error (MMSE) detection. Secondly, two sufficient conditions that the means of the GMPID converge to a higher MSE than that of the MMSE detection are proposed. However, the means of the GMPID may still not converge when $ N_u/N_rN_r$ with a faster convergence speed. Finally, numerical results are provided to verify the validity of the proposed theoretical results.

This paper considers a iterative Linear Minimum Mean Square Error (LMMSE) detection for the uplink Multiuser Multiple-Input and Multiple-Output (MU-MIMO) systems with Non-Orthogonal Multiple Access (NOMA). The iterative LMMSE detection greatly reduces the system computational complexity by departing the overall processing into many low-complexity distributed calculations. However, it is generally considered to be sub-optimal and achieves relatively poor performance. In this paper, we firstly present the matching conditions and area theorems for the iterative detection of the MIMO-NOMA systems. Based on the proposed matching conditions and area theorems, the achievable rate region of the iterative LMMSE detection is analysed. We prove that by properly design the iterative LMMSE detection, it can achieve (i) the optimal sum capacity of MU-MIMO systems, (ii) all the maximal extreme points in the capacity region of MU-MIMO system, and (iii) the whole capacity region of two-user MIMO systems.

Basic safety message(BSM) or heartbeat message is periodically broadcast by each vehicle on the road, conveying vehicle's instantaneous position and driving state. The vehicular communication network enables the fast exchange of BSMs with neighboring vehicles which assists drivers in acquiring the local driving environment and maneuvering promptly in case of any potential hazard. Such message is exceptionally crucial for vehicles at road intersection to avoid collisions. In this paper, we propose a novel BSM broadcast scheme using random linear network coding (NCB-RI) to address the message dissemination at road intersection. Considering each BSM packet broadcast occupies one broadcast slot, our objective is to reduce the required number of broadcast slots and lower the total transmission time for exchanging all BSMs among the studied vehicles. The coding overhead caused by the random linear network coding is further deducted by using coefficient seeds. The upper bounds regarding the broadcast slots for both NCB-RI and traditional broadcast scheme without network coding are discussed. Our proposed NCB-RI outperforms the traditional solution which is verified by analysis and simulations.

Recently, the generalisation of orthogonal frequency division multiplexing with index modulation (OFDM‐IM) was proposed in the authors’ previous works. Two generalisation schemes of OFDM‐IM, named as OFDM with type‐1 generalised index modulation (OFDM‐GIM1) and OFDM‐GIM2, have been proposed and investigated. In OFDM‐GIM1, the number of active subcarriers in an OFDM subblock is no longer fixed. In OFDM‐GIM2, independent index modulation is performed on the in‐phase and quadrature component of quadrature phase shift keying symbol of each subcarrier. Through such ways, a higher spectral efficiency than that of OFDM‐IM may be achieved. In this study, the authors focus on further improving OFDM‐GIM2‱s spectral efficiency by introducing joint I / Q index modulation where the two index modulations on the in‐phase and quadrature components are no longer entirely independent. In other words, in the proposed scheme, the input bit string jointly decides the indices of active subcarriers for in‐phase and quadrature components. As a result, more bits can be transmitted per OFDM frame. Computer simulation results clearly show the proposed scheme's superiority in both spectral efficiency and bit error rate performance compared with the existing OFDM‐GIM2.

The authors study the performance of protograph low‐density parity‐check (LDPC) codes over two‐dimensional (2D) intersymbol interference (ISI) channels in this study. To begin with, the authors propose a modified version of finite‐length (FL) extrinsic information transfer (EXIT) algorithm so as to facilitate the convergence analysis of protograph codes. Exploiting the FL‐EXIT analyses, the authors observe that the protograph codes optimised for 1D ISI channels, e.g. the 1D‐ISI protograph code, cannot maintain their advantages in the 2D‐ISI scenarios. To address this problem, the authors develop a simple design scheme for constructing a family of rate‐compatible improved protograph (RCIP) codes particularly for 2D‐ISI channels, which not only outperform the 1D‐ISI protograph code, but also are superior to the regular column‐weight‐3 code and optimised irregular LDPC codes in terms of the convergence speed and error performance. More importantly, such RCIP codes benefit from relatively lower error‐floor as well as linear encoding and fast decoding. Thanks to these advantages, the proposed RCIP codes stand out as better alternatives in comparison with other error‐correction codes for ultra‐high‐density data storage systems.

A pair of sequences is called a periodic complementary pair (PCP) if the periodic autocorrelations of the constituent sequences sum up to zero for all nonzero time shifts. Owing to the scarcity of PCPs, we investigate optimal binary periodic almost-complementary pairs (BP-ACPs), each displaying correlation property closest to that of PCP. We show that an optimal BP-ACP of even length N has zero out-of-phase periodic autocorrelation sums (PACSs) except at the time shift of N/2, where the corresponding PACS has minimum magnitude of 4. We also show that for any arbitrary odd N, all the out-of-phase PACSs of an optimal BP-ACP should have identical magnitude of 2. A number of optimal BP-ACPs from analytical constructions as well as computer search are presented. In addition, our proposed optimal BP-ACPs for the even-length case lead to two new families of base-two almost difference families.

In this letter, we provide a new framework for semiblind interference alignment (SBIA) in multi-input-single-output broadcast channels (MISO-BC) where the transmitter receives channel feedback from only one receiver (referred to as supernode). The supernode is called so because it has reconfigurable antennas as well as a feedback mechanism to the transmitters. All other receivers neither have reconfigurable antennas nor provide any kind of feedback to the transmitter. We provide a linear precoding scheme using interference alignment (IA) for this new framework for some classes of MISO-BC, and show that the degrees of freedom (DoF) gain is higher than the traditional orthogonal transmission schemes such as time-division-multiple-access (TDMA). Our proposed SBIA can also be applied to the two-user X-channel for which it is DoF-optimal.

The multi-level-cell (MLC) NAND flash channel exhibits nonstationary behavior over increasing program and erase (PE) cycles and data retention time. In this paper, an optimization scheme for adjusting the read (quantized) and write (verify) voltage levels to adapt to the nonstationary flash channel is presented. Using a model-based approach to represent the flash channel, incorporating the programming noise, random telegraph noise (RTN), data retention noise and cell-to-cell interference as major signal degradation components, the write-voltage levels are optimized by minimizing the channel error probability. Moreover, for selecting the quantization levels for the read-voltage to facilitate soft LDPC decoding, an entropy-based function is introduced by which the voltage erasure regions (error dominating regions) are controlled to produce the lowest bit/frame error probability. The proposed write and read voltage optimization schemes not only minimize the error probability throughout the operational lifetime of flash memory, but also improve the decoding convergence speed. Finally, to minimize the number of read-voltage quantization levels while ensuring LDPC decoder convergence, the extrinsic information transfer (EXIT) analysis is performed over the MLC flash channel.

A pair of sequences is said to be an almost-complementary pair (ACP) if they have zero aperiodic autocorrelation sums except at only one position over all the positive/negative time-shifts. Having correlation property very close to that of Golay complementary pairs (GCPs), ACPs may be used as an alternative to GCPs in many applications in communications and radar. For high-rate code-keying OFDM communication, we construct novel 16-QAM ACPs from three new classes of quadratic offsets, leading to three large sets of 16-QAM almost-complementary sequences with maximum peak-to-mean envelope power ratio (PMEPR) of 2.4.

In this paper, we consider the wireless broadcasting scenario with a source node sending some common information to a group of closely located users, where each link is subject to certain packet erasures. To ensure reliable information reception by all users, the conventional approach generally requires repeated transmission by the source until all the users are able to decode the information, which is inefficient in many practical scenarios. In this paper, by exploiting the close proximity among the users, we propose a novel two-phase wireless broadcasting protocol with user cooperations based on an efficient batched network code, known as batched sparse (BATS) code. In the first phase, the information packets are encoded into batches with BATS encoder and sequentially broadcasted by the source node until certain terminating criterion is met. In the second phase, the users cooperate with each other by exchanging the network-coded information via peer-to-peer (P2P) communications based on their respective received packets. A fully distributed and light-weight scheduling algorithm is proposed to improve the efficiency of the P2P communication in the second phase. The performance of the proposed two-phase protocol is analyzed and the channel rank distribution at the instance of decoding is derived, based on which the optimal BATS code is designed. Simulation results show that the proposed protocol outperforms most existing cooperative packet exchange schemes, especially when the inter-user links are not reliable. Lastly, the performance of the proposed scheme is further verified via testbed experiments.

The benefits of transmit antenna selection (TAS) invoked for spatial modulation (SM) aided multiple-input multiple-output (MIMO) systems are investigated. Specifically, we commence with a brief review of the existing TAS algorithms and focus on the recently proposed Euclidean distance-based TAS (ED-TAS) schemes due to their high diversity gain. Then, a pair of novel ED-TAS algorithms, termed as the improved QR decomposition (QRD)-based TAS (QRD-TAS) and the error-vector magnitude-based TAS (EVM-TAS) are proposed, which exhibit an attractive system performance at low complexity. Moreover, the proposed ED-TAS algorithms are amalgamated with the low-complexity yet efficient power allocation (PA) technique, termed as TAS-PA, for the sake of further improving the system's performance. Our simulation results show that the proposed TAS-PA algorithms achieve signal-to-noise ratio (SNR) gains of up to 9 dB over the conventional TAS algorithms and up to 6 dB over the TAS-PA algorithm designed for spatial multiplexing systems.

Cell-to-cell interference (CCI), arising from parasitic coupling-capacitance between adjacent cells, is a major factor for the degradation of cell threshold voltage in today's flash memory chips. In this paper, three novel postprocessing detection schemes that exploit the a priori information of neighboring/interfering cells for mitigating the CCI effect in multilevel cell NAND flash memory are presented. The proposed schemes are referred to as the Even-A-Priori (Even-AP), the All-A-Priori (All-AP), and the All-AP-coupling-capacitance ratio (CCR) detectors. The main idea is to remove the CCI component from the interfering cells before CCI cancellation from the victim cell. Specifically, the mean CCRs along the victim cell's vertical and diagonal directions are estimated to enable more accurate CCI cancellation. Performance analysis and simulation results show that the channel signal-to-noise ratio performance can be improved by up to 2 dB at a cell storage capacity of 1.8 bits/cell, which is significantly improved compared with some prior-art detection schemes.

This paper considers a low-complexity Gaussian message passing iterative detection (GMPID) algorithm for a massive multiuser multiple-input multiple-output (MU-MIMO) system, in which a base station with$M$antennas serves$K$Gaussian sources simultaneously. Both$K$and$M$are very large numbers, and we consider the cases that$K<M$. The GMPID is a message passing algorithm operating on a fully connected loopy graph, which is well understood to be non-convergent in some cases. As it is hard to analyze the GMPID directly, the large-scale property of the massive MU-MIMO is used to simplify the analysis. First, we prove that the variances of the GMPID definitely converge to the mean square error of minimum mean square error (mmse) detection. Second, we derive two sufficient conditions that make the means of the GMPID converge to those of the mmse detection. However, the means of GMPID may not converge when$ K/M\geq (\sqrt {2}-1)^{2}$. Therefore, a modified GMPID called scale-and-add GMPID, which converges to the mmse detection in mean and variance for any$K

In this paper, we investigate a pair of transmit precoding (TPC) algorithms conceived for spatial modulation (SM) systems communicating over flat-fading multiple-input multiple-output (MIMO) channels. In order to retain all the benefits of conventional SM, we design the TPC matrix to be diagonal and introduce two design criteria for optimizing the elements of the TPC matrix. Specifically, we first investigate a TPC design based on maximizing the minimum Euclidean distance dmin(max-dmin) between the SM signal points at the receiver side. A closed-form solution of the optimal max-dmin-based TPC matrix is derived. Then, another TPC design algorithm is proposed for directly minimizing the bit error ratio (BER) upper bound of SM, which is capable of jointly optimizing the overall Euclidean distance between all received signal points. In the minimum BER (min-BER)-based TPC algorithm, the theoretical gradient of the BER with respect to the diagonal TPC matrix is derived and a simplified iterative conjugate gradient (SCG) algorithm is invoked for TPC optimization. Our simulation results demonstrate that the proposed max-dmin-based TPC algorithm is optimal in terms of the minimum distance. However, increasing dmindoes not achieve a further BER improvement. We also confirm that the min-BER-based TPC outperforms the max-dmin-based TPC schemes in terms of the achievable BER performance.

In order to reduce the number of retransmissions and save power for the source node, we propose a two-phase coded scheme to achieve reliable broadcast from the source to a group of users with minimal source transmissions. In the first phase, the information packets are encoded with batched sparse (BATS) code, which are then broadcasted by the source node until the file can be cooperatively decoded by the user group. In the second phase, each user broadcasts the re-encoded packets to its peers based on their respective received packets from the first phase, so that the file can be decoded by each individual user. The performance of the proposed scheme is analyzed and the rank distribution at the moment of decoding is derived, which is used as input for designing the optimal BATS code. Simulation results show that the proposed scheme can reduce the total number of retransmissions compared with the traditional single-phase broadcast with optimal erasure codes. Furthermore, since a large number of transmissions are shifted from the source node to the users, power consumptions at the source node is significantly reduced.

A sequence is said to be spectrally-constrained if it has to satisfy a spectral map consisting of several non-contiguous nulled frequency-slots. Such sequences play a key role in emerging spectrally-constrained systems such as cognitive radio and cognitive radar. In this paper, we study two types of spectrally-constrained sequences (SCSs), one with low periodic auto-correlation function (PACF) sidelobe, the other with zero auto-correlation zone (ZACZ). By deriving a correlation lower bound, we show that Type-I SCSs are optimal with minimum total PACF sidelobe energy provided that uniform power allocation is applied to all active (non-nulled) frequency-slots. We also propose optimal Type-II SCSs, each having maximum ZACZ width, for certain spectral map patterns.

In this paper, we investigate the performance of the protograph low-density parity-check (LDPC) codes concatenated with space-time block code (STBC) over Rayleigh fading channels. We firstly extend the modified protograph extrinsic information transfer (PEXIT) algorithm in order to analyze the convergence performance of protograph codes. Based on the extended PEXIT algorithm and Gaussian approximation, we further derive the asymptotic bit-error-rate (BER) expression of protograph codes. Utilizing the PEXIT algorithm, theoretical and simulated BERs, we compare the performance of two classical protograph codes, i.e., accumulate-repeat-by-3-accumulate (AR3A) code and accumulate-repeat-by-4-jagged-accumulate (AR4JA) code, regular LDPC code, and optimized irregular LDPC codes, and illustrate that the AR3A code is superior to other three codes. Additionally, we discuss the impact of the number of receive antennas (i.e., NR) on the system performance and verify that the theoretical analyses hold as NR varies. As a result, the AR3A code stands out as a good candidate for wireless communication applications with multiple antennas.

Transform domain communication system (TDCS) is a multi- carrier cognitive radio (CR) technique which uses cyclic code shift keying (CCSK) for overlay opportunistic spectrum access. Specifically, at any given time, a TDCS system cyclically shifts a fundamental modulation waveform (i.e., a CR sequence satisfying a dynamic spectrum hole constraint) according to specific input data symbol. In practical TDCS system, an interesting research problem is how to design CR sequence with inter-carrier interference (ICI) resilience, where ICI is caused by carrier frequency offset (CFO) or Doppler spread. In this paper, we present a novel family of CR sequences which are able to achieve ICI self-cancellation in TDCS. Analysis and simulations validate that our proposed CR sequence is effective for ICI suppression in TDCS. A future work of this research is to optimize the peak-to-mean power ratio of such CR sequences.

We study RF-enabled wireless energy transfer (WET) via energy beamforming, from a multi-antenna energy transmitter (ET) to multiple energy receivers (ERs) in a backscatter communication system such as RFID. The acquisition of the forward-channel (i.e., ET-to-ER) state information (F-CSI) at the ET (or RFID reader) is challenging, since the ERs (or RFID tags) are typically too energy-and-hardware-constrained to estimate or feedback the F-CSI. The ET leverages its observed backscatter signals to estimate the backscatter-channel (i.e., ET-to-ER-to-ET) state information (BS-CSI) directly. We first analyze the harvested energy obtained using the estimated BS-CSI. Furthermore, we optimize the resource allocation to maximize the total utility of harvested energy. For WET to single ER, we obtain the optimal channel-training energy in a semiclosed form. For WET to multiple ERs, we optimize the channel-training energy and the energy allocation weights for different energy beams. For the straightforward weighted-sum-energy (WSE) maximization, the optimal WET scheme is shown to use only one energy beam, which leads to unfairness among ERs and motivates us to consider the complicated proportional-fair-energy (PFE) maximization. For PFE maximization, we show that it is a biconvex problem, and propose a block-coordinate-descent-based algorithm to find the close-to-optimal solution. Numerical results show that with the optimized solutions, the harvested energy suffers slight reduction of less than 10%, compared to that obtained using the perfect F-CSI.

This paper studies a wireless-energy-transfer (WET) enabled massive multiple-input-multiple-output (MIMO) system (MM) consisting of a hybrid data-and-energy access point (H-AP) and multiple single-antenna users. In the WET-MM system, the H-AP is equipped with a large number M of antennas and functions like a conventional AP in receiving data from users, but additionally supplies wireless power to the users. We consider frame-based transmissions. Each frame is divided into three phases: the uplink channel estimation (CE) phase, the downlink WET phase, as well as the uplink wireless information transmission (WIT) phase. Firstly, users use a fraction of the previously harvested energy to send pilots, while the H-AP estimates the uplink channels and obtains the downlink channels by exploiting channel reciprocity. Next, the H-AP utilizes the channel estimates just obtained to transfer wireless energy to all users in the downlink via energy beamforming. Finally, the users use a portion of the harvested energy to send data to the H-AP simultaneously in the uplink (reserving some harvested energy for sending pilots in the next frame) . To optimize the throughput and ensure rate fairness, we consider the problem of maximizing the minimum rate among all users. In the large-M regime, we obtain the asymptotically optimal solutions and some interesting insights for the optimal design of WET-MM system.

Recently, a transmission strategy was designed for the K-user interference channel (IC) without CSI at the transmitters (CSIT) but aided by a relay which knows the global CSI, where it was shown that K - 1 relay antennas are required to attain the maximum network DoF. In this paper, we derive general IA feasibility conditions for a single relay-aided IC with CSI at both the relay and the transmitters. Our result shows that except for the special case of K = 3 users, providing CSI to the transmitters still requires K - 1 relay antennas to achieve the maximum network DoF. Next, we design the minimum-sum-mean-squareerror (MSMSE) and weighted-sum-rate (WSR) maximizing precoders for the relay-aided IC network without CSIT which show higher sum-rate in the finite-SNR region compared to the IA precoding. Further, a so-called scaled-IA initialization is proposed for the MSMSE and the WSR algorithms which not only preserves the network DoF but also saves the power at the relay considerably compared to the MSMSE and the WSR schemes with random initializations. Extensive simulation results in both symmetric and asymmetric networks show that the MSMSE precoder shows better BER performance than both the IA and the WSR algorithms for low-to-moderate data rates.

Recently, orthogonal frequency division multiplexing (OFDM) with index modulation (OFDM-IM) was proposed. By selecting a fixed number of subcarriers as active subcarriers to carry constellation symbols, the indices of these active subcarriers may carry additional bits of information. In this paper, we propose two generalization schemes of OFDM-IM, named OFDM with generalized index modulation 1 (OFDM-GIM1) and OFDM-GIM2, respectively. In OFDM-GIM1, the number of active subcarriers in an OFDM subblock is no longer fixed. Dependent on the input binary string, different numbers of active subcarriers are assigned to carry constellation symbols. In OFDM-GIM2, independent index modulation is performed on the in-phase and quadrature component per subcarrier. Through such ways, a higher spectral efficiency than that of OFDM-IM may be achieved. Since both generalization schemes proposed suffer from BER performance loss in low SNR region, an interleaving technique is proposed to tackle this problem. Finally, noting that the two generalization schemes are compatible with each other, the combination of these two schemes, named OFDM-GIM3, has also been investigated. Computer simulation results clearly show our proposed scheme's superiority in both spectral efficiency and BER performance compared to existing works.

We investigate the performance of the protograph low-density parity-check (LDPC) codes over Nakagami block-fading (BF) channels with multiple receive antennas. Using the modified protograph extrinsic information transfer (PEXIT) algorithm, we observe that the existing protograph LDPC codes, which have been shown to possess excellent error performance over additive white Gaussian noise channels, cannot perform well over non-ergodic BF channels. To address this problem, a simple design scheme is proposed to construct three new root-protograph (RP) LDPC codes, i.e., the regular RP code, the improved RP1 (IRP1) code, and the IRP2 code. The convergence analysis, error-performance analysis, and simulated results show that the three proposed RP codes outperform the existing protograph LDPC codes and regular quasi-cyclic LDPC code. Furthermore, the IRP1 code and the IRP2 code are superior to the regular RP code and the regular root-LDPC code and exhibit outage-limit-approaching error performance. Consequently, these two proposed IRP codes appear to be better alternatives as compared to other error-correction codes for slowly-varying wireless communication systems.

Complete complementary codes (CCC) refer to a set of two-dimensional matrices, which have zero non-trivial aperiodic auto- and cross- correlation sums. A modern application of CCC is in interference-free multicarrier code-division multiple-access (MC-CDMA) communications. In this paper, we first show that in asynchronous “fractional-delay” uplink channels, CCC-MC-CDMA systems suffer from orthogonality loss, which may lead to huge interference increase when a conventional correlator based receiver is deployed. Then, by exploiting the correlation properties of CCC, we present a fractional-delay-resilient receiver which is comprised of a chip-spaced correlating array. Analysis and simulations validate the interference-free achievability of the proposed CSCA receiver in strong interference scenarios.

Recently, an Orthogonal Frequency Division Multiplexing (OFDM) with index modulation (OFDM-IM) [1] was proposed. By selecting a fixed number of subcarriers as active subcarriers to carry constellation symbols, the indices of these active subcarriers may carry additional bits of information. In this paper, a generalization of OFDM-IM, named as OFDM with generalized index modulation (OFDM-GIM) is proposed. In this scheme, the number of active subcarriers in an OFDM subblock of the overall structure is no longer fixed. Dependent on the input binary string, different number of active subcarriers are assigned to carry constellation symbols. In such a way, a higher spectral efficiency than that of OFDM-IM may be achieved. To facilitate the implementation of our scheme, a generalized index modulation block for the active subcarrier selection and an upgraded Log-likelihood Ratio (LLR) detector for the active subcarriers and symbol constellation detection have also been proposed. Computer simulation results show that our proposed scheme does offer us a much higher spectral efficiency at the cost of a marginal error performance loss.

We consider a multi-input-single-output (MISO) system where a wireless power transmitter sends wireless power to a receiver via beamforming, taking into account the effects of channel estimation. In this paper, we address the problem of dynamically balancing the amount of time resource used for training, as well as the energy resource used for wireless power transfer, so as to maximize the amount of energy harvested at the receiver. First, the optimal length of preamble is obtained under the case when the length is optimized offline, or dynamically optimized online, depending on the channel estimate. For the latter case, the solution is obtained by solving a dynamic programming (DP) problem, which is shown to be a threshold-type policy that depends only on the channel estimate power. We further derive the optimal dynamic power allocation scheme for DP-based wireless power delivery. The analysis results are validated by numerical simulations. The scheme based on dynamic-length preamble with power allocation is shown to significantly outperform the fixed-length preamble scheme.

In this paper, we present a direct construction of zero-correlation zone (ZCZ) sequence sets (each associated with a graph) from the second order cosets of the first-order generalized Reed-Muller codes. This settles an open problem introduced by Rathinakumar and Chaturvedi in their 2008 paper.

In this paper, for the K-user interference relay channel (IRC) with transmitters and relays having no and global channel state information (CSI), respectively, a minimum sum mean squared error (MSMSE) based relay precoder is proposed. It is shown that MSMSE outperforms interference alignment (IA) relay precoder in sum rate at low to mid signal-to-noise ratio (SNR) regions as well as in bit error rate (BER) at all SNR regions for small and moderate sizes of Gray encoded constellations.

Non‐binary low‐density parity‐check (NB‐LDPC) codes can be directly constructed by using algebraic methods, or indirectly constructed by mapping well‐designed binary parity‐check matrices to non‐binary parity‐check matrices. Given the Tanner graph (TG) of a NB‐LDPC code, the selection of edge weights in the TG significantly affects the performance of the NB‐LDPC code. The authors introduce an edge weight distribution (EWD) parameter for the TG of NB‐LDPC codes. By utilising particle swarm optimisation (PSO), the EWD is optimised and it has been demonstrated that the optimal EWD approaches a two‐element distribution for large field size and high average variable‐node degree. With the optimised EWD, the authors construct a class of field‐compatible LDPC (FC‐LDPC) codes over GF( q ) whose parity‐check matrices only include elements 0, 1 and 2, and can be encoded and decoded over different field sizes. The simulations demonstrate that the performance of the proposed FC‐LDPC codes improves monotonically with increasing field size, and significantly outperforms that of the corresponding algebraic NB‐LDPC codes or NB‐LDPC codes generated with uniform distribution of non‐zero elements over GF( q ).

The authors consider centralised cooperative spectrum sensing under correlated shadowing in this study. Formulating the spectrum sensing problem as a Gauss–Gauss hypothesis test, they use a linear quadratic rule and show that it is the optimal detector under Bayesian criterion. They derive the upper and lower Bhattacharyya bounds and investigate the error performance of spectrum sensing by studying the behaviour of these upper and lower bounds. They also study the asymptotic error performance in two different scenarios of finite and infinite area networks. They show that by increasing the number of nodes the sensing error probability approaches zero in both cases but with different decay rates. The lower the correlation between nodes or the larger the network area, the faster the decay.

Transform domain communication system (TDCS) has been proposed to establish a communication link with a low probability of interception by synthesising an adaptive waveform containing energy only in the unused frequency bands. However, this communication system suffers from a low spectral efficiency. To improve its spectral efficiency, a unified modulation framework for a TDCS is proposed in this study to embrace the previously reported modulation schemes under one framework. The resulting spectral efficiency is higher compared to these previous schemes. Also, to combat the slow channel fading, a TDCS system using two transmit antennas and one receive antenna with the proposed modulation scheme is presented. Simulation results show that the multiple‐antenna system improves the performance dramatically compared to the single antenna system under the slow fading environment.

To support interference-free quasi-synchronous code-division multiple-access (QS-CDMA) communication with low spectral density profile in a cognitive radio (CR) network, it is desirable to design a set of CDMA spreading sequences with zero-correlation zone (ZCZ) property. However, traditional ZCZ sequences (which assume the availability of the entire spectral band) cannot be used because their orthogonality will be destroyed by the spectrum hole constraint in a CR channel. To date, analytical construction of ZCZ CR sequences remains open. Taking advantage of the Kronecker sequence property, a novel family of sequences (called "quasi-ZCZ" CR sequences) which displays zero cross-correlation and near-zero auto-correlation zone property under arbitrary spectrum hole constraint is presented in this paper. Furthermore, a novel algorithm is proposed to jointly optimize the peak-to-average power ratio (PAPR) and the periodic auto-correlations of the proposed quasi-ZCZ CR sequences. Simulations show that they give rise to single-user bit-error-rate performance in CR-CDMA systems which outperform traditional non-contiguous multicarrier CDMA and transform domain communication systems; they also lead to CR-CDMA systems which are more resilient than non-contiguous OFDM systems to spectrum sensing mismatch, due to the wideband spreading.

For an even-period binary Z-complementary pair (EB-ZCP), if it is not a Golay complementary pair (GCP), we show that Z ≤ N-2, where N and Z denote the sequence length and the zero correlation zone (ZCZ) width, respectively. This result partially answers the Fan-Yuan-Tu conjecture in 2007. In addition, we present a construction of EB-ZCPs with large ZCZ widths, where N=2m+1+2mand Z=2m+1. Interestingly, each of the proposed EB-ZCPs features zero out-of-phase aperiodic auto-correlation sums except for the time-shift of ±2m+1, thus displaying a very close correlation property to that of GCPs.

Owing to the zero non-trivial aperiodic correlation sum properties, complete complementary codes (CCC) have been applied to asynchronous multi-carrier code-division multiple-access (MC-CDMA) communications in order to provide zero interference performance. When each complementary code is arranged to be a matrix, the peak-to-mean envelope power ratio (PMEPR) of the CCC-MC-CDMA system is determined by the column sequences of the complementary matrices. The existing CCC have the column sequence PMEPR of M, where M denotes the number of subcarriers in a CCC-MC-CDMA system. In practice, M is generally large and a PMEPR approaching this value is unacceptable. To solve this problem, a new class of CCC using generalized Boolean functions and with a column sequence PMEPR of at most 2 is proposed in this paper.

In this paper, we present an achievable rate region for double-unicast networks by assuming that the intermediate nodes perform random linear network coding, and the source and sink nodes optimize their strategies to maximize the achievable region. Such a setup can be modeled as a deterministic interference channel, whose capacity region is known. For the particular class of linear deterministic interference channels of our interest, in which the outputs and interference are linear deterministic functions of the inputs, we show that the known capacity region can be achieved by linear strategies. As a result, for a given set of network coding coefficients chosen by the intermediate nodes, the proposed linear precoding and decoding for the source and sink nodes will give the maximum achievable rate region for double-unicast networks. We further derive a suboptimal but easy-to-compute rate region that is independent of the network coding coefficients used at the intermediate nodes, and is instead specified by the min-cuts of the network. It is found that even this suboptimal region is strictly larger than the existing achievable rate regions in the literature.

Drill string acoustic telemetry is an effective transmission method to retrieve downhole data. Finite-difference simulations produce the comb-filter-like channel response (patterns of pass bands and stop bands) due to the presence of coupling joints in the metallic drill string. Practical pipes used for drilling deep wells have slight variation in length. The selection and arrangement of downhole pipes is important for improving the transmission efficiency of extensional waves transmitted through the drill string. Downhole drill string channel is studied using the transmission coefficients calculated from the transmission matrix method, and the resultant transfer function produces identical results similar to the finite-difference simulations. Reciprocity of the drill string structure is proved by comparing the pass band responses using the ascend-only (AO) and descend-only pipe arrangements. Transferred energies calculated up to 180 pipes of random length at the end of the drill strings using transmission coefficients for the three different pipe arrangements, namely, AO, descend-then-ascend, and ascend-then-descend (ATD), are compared to find the optimal pipe arrangement for single measurement. For the situations when pipes are distributed in sets, multiple measurements are required. In this paper, two sets of AO and two sets of ATD arrangements are analyzed for multiple measurements. ATD and$n$xATD arrangements are proposed as optimal pipe arrangements to produce the best possible telemetry performance in terms of optimal acoustic energy transfer via one- and two-way acoustic communication for single and multiple measurements, respectively.

Levenshtein improved the famous Welch bound on aperiodic correlation for binary sequences by utilizing some properties of the weighted mean square aperiodic correlation. Following Levenshtein's idea, a new correlation lower bound for quasi-complementary sequence sets (QCSSs) over the complex roots of unity is proposed in this paper. The derived lower bound is shown to be tighter than the Welch bound for QCSSs when the set size is greater than some value. The conditions for meeting the new bound with equality are also investigated.

A pair of sequences is called a Golay complementary pair (GCP) if their aperiodic autocorrelation sums are zero for all out-of-phase time shifts. Existing known binary GCPs only have even-lengths in the form of 2α10β26γ(where \(α, β, γ) are nonnegative integers). To fill the gap left by the odd-lengths, we investigate the optimal odd-length binary (OB) pairs, which display the closest correlation property to that of GCPs. Our criteria of closeness is that each pair has the maximum possible zero-correlation zone (ZCZ) width and minimum possible out-of-zone aperiodic autocorrelation sums. Such optimal pairs are called optimal OB Z-complementary pairs (OB-ZCP) in this paper. We show that each optimal OB-ZCP has maximum ZCZ width of (N+1)/2, and minimum out-of-zone aperiodic sum magnitude of 2, where N denotes the sequence length (odd). Systematic constructions of such optimal OP-ZCPs are proposed by insertion and deletion of certain binary GCPs, which settle the 2011 Li-Fan-Tang-Tu open problem positively. The proposed optimal OB-ZCPs may serve as a replacement for GCPs in many engineering applications, where odd sequence lengths are preferred. In addition, they give rise to a new family of base-two almost difference families, which are useful in studying partially balanced incomplete block design.

The Levenshtein bound on aperiodic correlation, which is a function of the weight vector, is tighter than the Welch bound for sequence sets over the complex roots of unity when$M\geq 4$and$n\geq 2$, where$M$denotes the set size and$n$the sequence length. Although it is known that the tightest Levenshtein bound is equal to the Welch bound for$M\in\{1,2\}$, it is unknown whether the Levenshtein bound can be tightened for$M=3$, and Levenshtein, in his paper published in 1999, postulated that the answer may be negative. A new weight vector is proposed in this paper, which leads to a tighter Levenshtein bound for$M=3$,$n\geq 3$and$M\geq 4$,$n\geq 2$. In addition, the explicit form of the weight vector (which is derived by relating the quadratic minimization to the Chebyshev polynomials of the second kind) in Levenshtein's paper is given. Interestingly, this weight vector also yields a tighter Levenshtein bound for$M=3$,$n\geq 3$and$M\geq 4$,$n\geq\sqrt{M}$, a fact not noticed by Levenshtein.

We provide the degrees of freedom (DoF) characterization for the three-user MT× MRmultiple-input-multipleoutput (MIMO) interference channel (IC) with rank-deficient channel matrices, where each transmitter is equipped with MTantennas and each receiver with MRantennas, and the interfering channel matrices from each transmitter to the other two receivers are of ranks D1and D2, respectively. One important intermediate step for both the converse and achievability arguments is to convert the fully-connected rank-deficient channel into an equivalent partially-connected full-rank MIMO-IC by invertible linear transformations. As such, existing techniques developed for full-rank MIMO-IC can be incorporated to derive the DoF outer and inner bounds for the rank-deficient case. Our result shows that when the interfering links are weak in terms of the channel ranks, i.e., D1+ D2≤ min(MT, MR), zero forcing is sufficient to achieve the optimal DoF. On the other hand, when D1+ D2> min(MT, MR), a combination of zero forcing and interference alignment is in general required for DoF optimality. The DoF characterization obtained in this paper unifies several existing results in the literature.

An algorithm based on the fitting of modified Extrinsic Information Transfer (EXIT) chart is proposed to calculate the thresholds of low-density parity-check (LDPC) codes optimized for the two-dimensional (2D) intersymbol interference (ISI) channels encountered in high-density magnetic recording, such as bit-patterned magnetic recording (BPMR) and two-dimensional magnetic recording (TDMR). The modified EXIT chart algorithm determines the LDPC threshold by fitting the EXIT curve of the variable node decoder (VND) combined with 2D detector (2D-DET) to the EXIT curve of the check node decoder (CND). Both the optimal Bahl-Cocke-Jelinek-Raviv (BCJR) 2D-DET and reduced-complexity Gaussian-approximated iterative row-column 2D-DET are considered.

This paper studies the achievable rate region of the K-user Gaussian multiple-input single-output interference channel (MISO-IC) with interference treated as noise, when improper or circularly asymmetric complex Gaussian signaling is applied. By exploiting the separable rate expression with improper Gaussian signaling, we propose a separate covariance and pseudo-covariance optimization algorithm, which is guaranteed to improve the users' rates over the conventional proper or circularly symmetric complex Gaussian signaling. In particular, for the pseudo-covariance optimization, the semidefinite relaxation (SDR) technique is applied to provide a high-quality approximate solution. For the special case of two-user MISO-IC, the SDR technique yields the optimal pseudo-covariance solution.

We study the achievable degrees of freedom (DoF) of the 3-user multiple-input multiple-output (MIMO) interference channel (IC), with possibly rank-deficient channel matrices. A two-layered linear processing scheme is proposed, with the inner layer zero-forcing part of the interfering links by applying a technique known as change of basis, and the outer layer performing interference alignment for the remaining interfering links. The achievable DoF of the proposed scheme is derived. In many special cases of the problem considered in this paper, our proposed two-layered scheme is DoF optimal.

We consider the scenario in which multiple sensors send spatially correlated data to the fusion center (FC) via independent Rayleigh-fading channels with additive noise. Assuming that the sensor data is sparse in some basis, we show that the recovery of the signal can be formulated as a compressive sensing (CS) problem. To model the scenario where sensors operate with intermittently available energy that is harvested from the environment, we propose that each sensor transmits independently with some probability, and adapts the transmit power to its harvested energy. Due to the probabilistic transmissions, the elements of the equivalent sensing matrix are not Gaussian. Besides, since the sensors have different energy harvesting rates and different sensor-to-FC distances, the FC has different receive signal-to-noise ratios (SNRs) for each sensor, referred to as the inhomogeneity of SNRs. Thus, the elements of the sensing matrix are also not identically distributed. We provide theoretical guarantees on the number of measurements for reliable reconstruction, by showing that the sensing matrix satisfies the restricted isometry property (RIP), under some mild conditions. We then compute an achievable system delay under an allowable mean-squared-error (MSE). Furthermore, using techniques from large deviations theory, we analyze the impact of inhomogeneity of the SNRs on the so-called k-restricted eigenvalues, which governs the number of measurements required for the RIP to hold. Our analysis is corroborated by numerical results.

This paper studies the transmit optimization for the K-user Gaussian single-input single-output interference channel (SISO-IC), with the interference treated as Gaussian noise and by applying improper or circularly asymmetric complex Gaussian signaling. The transmit optimization with improper Gaussian signaling involves not only the signal covariance as in the conventional proper or circularly symmetric complex Gaussian signaling, but also the signal pseudo-covariance, which is conventionally set to zero in proper Gaussian signaling. By utilizing the rate-profile method, the achievable rate region of the K-user SISO-IC is characterized by solving a sequence of minimum-weighted-rate maximization (MinWR-Max) problems, which are non-convex and thus difficult to be solved globally optimally. By applying the semidefinite relaxation (SDR) technique, we propose an efficient approximate solution, which jointly optimizes the covariance and pseudo-covariance of the transmitted signals. Simulation results demonstrate the effectiveness of the proposed algorithm for the K-user SISO-IC with improper Gaussian signaling.

In this work, we propose two decision techniques that interchange between MIMO and diversity schemes for improving shadowing and alignment problems. In a visible-light communication (VLC) system, the transmission nature can be of two types: (1) all LEDs transmit the same signal stream simultaneously; or (2) each LED transmits different parts of a signal stream independently. The MIMO-diversity technique detects and computes the transmitted signal power during reception and conditionally informs the transmitter to switch between Type (1) and Type (2) transmission. Two experimental models have been tested to show the feasibility of such a technique. In the first model, we constructed a full transceiver and uses a switch IC to switch between MIMO and diversity. The second model uses a microcontroller and software decision to switch between two COM ports, each of them dedicated to MIMO and diversity output respectively. Results suggest that shadowing and alignment problems commonly encountered in visible-light communication systems can be readily solved using these methods. A focusing equation has also been formulated to predict signal intensity more accurately. The focusing gap between the concentrator and the photodiode is taken into consideration during channel computation.

In this paper we introduce the optimal odd-period binary Z-complementary pairs (OB-ZCPs), which display properties similar to Golay complementary pairs. These pairs have the maximum possible zero-correlation-zone (ZCZ) of width (N + 1)/2, where N denotes the sequence length, and the minimum possible magnitude of 2 for each out-of-zone aperiodic auto-correlation sum. Furthermore, we show that the optimal OB-ZCPs correspond to sets of almost difference families and present some of their interesting properties.

The Levenshtein bound, as a function of the weight vector, is only known to be tighter than the Welch bound on aperiodic correlation for K ≥ 4, N ≥ 2, where K and N denoting the set size and the sequence length, respectively. A quadratic weight vector is proposed in this paper which leads to a tighter Levenshtein bound for K ≥ 4, N ≥ 2 and K = 3, N ≥ 4. The latter case was left open by Levensthein.

This paper presents a lower bound on the mutual information between any two sets of source/edge random variables in a general multi-source multi-sink network. This bound is useful to derive a new class of better information-theoretic upper bounds on the network coding capacity given existing edge-cut based bounds. A refined functional dependence bound is characterized from the functional dependence bound using the lower bound. It is demonstrated that the refined versions of the existing edge-cut based outer bounds obtained using the mutual information lower bound are stronger.

Transform domain communication system (TDCS), as an overlay cognitive radio communication system, has been proposed to obtain a low probability of interception by using spectrum bin nulling to synthesise an adaptive waveform corresponding to the spectrum sensing output. However, its low spectral efficiency limits potential practical applications. In this study, an efficient modulation scheme, namely quadrature cyclic code shift keying (Q‐CCSK), is proposed for TDCSs by generating another fundamental modulation waveform as the data‐bearing quadrature branch. Although using Q‐CCSK doubles the spectral efficiency for TDCSs, the instinct inter‐branch interference arises from the added quadrature branch. Through the orthogonality analysis, it is proven that the two branches (in‐phase and quadrature‐ branches) are still satisfied the property of quasi‐orthogonality. Moreover, the performances of TDCSs employing Q‐CCSK in additive white Gaussian noise (AWGN) and multipath fading channels are discussed, respectively. Analytical and simulation results demonstrate that, compared with conventional schemes, the TDCS employing Q‐CCSK can double the spectral efficiency with comparable systematic performance.

In this paper, beamforming design is considered for a K-user multiple-input-multiple-output interference channel (MIMO-IC) under limited channel feedback budget at the users. Using a geometrical framework, an analytical expression for the upper bound on the total rate loss due to limited channel feedback is derived. The rate loss is a function of the total feedback budget available to the receiver, the strength of the desired and the interfering links at the users and the network parameters. This rate loss upper bound metric is then minimized at each receiver locally by allocating the feedback bits optimally among the links observed by it. We obtain an interesting result that under limited channel feedback budget at the users, interference alignment (IA) may not be the appropriate beamforming strategy. Based on the feedback bit allocation policy to minimize the rate loss upper bound at the receiver and the corresponding channel quantization error statistics at the transmitter, an improved beamformer design is proposed. The proposed beamformer shows a higher throughput gain compared to all other existing schemes.

Scheduling strategy is considered an important aspect of belief-propagation (BP) decoding of low-density parity-check (LDPC) codes because it affects the decoder's convergence rate, decoding complexity and error-correction performance. In this paper, we propose two new scheduling strategies for the BP decoding of non-binary LDPC (NB-LDPC) codes. Both the strategies are devised based on the concept of check node reliability and employ a heuristically defined threshold which can adapt to the communication channel variations. As the scheduling strategies only update a subset of the check nodes in each iteration, they result in reduced iteration cost. Furthermore, since the BP performs suboptimally for finite-length LDPC codes, especially for short-length LDPC codes, by enhancing the message propagation over the Tanner Graphs of short-length NB-LDPC codes, the new scheduling strategies can even improve the error-correction performances of BP decoding. Simulation results demonstrate that the new scheduling strategies provide good performance/complexity tradeoffs.

For overlay cognitive radio networks (CRNs), transform domain communication system (TDCS) has been proposed to support multiuser communications through spectrum bin nulling and frequency domain spreading. In TDCS-based CRNs, each user is assigned a specific pseudorandom spreading sequence. However, the existence of multiuser interference (MUI) is one of main concerns, due to the non-zero cross-correlations between any pair of TDCS signals. In this paper, a novel framework of TDCS-based CRNs with the joint design of sequences and modulation schemes is presented to realize MUI avoidance. With the uncertainty of spectrum sensing results in CRNs, we first introduce a unique sequence design through two-dimensional time-frequency synthesis and obtain a class of almost perfect sequences whose periodic auto-correlation and cross-correlations are identically zero for most circular shifts. These correlation properties are further exploited in conjunction with a specially-designed cyclic code shift keying in order to achieve the advantage of MUI avoidance. Numerical results demonstrate that the proposed TDCS-based CRNs are well suited for decentralized networks against the near-far problem.

There have been five constructions (Cases I to V) of 64-QAM Golay complementary sequences (GCSs), of which the Cases IV and V constructions were identified by Chang in 2010. The Generalized Cases I-III constructions for 4q-QAM (q ≥ 1) GCSs were additionally proposed by Li. In this paper, the Generalized Case IV and Generalized Case V constructions for 4q-QAM (q > =3) GCSs are proposed using selected Gaussian integer pairs, each of which contains two distinct Gaussian integers with identical magnitude and which are not conjugate with each other.

This paper studies the achievable rate region of the K-user Gaussian multiple-input single-output interference channel (MISO-IC) with the interference treated as noise, when improper or circularly asymmetric complex Gaussian signaling is applied. The transmit optimization with improper Gaussian signaling involves not only the signal covariance matrix as in the conventional proper or circularly symmetric Gaussian signaling, but also the signal pseudo-covariance matrix, which is conventionally set to zero in proper Gaussian signaling. By exploiting the separable rate expression with improper Gaussian signaling, we propose a separate transmit covariance and pseudo-covariance optimization algorithm, which is guaranteed to improve the users' achievable rates over the conventional proper Gaussian signaling. In particular, for the pseudo-covariance optimization, we establish the optimality of rank-1 pseudo-covariance matrices, given the optimal rank-1 transmit covariance matrices for achieving the Pareto boundary of the rate region. Based on this result, we are able to greatly reduce the number of variables in the pseudo-covariance optimization problem and thereby develop an efficient solution by applying the celebrated semidefinite relaxation (SDR) technique. Finally, we extend the result to the Gaussian MISO broadcast channel (MISO-BC) with improper Gaussian signaling or so-called widely linear transmit precoding.

This paper addresses the application of the emerging compressive sensing (CS) technology to the detection of ultra-wideband (UWB) signals. Capitalizing on the sparseness of random UWB signals in the basis of eigen-functions, we develop a new CS dictionary called eigen- dictionary. Coupled with this eigen-dictionary, an enhanced Bayesian learning procedure is proposed to reconstruct the sparse UWB signal from a small collection of random projection measurements. Furthermore, by utilizing a common sparsity profile inherent in UWB signals, the proposed Bayesian algorithm naturally lends itself to multi-task CS for simultaneously recovering multiple UWB signals. Since the statistical inter-relationships between different CS tasks are exploited, the multi-task (MT) Bayesian CS can efficiently improve the reconstruction accuracy and thus the performance of UWB communications. Simulations based on real UWB data demonstrate the advantages of the proposed approach over its counterparts.

Levenshtein improved the Welch bound on aperiodic correlation by weighting the cyclic shifts of the sequences over complex roots-of-unity. Although many works have been concerned on meeting the Welch bound with equality, no such effort has been reported for the Levenshtein bound. We show that the Levenshtein bound with equality is met if and only if the non-trivial aperiodic correlations have identical amplitude for all time-shifts, and the sequences form a novel class of complementary set whose aperiodic correlation is defined as the conventional aperiodic correlation modulated by a simplex weighting vector.

As a challenged network, the delay-tolerant network (DTN) generally has unpredictable delay for the packet delivery due to the insufficiency of the network connectivity. Thus, it is inadequate to support some time-sensitive applications. In this paper we aim to mitigate this unpredictability of the delay in DTN to broaden its applications by proposing a new routing strategy to make use of the node mobility. Our designed solution is called purpose-movement assisted routing (PMAR) aiming for a more realistic mobility model, group mobility, which has been paid attention in many realistic scenarios. Different from the traditional store-carry-forward DTN routing strategy, in which nodes wait for the connectivity passively, PMAR employs nodes to actively create new connection by altering the node movement, and thus the packet could be delivered in an expedited manner. More importantly, the proposed PMAR could be taken as a component to be integrated into different DTN routings for group mobility to make them more aggressive in delivering packets. In this paper, we integrate PMAR into a previously proposed routing, group-epidemic routing (G-ER). PMAR could be triggered whenever some packets need to be delivered to the destinations in a faster way, but without any connection to their destinations. By computer simulation, it is found that PMAR could greatly improve G-ER, especially the packet delay.

In direct sequence-code division multiple access (DS-CDMA) systems over frequency-selective channels, blind joint equalisation and multiuser detection has been the focus subject of research by many researchers. In this scheme, only the desired user's spreading code and timing information are available to the receiver. The two-stage constant modulus algorithm (CMA)-based receiver proposed, implements an adaptive blind joint equalisation and multiuser detection without explicit channel estimation. The first stage of the algorithm can be seen as a linear combination of blind adaptive minimum output energy (MOE) detectors based on total data correlation matrix. In this study, the authors adopt a MOE detector based on interference correlation matrix instead of total data correlation matrix to improve the performance of the first-stage detector, thus achieving an improved two-stage CMA-based blind joint equalisation and multiuser detection algorithm. In addition, the convergence of the proposed adaptive algorithm is analysed. Both the analysis and computer simulation verify the performance improvement of our proposed algorithm.

This study presents a cluster-based transform domain communication system (TDCS) to improve spectrum efficiency. Unlike the utilities of clusters in orthogonal frequency division multiplex systems, the cluster-based TDCS framework divides entire unoccupied spectrum bins into L clusters, where each one represents a data stream independently, to achieve L times of spectrum efficiency compared to that of the traditional one. Among various schemes of spectrum bin spacing and allocation, the TDCS with random allocation scheme appears to be an ideal candidate to significantly improve spectrum efficiency without seriously degrading power efficiency. In multipath fading channel, the coded TDCS with random allocation scheme achieves robust bit error rate (BER) performance owing to a large degree of frequency diversity. Furthermore, this study shows that the smaller spectrum bin spacing should be configured for the cluster-based TDCS to achieve higher spectrum efficiency and more robust BER performance.

Taking the effect of pulse overlap into account, we address the pre/post-rake technique using rake structure at both transmitter and receiver. Firstly, we propose a single-stream (SS) pre/post-rake, in which the pair of pre-rake and post-rake weighting vectors is optimally derived through generalized eigenvalue decomposition (GEVD). Secondly, by judiciously designing multiple pairs of weighting vectors, we derive multiple-stream (MS) pre/post-rake, which enables parallel subchannel transmission while not requiring multiple antennas at the transmitter or receiver. Finally, we conduct simulations to verify the advantages of the SS pre/post-rake and the MS pre/post-rake over existing rakes.

In this paper we study decode-and-forward multi-antenna relay systems that achieve bi-directional communication in two time slots. We investigate different downlink broadcast schemes which employ binary or analog network coding at the relay. We also analyze and compare their performances in terms of diversity order and symbol error probability. It is shown that if exact downlink channel state information is available at the relay, using analog network coding in the form of multi-antenna maximal-ratio transmit beamforming to precode the information vectors at the relay gives the best performance. Then, we propose a Max-Min antenna selection with binary network coding scheme that can approach this performance with only partial channel state information.

When secondary users (SUs) in a cognitive radio network (CRN) are battery-powered wireless devices, energy resources become very precious. Therefore, it is important that their energies are used efficiently. In this paper, we define the energy efficiency as the ratio of the average throughput of the CRN over the average energy used by the CRN. In cooperative spectrum sensing, the fusion rule threshold, detector's thresholds at the SUs, length of the sensing time, and the number of cooperating SUs will affect both the average throughput and the average energy consumed by the CRN. Therefore, in this paper, we optimize and evaluate these parameters with the aim of maximizing the energy efficiency of the CRN.

In an opportunistic spectrum access based cognitive radio network, it is usually assumed that the secondary user (SU) will transmit at peak power when it detects that the primary user (PU) is absent while it will not transmit when the PU is detected to be present. However, when the PU is detected to be inactive, the PU may not definitely be inactive as it may be a case of miss-detection. If it is a miss-detection, the SU's data rate for that particular data frame will be reduced due to interference from the PU. In this paper, power allocation strategies are designed for each data frame based on the spectrum sensing information (SSI) gathered during the sensing period. Using the SSI, the miss-detection probability for a data frame is taken into consideration when maximizing the average data rate and minimizing the outage probability of the SU. The probability of detecting the PU is required to be above a targeted threshold and the SU is required to satisfy its long-term transmit power budget. Our results showed that the transmit power of the SU should be a function of the SSI in order to maximize the average data rate and minimize the outage probability of the SU.

The Welch bound for aperiodic correlation for binary sequence set was improved by Levenshtein by weighting the cyclic shifts of the sequence vectors. Taking Levenshtein's idea, a new lower bound for quasi-complementary sequence set (QCSS) over the complex roots-of-unity is derived in this paper. It is shown to be tighter than the Welch bound for QCSS in one of the following cases: 1) K = 4M − 1, M ≥ 2 and equation; 2) K ≥ 4M, M ≥ 2 and N ≥ 2. where K,M,N respectively denotes the set size, number of channels, elementary sequence length of QCSS.

In this paper we propose a new routing scheme called group-epidemic routing (G-ER), which is specifically designed for group mobility in mobile ad hoc networks. G-ER is based on one proposed delay tolerant routing protocol called epidemic routing (ER). So, it could tolerate the disconnection characteristic among groups in group mobility. By considering each group as a single node, G-ER has made a great improvement to ER, which is shown in the simulation. In addition, we introduce the buffer sharing mechanism inside each group for G-ER. Also, the simulation has demonstrated the effectiveness of the buffer sharing mechanism, especially in a more disconnected scenario for group mobility.

The correlation lower bounds, as well as the set size upper bounds, of low-correlation-zone complementary sequences are presented in this paper. They can be treated as an extension of Tang-Fan-Matsufuji bounds in and , which considered only the conventional (non-complementary) low-correlation-zone sequence sets.

High-rate space-time block codes (STBC with code rate >; 1) in multi-input multi-output (MIMO) systems are able to provide both spatial multiplexing gain and diversity gain, but have high maximum likelihood (ML) decoding complexity. Since group-decodable (quasi-orthogonal) code structure can reduce the decoding complexity, we present in this paper systematic methods to construct group-decodable high-rate STBC with full symbol-wise diversity gain for arbitrary transmit antenna number and code length. We show that the proposed group-decodable STBC can achieve high code rate that increases almost linearly with the transmit antenna number, and the slope of this near-linear dependence increases with the code length. Comparisons with existing low-rate and high-rate codes (such as orthogonal STBC and algebraic STBC) are conducted to show the decoding complexity reduction and good code performance achieved by the proposed codes.

Full-rate STBC (space-time block codes) with non-vanishing determinants achieve the optimal diversity-multiplexing tradeoff but incur high decoding complexity. To permit fast decoding, Sezginer, Sari and Biglieri proposed an STBC structure with special QR decomposition characteristics. In this paper, we adopt a simplified form of this fast-decodable code structure and present a new way to optimize the code analytically. We show that the signal constellation topology (such as QAM, APSK, or PSK) has a critical impact on the existence of non-vanishing determinants of the full-rate STBC. In particular, we show for the first time that, in order for APSK-STBC to achieve non-vanishing determinant, an APSK constellation topology with constellation points lying on square grid and ring radius √m2+n2(m,n integers) needs to be used. For signal constellations with vanishing determinants, we present a methodology to analytically optimize the full-rate STBC at specific constellation dimension.

Two-path or successive relaying has recently emerged as a promising cooperative communication protocol to improve spectral efficiency in half-duplex relaying networks. In this paper, we consider decode-and-forward (DF) version of the two-path relaying protocol. We analyze the fundamental performance of this protocol in terms of the diversity-multiplexing tradeoff (DMT). We first derive the DMT for this protocol, where perfect decoding at the relays is assumed, and show that it approaches the 3 x 1 multiple-input single-output (MISO) DMT. We then remove the assumption of perfect decoding at the relays and derive the closed-form expression of the achievable DMT based on the relative distances between nodes. Specifically, we found that for sufficiently long transmission length, if the average source-relay SNR is at least 2.5 times (measured in dB) of other links, the 3 x 1 MISO DMT is achieved. Successive interference cancellation at the relays is also proposed to further improve the performance of the DMT.

In an opportunistic spectrum access (OSA) based cognitive radio system, a secondary user (SU) is allowed to access the licensed spectrum of the primary user (PU) when it is inactive. Conventional power allocation strategies, which do not consider spectrum sensing information (SSI), may not be optimal in OSA based cognitive radio system because when the SU mis-detects the PU's presence, the interference from the PU will cause a lower data rate or a higher outage probability to the SU. In this paper, power allocation strategies for each frame are designed based on the SSI gathered by the SU during the sensing period of the frame. We consider both cooperative and non-cooperative spectrum sensing scenarios. In non-cooperative spectrum sensing, the SU transmitter (SU-Tx) has its SSI while in cooperative spectrum sensing, the SU-Tx has both its SSI and the SU receiver's (SU-Rx's) SSI. Using the SSIs, power allocation strategies are designed to either maximize the average data rate or minimize the outage probability of the SU. The proposed power allocation strategies have to ensure that the PU is sufficiently protected and the SU satisfies its long-term transmit power budget. Optimization of the sensing time is also considered to further enhance the performances of the system.

Abstract Automatic modulation classification (MC) is beneficial to digital radio receivers. Conventional MC schemes usually assume that perfect synchronization has been accomplished and then rely on certain statistical characteristics to distinguish different modulation formats. In practice, however, this perfect synchronization assumption is not reasonable since modulation classifiers operate in a non‐cooperative manner and therefore, the receiver has little prior knowledge about a transmitted signal and no training is available. In this paper, we first address asynchronous MC for high‐order QAMs through blind time synchronization. A characteristic function (CF) based approach is proposed to improve the performance of the conventional cumulant method. We then move on to consider asynchronous MC in the presence of frequency offset. We propose a hybrid MC scheme based on blind time synchronization, differential processing, and cumulants to solve this difficult problem. Copyright © 2010 John Wiley & Sons, Ltd.

We show that the common approach to channel and Time-of-Arrival (ToA) estimation for Impulse-Radio Ultra WideBand (IR-UWB), where the occurrence of pulse overlap is ignored, is bound to lead to considerable estimation errors in the presence of pulse overlap. We propose the use of a low-complexity extension of the sliding correlator as a Maximum Likelihood (ML) channel estimation method that can deal with sporadic pulse overlap due to multipath components with delay difference of less than the pulse duration. We also illustrate the superior performance of the improved estimator.

In this paper, we study the performance of bi-directional (two-way) communication system using two different broadcast schemes with network coding. We propose the use of Max-Min criteria for antenna selection with binary network coding and transmit beamforming without binary network coding. We derive and show that unlike traditional antenna selection with binary network coding scheme, the Max-Min Antenna Selection scheme can achieve full diversity order. Compared with transmit beamforming, Max-Min Antenna Selection achieves comparable performance but with less stringent requirements on the channel state information. Also its extension to distributed network of multiple-antenna relays is much easier than transmit beamforming relaying scheme.

In cognitive radio networks, both the sensing time and the fusion schemes used for cooperative spectrum sensing affect the detection probabilities of the primary users and the throughput of the secondary users. Therefore, joint optimization of the sensing time and the cooperative fusion scheme has been studied before in terms of sensing-throughput tradeoff design. In this paper, different from the previous studies, we consider the case that each secondary user may have different detection signal- to-noise ratio (SNR), and requires different threshold for energy detection. Weightings are used to weigh the decisions from the secondary users before combining. A new algorithm is proposed to compute the thresholds for the secondary users and the optimal weightings for the decisions are shown. Computer simulations are presented to show the performance of the proposed algorithm.

Some mobile ad hoc networks (MANETs) such as inter-vehicle communications require a transmission scheme that is robust to mobility and asynchronous reception with low setup overhead. However, many existing cooperative MANETs have complex MAC protocol and high overhead. In this paper, we propose a cross-layer design across the routing, MAC and PHY layers, called delay-tolerant cooperative diversity routing (DT-CDR) MANET, in which network nodes work cooperatively to enhance the robustness of routing through cooperative spacetime transmission. We construct a delay-tolerant orthogonal space-time block code (OSTBC) that supports low-complexity symbolwise decoding in the PHY layer. Due to the delay-tolerant code, strict synchronism is no longer required, hence simple MAC protocol is possible. Performance comparisons between the proposed DT-CDR MANET and conventional routing MANETs show that our design can significantly improve the end-to-end robustness and achieve cooperative diversity.

Diversity embedded space-time code can be used to simultaneously transmit data streams with quality-of-service (QoS) requirements. However, the decoding complexity of existing diversity embedded codes is high since these codes are fully non-orthogonal. In this paper, group-decodable diversity embedded space-time code is proposed and constructed systematically. A code construction is presented for high Scalability in term of diversity levels and decoding complexity. Code examples are shown to achieve very low decoding complexity.

Existing high-rate full diversity space-time codes (STC) always have high decoding complexity with traditional ML (maximum-likelihood) or near-ML decoding. Hence the decoding scheme with reduced complexity is required. In this paper, block-orthogonal structure is introduced for the first time, and most of the existing STC do have such structure. A simplified decoding is proposed to exploit the block-orthogonal structure of the code and reduce the complexity drastically without any extra loss in decoding performance.

In this paper, we present a new class of spacetime block code, called shift-orthogonal space-time block codes (SOSTBC), which can maintain orthogonality (hence symbolwise linear decoding) in asynchronous wireless relay networks, under both amplify-and-forward (AF) and decode-and-forward (DF) relaying schemes. The definitions, systematic constructions and properties of SOSTBC are presented. We also present some simulation results to illustrate its effectiveness in achieving full asynchronous cooperative diversity.

In this paper, an amplify-and-forward (AF) two-path half-duplex relaying scheme is considered in which one of the relays additionally performs inter-relay interference cancellation. We first generalize lower bounds for the diversity-multiplexing tradeoff (DMT) of an arbitrary block lower triangular channel matrix. We then characterize the diversity-multiplexing tradeoff for the AF two-path relaying scheme and show that the DMT achieves the multiple-input single-output (MISO) upper bound. The analysis also demonstrates that, with a careful choice of the coding strategy, the DMT of this scheme is achievable for finite codeword lengths. We then propose using an equivalent linear space time code at the source, which does not require any form of channel state information, as a simple and effective coding strategy to achieve the full DMT of the scheme. From the DMT perspective, the proposed AF two-path relaying with the equivalent linear space time coding outperforms existing schemes. Our analysis is then extended to the slotted-amplify-and-forward (SAF) scheme with multiple relays, where we provide a stronger result by deriving the DMT while taking into account inter-relay interference.

In cognitive radio networks, joint optimization of sensing time and cooperative fusion scheme has been studied in the past in terms of sensing-throughput tradeoff design. In this paper, different from previous studies, we consider the case that the secondary users have different detection signal-to-noise ratios (SNRs) and their decisions are weighted based on the likelihood-ratio test at the fusion center. We consider three scenarios. In Scenario I, we optimize individual secondary users' thresholds together with the fusion rule's threshold at the fusion center. In Scenario II, all the secondary users' thresholds are constrained to be the same and we seek the optimal threshold jointly with the fusion rule's threshold at the fusion center. In Scenario III, each secondary user computes its own threshold while the fusion center optimizes the fusion rule's threshold based on the secondary users' threshold results. Solutions are provided for the three different scenarios and computer simulations are presented to compare their performances.

In this paper, a Modified Phase-Only Correlator (MPOC) is proposed for resolving overlapped Ultra Wideband (UWB) radio pulses in order to achieve high-resolution multi-target UWB ranging. A Phase-Only Correlator (POC) is able to deconvolve the multi-target UWB channel from the received signal spectrum and its impulse-like output enables it to resolve UWB signals that are overlapping. However, the deconvolved channel is often corrupted by amplified noise arising from spectral nulls in the local template. To mitigate this problem, a MPOC which consists of a POC followed by a kurtosis-based noise suppression module is developed to suppress the amplified noise while maintaining the sharpness of the MPOC output. This enables the MPOC to achieve Time-of-Arrival (ToA) resolution much smaller than the UWB pulse duration. The excellent ToA detection performance of the proposed MPOC is verified by experimental measurement.

Interleaving, which is commonly used to combat bursts of errors, is an important technique in digital communications systems. The blind detection of interleaving parameters has been studied by many researchers for forward-error-correction (FEC) encoded binary data streams. In this paper, we use finite field Gauss elimination with pivoting to blindly estimate interleaving period of non-binary data streams. To be specific, the data streams are encoded by Reed Solomon codes. Suppose the percentage of zeros in each column of the analytical matrix block is denoted as phi0. Numerical results show that both the variance and the mean of phi0can be used as the evaluation parameter to detect the interleaving parameters. However, the former approach provides a 1-dB gain over the latter.

In cognitive radio networks, protection to primary users can be quantified by the probability of detection. On the other hand, the probability of false alarm affects the achievable throughput of secondary users. Performances of these two probabilities depend heavily on the fusion scheme used when cooperative sensing is performed. In this paper, we consider the case where N secondary users sense the channel cooperatively using k-out-of-N fusion rule. A sensing-throughput tradeoff under cooperative sensing scenario is formulated to find a pair of sensing time and k value that maximize the secondary users' throughput subject to sufficient protection provided to the primary user. An iterative algorithm is proposed to obtain the optimal values of these two parameters. Computer simulations show that significant improvement of the secondary users' throughput can be achieved when the parameters from the fusion scheme and sensing time are jointly optimized.

Two-way relaying is a way of improving the bandwidth efficiency of a half-duplex relay system. In such scenarios, network coding can also be applied to improve the system if there exist sufficient degrees of freedom to decode the received messages at the relay node. In this paper, we look at two specific transmission schemes employed at the multi-antenna relay node and analyze their performance by deriving both the upper bounds of their symbol error probability and their diversity order.

Spatial multiplexing code (SMC) has high transmission code rate but also high maximum likelihood (ML) decoding complexity. To overcome this disadvantage, we propose 2-group decodable SMC with full symbol-wise diversity for arbitrary number of transmit antennas. In this paper, unbalanced 2-group decodable SMC is first constructed and used to construct balanced 2-group decodable SMC. The rates of both the unbalanced/balanced 2-group decodable SMCs approach Ntasymptotically when T ¿ Ntwhere Ntis the number of transmit antennas and T is the code length. To our best knowledge, this is the first time that 2-group decodable SMCs are constructed systematically.

Abstract-In this paper, novel real symbol-wise ML decodable STBCs with balanced information distribution per codeword are constructed systemically for arbitrary number of transmit antennas. Compared with the existing real symbol-wise ML decodable STBCs in IEEE 802.16e standard, the proposed STBCs also have lower peak-to-average power ratio (PAPR) and better performance even without adaptive antenna grouping based on channel state information(CSI) feedback.

In this paper, we propose to exploit the richly scattered multi-path nature of a frequency selective channel to provide additional degrees of freedom for designing effective precoding schemes for multi-user communications. We design the precoding matrix for multi-user communications based on the generalized multi-unitary decomposition (GMUD), where the channel matrix H is transformed into PiRrQiH. An advantage of GMUD is that multiple pairs of unitary matrices Piand Qican be obtained with one single Rr, Since the column of Qican be used as the transmission beam of a particular user, multiple solutions of Qiprovide a large selection of transmission beams, which can be exploited to achieve high degrees of orthogonality between the multipaths, as well as between the interfering users. Hence the proposed precoding technique based on GMUD achieves better performance than precoding based on singular value decomposition.

The authors first propose a new class of full-rate full-diversity quasi-orthogonal space time block code (QO-STBC), namely QO-STBC with blind precoding (BP-QO-STBC). Based on this code, a novel open-loop multiple-input multiple-output (MIMO) multi-user downlink transmission scheme is derived. Presuming M blocks of BP-QO-STBC (which could be of different code lengths) are transmitted from the access point simultaneously, thus achieving a multiplexing gain of M, we show that the multiplexed signal can be separated by any user with only mR≥M receive antennas, which is much smaller than that required by conventional linear schemes. The proposed scheme is extendable by simple iterative construction to an arbitrary number of transmit antennas. By exploiting the special structure of BP-QO-STBC, we derived two decoding algorithms with different levels of complexities, namely an optimal maximum likelihood decoder that achieves the lowest decoding complexity known for all full-rate full-diversity QO-STBC and a suboptimal linear decoder with even lower complexity. These two decoders combined with the simple construction method of BP-QO-STBC provide flexible tradeoffs between complexity and performance, as well as between diversity and multiplexing gains. A comparison between the proposed scheme and random beamforming in terms of bit error rate and achievable sum throughput is shown via simulation, and the results support our proposed scheme as a better practical candidate for high-speed wireless network.

A binned dirty-paper code (DPC) divides a set of codewords into a number of bins. The codeword in the bin that has the maximum correlation with the side information is selected as the transmitted dirty-paper codeword. This letter derives and verifies the analytical bit-error rate (BER) expression of binned orthogonal block code used as DPC in watermarking applications. The BER trends of such orthogonal DPC under constant code length or constant code rate constrains are analyzed. Finally, we propose a new class of DPC based on binned bi-orthogonal codes and demonstrate its BER performance gain over orthogonal DPC.

Based on the frequency-domain oversampling and minimum mean-square error (MMSE) principles, we propose three linear single-user detectors for downlink multicarrier codedivision multiple-access (MC-CDMA) systems. We begin with an optimal linear MMSE detector, which is computationally demanding. To reduce the complexity, a two-stage MMSE detector and a diagonal one-stage MMSE detector are developed subsequently. Simulation results show that the proposed detectors can efficiently suppress the multiple access interference (MAI) caused by frequency-selective fading, near-far effect, frequency offset, and nonlinear power amplification.

In this paper, we consider a network consisting of one source, one destination and two relays, under a half duplex constraint. We analyze an amplify-and-forward (AF) two-path relaying scheme where one of the relays additionally performs inter-relay interference cancellation. With this inter-relay self interference cancellation, we show that degradation in performance can be significantly reduced when inter-relay channel gain increases. By treating the AF two-path relaying as an equivalent MIMO space time code, we derive an upper bound for the pairwise error probability (PEP) of this system. Previous works indicate that only a diversity order of two can be achieved even though physically there are three possibly independent spatial paths to the destination. From our analysis of the PEP, we analytically prove and identify the reason to the limited diversity order of two. We then propose a simple but novel precoding scheme which allows the system to achieve a full diversity order of three while maintaining the spectral efficiency gain of the two-path relaying protocol. A robust precoding design to achieve full diversity order with high coding gain is also proposed.

Abstract In this paper, we propose a receiver structure for transmitted‐reference ultra‐wideband (TR‐‐UWB) systems with both narrowband interference (NBI) and inter‐pulse interference (IPI) mitigation capabilities. The effects of additive white Gaussian noise (AWGN) and the IEEE 802.15.4a fading channel are also taken into consideration. We adopt band‐stop filtering to suppress the NBI. For IPI, it is statistically removed by a sum‐and‐average process. Theoretical analysis is carried out to study the lower bound of the bit‐error rate (BER) performance of the proposed receiver. Numerical results show that the proposed receiver is able to provide satisfactory performance and is robust to variations in the system design parameters. It is also observed that the proposed receiver is able to deliver good performance even when there is zero delay between the reference and data pulses, which can effectively double the system throughput. Copyright © 2008 John Wiley & Sons, Ltd.

In certain multiple-input multiple-output (MIMO) fading environments, the offered channel capacity can be very low even when the MIMO channels are uncorrelated; this effect has been termed as keyhole or pinhole effect. In this paper, we present rigorous error rate analysis of orthogonal space time block codes in Nakagami-m keyhole channels with arbitrary fading parameters. Using a moment-generation-function based approach, we provide the exact expressions of symbol error rate. Furthermore, the closed-form asymptotic expressions are derived, based on which one can easily quantify the diversity gain of the considered system with arbitrary m-fading parameters while existing methods in literature fail to do so. Simulation results are also provided to verify our analytical results.

In this paper, we propose a decomposition method called Generalized Multi-Unitary Decomposition (GMUD) which is useful in multi-user MIMO precoding. This decomposition transforms a complex matrix H ∈ Cm×ninto H = PiRQiH, where R is a special matrix whose first row contains only a non-zero user defined value at the left-most position, Piand Qiare a pair of unitary matrices. The major attraction of our proposed GMUD is we can obtain multiple solutions of Piand Qi. With GMUD, we propose a precoding method for a MIMO multi-user system that does not require full channel state information (CSI) at the transmitter. The proposed precoding method uses the multiple unitary matrices property to compensate the inaccurate feedback information as the transmitter can steer the transmission beams of individual users such that the inter-user interference is kept minimum.

In a three-node wireless relay network, two nodes, BS1 and BS2, exchange information through a relay node, RL. Suppose time division duplex is used, Physical Network Coding (PNC) uses two time slots for the information exchange instead of four time slots needed by the conventional method. In the first time slot, both BS1 and BS2 transmit simultaneously to RL. The relay node, RL does a PNC mapping based on the received signal and broadcast the mapped signal back to BS1 and BS2 simultaneously during the second time slot. The nodes, BS1 and BS2 are able to decode their desired information based on the received mapped signal and the signal which they had transmitted during the first time slot. In this paper, we analyze the average BER of the information exchanged between the two nodes in Rayleigh fading environments. We also derive the average BER of the mapped signal at the relay during the first time slot. With the derived BER of the mapped signal at RL, we propose to use power control at BS1 and BS2 to minimize the instantaneous BER of the mapped signal at RL. The proposed technique improves the BER of the desired information decoded at the two nodes. The solution turns out to be channel inversion based power control at both BS1 and BS2. The proposed power control technique improves both the average BER of the mapped signal at RL and the desired information at BS1 and BS2.

In this paper, we analyze a full rate cooperative communication system using amplify-and-forward (AF) two-path relaying scheme with inter-relay interference cancellation at the relay. By treating AF two-path relaying as an equivalent MIMO system, we derive the pairwise error probability (PEP) of this system. Given one source node, two half duplex relay nodes, and one destination node, all equipped with single antenna, our theoretical analysis and simulation results show that diversity order of 2 can be achieved by this system. Furthermore, with inter-relay interference cancellation, degradation in performance can also be significantly reduced when inter-relay channel gain increases. Our analysis also shows that diversity order of the system will not be affected by the magnitude of this inter-relay channel gain.

In this paper, we consider a two-way or bi-directional communications system with a relay equipped with multiple antennas. We show that when the downlink channel state information is not known at the relay, the benefit of having additional antennas at the relay can only be obtained by using decode and forward (DF) but not amplify and forward (AF). The gain becomes significant when we employ transmit diversity together with wireless network coding. We also demonstrate how the performance of such system can be improved by performing antenna selection at the relay. Our results show that if downlink channel state information is known at the relay, network coding may not provide additional gain than simple antenna selection scheme.

In this paper, we develop kappa-connectivity in wireless multi-hop networks in the presence of both the log-normal shadowing and Nakagami-m fading. The formula are generally applicable in a variety of realistic physical situations. Based on the results, we are able to find the critical node density to satisfy node kappa-connectivity almost with probability 1. In addition, kappa-connectivity shows significant difference with distinct channel conditions, which necessitates a generic result. The result is useful for the design of a wireless multi-hop network in practice.

A novel 4 x 1 scheme of transmitting two real orthogonal space-time block codes (O-STBC) on the I- and Q-channels with pulse-shaped offset QAM has recently been proposed. In order to achieve good spectral confinement, a pulse shape that introduces a small amount of controlled interference between I/Q- channels is used for the offset QAM signal. In this paper, two-real- symbol joint decoding is investigated for the scheme. This joint decoding can outperform the single-real symbol decoding proposed previously and the best-performing quasi-orthogonal space-time block code (QO-STBC) by more than 1 dB at bit error probability of 10-5when the interference introduced by the pulse shape is relatively large.

Conventional modulation classification (MC) schemes usually assume that perfect synchronization has been accomplished and then rely on certain statistical characteristics to distinguish different modulation formats. In practice, however, this perfect synchronization assumption is not reasonable since modulation classifiers operate in a non-cooperative manner and therefore, the receiver has little prior knowledge about the transmitted signals and no training is available. In this paper, we first address asynchronous MC for high-order QAMs through blind time synchronization. A characteristic function (CF) based approach is proposed to improve the performance of the conventional cumulant method. We then move on to consider asynchronous MC with frequency offset. We propose a hybrid MC scheme based on blind time synchronization, differential processing, and cumulants to solve this difficult problem.

This paper concerns the multiple access performance of spreading codes used in the downlink of multicar- rier code-division multiple-access (MC-CDMA). Complex-valued spreading codes, particularly polyphase codes, are investigated based on a system model in which modulation formats, equalization/combining schemes, and fading statistics are general. Different from previous approaches, we pay special attention to the influence of channel correlation on the performance of spreading codes and try to take advantage of the channel correlation in the design of spreading codes. We begin by considering a worst-case frequency-selective fading channel characterized by independent and identically distributed (i.i.d.) fading for all subcarriers. Unitary polyphase spreading codes are proven to be optimal among all polyphase codes for the i.i.d. fading channel. Next, optimal spreading codes for a two-user case are derived analytically. We proceed to consider a block-wise correlated fading channel model. We propose a class of channel-correlation- matched Kronecker-product unitary spreading codes, which can exploit the channel correlation, thereby reducing multiple access interference (MAI) significantly. Finally, we extend the Welch lower bound to the downlink of MC-CDMA over correlated fading channels and investigate the performance of Welch bound equality codes.

A new 4times1 scheme of transmitting two real orthogonal space-time block codes (O-STBC) on the I- and Q-channels with pulse-shaped offset QAM is proposed. This scheme achieves full rate and employs simple real-symbol-wise decoding. Two families of pulse shapes for the offset QAM signal are considered: i) pulse shapes with zero cross-channel interference (CCI) between I/Q-channels and ii) pulse shapes with controlled CCI. In the zero CCI case, the real-symbol-wise decoding is optimum and the bit error probability (BEP) performance is better than that of conventional full-rate, full-diversity quasi-orthogonal space-time block codes (QO-STBC), as well as rate-1/2 and rate-3/4 complex O-STBCs. To achieve good spectral confinement while maintaining decoding simplicity, we investigate pulse shapes that introduce a small amount of controlled CCI at the sampling instants so that some performance gains can be traded off. We derive a BEP upperbound for the case of nonzero CCI and formulate the design criteria on how to construct the two real O-STBCs on the I/Q channels in order to minimize the performance degradation caused by the controlled CCI. The optimum real O-STBCs are then identified and their BEP performance is compared with that of the benchmark schemes.

In this paper we propose a location aided directed routing protocol for mobile wireless ad-hoc networks. The proposed protocol is an on demand reactive strategy that relies on location information of nodes. The protocol uses simple caches and certain routing packets to maintain nodes' location information. Simulation results illustrate the performance of the proposed protocol.

The rate-one semi-orthogonal algebraic space-time (SAST) codes is recently introduced. In this paper, the authors propose a new signal design method for SAST codes so that the transmitted symbols can be divided into four groups for maximum likelihood (ML) detection; the authors call this new design of SAST codes four-group decodable SAST (4Gp-SAST) codes. The decoding complexity of 4Gp-SAST codes is greatly reduced compared with that of the original SAST codes, where the ML decoding of the transmitted symbols is made into two groups. The exact pair-wise error probability of 4Gp-SAST codes is derived and it is used to optimize the signal designs. Simulation results show that 4Gp-SAST codes perform better than several low complexity space-time block codes such as orthogonal and quasi-orthogonal codes.

Space-time block codes are known to be a powerful tool to achieve full spatial diversity gain while maintaining a very simple receiver structure. In this paper, the authors analyze the asymptotic error performance of orthogonal space-time block codes over flat Nakagami-m fading channels. Both identical and non-identical m-distributions are considered. Based on the moment generation function (MGF) approach, the closed-form asymptotic symbol error probabilities over Nakagami-m fading channels are derived. Compared to the previous works in literature, our results are more accurate and more general. Simulation results are provided to verify our analytical results.

The generic algebraic structure of a quasi-orthogonal space-time block code (QO-STBC) has been derived, and group-constrained linear transformation (GCLT) as a mean to optimise the diversity and coding gains of a QO-STBC with square or rectangular QAM constellations has been proposed. Compared with QO-STBC with constellation rotation (CR), QO-STBC with GCLT requires only half the number of symbols for joint detection, hence lower maximum-likelihood decoding complexity. The optimum GCLT parameters for QO-STBC with square QAM constellation have been derived analytically. The optimised QO-STBCs with GCLT are able to achieve full-transmit diversity and have negligible performance loss compared with QO-STBCs with CR at the same code rate.

In this paper, we show, in both theory and simulation, that by transmitting two real orthogonal space-time block codes (O-STBC) on the I- and Q-channels with pulse-shaped offset QAM, we are able to achieve full rate transmit diversity for a 4 times 1 system with very simple "half-symbol" detection, i.e., the real I- and Q-symbols can be separately decoded. For this scheme, two families of pulse shapes of the offset QAM signal are considered: i) pulse shapes with zero inter-symbol interference (ISI) and ii) pulse shapes with controlled ISI between the I- and Q-channels. In the zero ISI case, the half-symbol decoding is optimum in the maximum likelihood sense and the system is able to achieve full transmit diversity. The associated bit error probability (BEP) performance is better than that of conventional full-rate, full-diversity quasi-orthogonal space-time block codes (QO-STBC), as well as rate-1/2 and rate-3/4 complex O-STBCs. To achieve good spectral confinement while maintaining decoding simplicity, we investigate pulse shape design techniques so that some performance gains can be traded off by introducing a controlled small amount of ISI at the sampling instants. We also extend the analysis to a 4 times 4 multiple-input multiple-output (MIMO) system, and derive the design criteria on how to construct the two real O-STBCs in order to minimize the performance degradation caused by the controlled ISI

We show that there exists an interesting relationship between the algebraic constraints of Amicable Orthogonal Design (AOD) matrices and the dispersion matrices of Quasi-Orthogonal Space-Time Block Code with minimum decoding complexity (MDC-QOSTBC). This insight leads to a novel concept of "Preferred AOD Pair", and we show that MDC-QOSTBC can be constructed from two AODs that form a Preferred AOD Pair. With the Preferred AOD Pair concept, we are able to derive the lower bound on code rate of square MDC-QOSTBC, and show them to be 1 and 3/4 for four and eight transmit antennas respectively. We also present a systematic method to construct Preferred AOD Pair and use them to obtain new MDC-QOSTBCs.

We show a new design of unitary differential space-time modulation (DSTM) with low decoding complexity, i.e. its decoding can be performed by two parallel decoders where each of the decoder has a search space of only radicN for a codebook with N codewords. The design is based on orthogonal space-time block code (O-STBC) and spherical code. We separate the symbols of an O-STBC into two groups and jointly modulate the symbols within a group using a joint constellation set constructed from spherical code. The proposed unitary DSTM scheme has a lower decoding complexity than many other DSTM schemes including those based on group codes and Sp(2) with better or comparable decoding performance. It also achieves a better performance than existing DSTM schemes with similar decoding complexity

Code shortening is commonly performed in practice but its analytical decoding performance is not well understood. In this letter, we present tight bounds for estimating the weight enumerator and hence BER (bit error rate) performance of linear shortened and shortened-extended block/product codes operating in AWGN and Rayleigh fading channels. The bounds are easy to use as they are expressed in terms of the original (unshortened) code properties, which are widely reported. Asymptotic coding loss expressions associated with code shortening are also derived.

We present a novel differential space-time modulation (DSTM) scheme that is single-symbol decodable and can provide full transmit diversity. It is the first known single-symbol-decodable DSTM scheme not based on orthogonal STBC (O-STBC), and it is constructed based on the recently proposed minimum-decoding-complexity quasi-orthogonal space-time block code (MDC-QOSTBC). We derive the code design criteria and present systematic methodology to find the solution sets. The proposed DSTM scheme can provide higher code rate than DSTM schemes based on O-STBC. Its decoding complexity is also considerably lower than DSTM schemes based on Sp(2) and double-symbol-decodable QO-STBC, with negligible or slight trade-off in decoding error probability performance

We show that minimum-decoding-complexity quasi-orthogonal space-time block code (MDC-QOSTBC) can be used to form a new differential space-time modulation (DSTM) scheme to provide full transmit diversity with single-symbol decodable complexity. This is the first known single-symbol decodable DSTM not based on orthogonal STBC. We derive the DSTM code design criteria and use them to construct customized constellation sets that provide full diversity and maximum coding gain. Our new DSTM scheme can provide a higher code rate and better decoding performance than DSTM schemes based on rate-1/2 orthogonal STBC. It also has a lower decoding complexity than the other DSTM schemes, as its maximum likelihood decoding only requires the joint detection of two real symbols.

We propose and construct a new class of full-rate full-diversity Quasi-Orthogonal Space-Time Block Code (QO-STBC), namely Four-Group QO-STBC (4Gp-QOSTBC), which can support any number of transmit antennas. 4Gp-QOSTBC can linearly separate the symbols into four independent groups, such that symbols within a group is orthogonal to all other symbols in another groups, and the maximum-likelihood (ML) decoder of the code only needs to jointly decode the symbols within the same group. The number of symbols required for joint detection of the newly proposed 4Gp-QOSTBC is half of that required by the existing QO-STBCs with the same code rate, and their full-diversity decoding performances are comparable. We also extend the proposed code construction to obtain 6Gp-QOSTBC and 8Gp-QOSTBC, which have even lower decoding complexity, albeit at a slight loss in code rate.

In this paper, several new high-rate STBCs with transmit diversity gain of two are proposed. The proposed STBCs can flexibly support either three or four transmit antennas, and a code rate of between two to four. We show that they have good symbol dispersion property that leads to better BER performance under spatially-correlated MIMO channels or when the number of transmit antennas is reduced from four to three. They also have better coding gain and lower decoding complexity than some existing high-rate STBCs at the same spectral efficiency.

A new class of quasi-orthogonal space-time block code (QO-STBC) namely minimum-decoding-complexity QO-STBC (MDC-QOSTBC) has recently been proposed in the literature. In this paper, we analyze some of its essential code parameters and code properties. Specifically we derive its maximum achievable code rate expression for any number of transmit antennas. We also derive the closed form expression of its diversity product and use it to obtain the optimum constellation rotation in order to achieve optimum decoding performance. Other performance benefits of MDC-QOSTBC, such as low decoding complexity, good coding gain and power distribution property, flexibility in supporting any constellation and number of transmit antennas, are also presented and discussed.

In this paper, we consider a quasi-orthogonal (QO) space-time block code (STBC) with minimum decoding complexity (MDC-QO-STBC). We formulate its algebraic structure and propose a systematic method for its construction. We show that a maximum-likelihood (ML) decoder for this MDC-QO-STBC, for any number of transmit antennas, only requires the joint detection of two real symbols. Assuming the use of a square or rectangular quadratic-amplitude modulation (QAM) or multiple phase-shift keying (MPSK) modulation for this MDC-QO-STBC, we also obtain the optimum constellation rotation angle, in order to achieve full diversity and optimum coding gain. We show that the maximum achievable code rate of these MDC-QO-STBC is 1 for three and four antennas and 3/4 for five to eight antennas. We also show that the proposed MDC-QO-STBC has several desirable properties, such as a more even power distribution among antennas and better scalability in adjusting the number of transmit antennas, compared with the coordinate interleaved orthogonal design (CIOD) and asymmetric CIOD (ACIOD) codes. For the case of an odd number of transmit antennas, MDC-QO-STBC also has better decoding performance than CIOD.

By using joint modulation and a customized constellation set, we show that quasi-orthogonal space-time block coding (QO-STBC) can be used to form a new differential space-time modulation (DSTM) scheme to provide full transmit diversity with non-coherent detection. Our new scheme can provide a higher code rate than existing DSTM schemes based on orthogonal STBC. It also has a lower decoding complexity than other DSTM schemes, such as those based on group codes, because it only requires a joint detection of two complex symbols. We derive the design criteria for the customized constellation set and use them to construct a constellation set that provides a wide range of spectral efficiency with full diversity and maximum coding gain.

We first derive the generic algebraic structure of a quasi-orthogonal STBC (QO-STBC). Next we propose group-constrained linear transformation (GCLT) as a means to optimize the diversity and coding gains of a QO-STBC. Compared with the constellation rotation (CR) technique reported in the literature, we show that QO-STBC with GCLT requires only half the number of symbols for joint detection in the maximum-likelihood (ML) decoder when square or rectangular QAM constellations are used, hence it has lower decoding complexity than QO-STBC with CR. We also derive analytically the optimum GCLT parameters for QO-STBC with a generic square or rectangular QAM constellation for four and eight transmit antennas. The resulting QO-STBCs with GCLT can achieve full transmit diversity, and they have negligible performance loss compared with QO-STBCs with CR at the same code rate.

Data embedding in sharply-contrasted binary images like text, drawing, signature and cartoon is a challenging issue due to simple pixel statistics in such images. Arbitrary modification to the pixels can be visually perceptible in the process of data embedding. The use of a valid perceptual model is important to minimize the effect of such visual distortion in binary images. In this paper, a novel perceptual model is used to embed significant amount of information such that the original and the marked images before and after data embedding process are perceptually similar. In our model, the distortion that occurs after flipping a pixel is estimated on the curvature-weighted distance difference (CWDD) measure between two contour segments.

Amicable complex orthogonal design (ACOD), which is the complex version of amicable orthogonal design first reported in Wolfe (1976) is proposed and its existence is proven. Using ACOD, new orthogonal space-time block codes (O-STBC) for four and eight transmit antennas are constructed. Their maximum achievable code rates are proven to be as high as the existing O-STBC designs. In addition, the new O-STBC are shown to have better practical implementation features than the existing O-STBC, as they do not require any transmit antenna to be turned off intermittently (which is beneficial for the power amplifier design), and they do not have irrational-number coefficients inside the codeword (which simplifies the hardware implementation).

The alteration of compressed video bitstream due to embedding of digital watermark tends to produce unpredictable video bit rate variations which may in turn lead to video playback buffer overflow/underflow or transmission bandwidth violation problems. This paper presents a novel bit rate control technique for real-time MPEG watermarking applications. The proposed bit rate control scheme evaluates the combined bit length of a set of multiple watermarked VLC codewords and successively replaces watermarked VLC codewords having the largest increase in bit length with their corresponding unmarked VLC codewords until a target bit length is achieved. The proposed method offers much flexibility and scalability compared to the pioneering scheme proposed by Hartung and Girod. Experimental results show that the proposed bit rate control scheme is effective in meeting the bit rate targets and capable of improving the watermark detection robustness for different video contents compressed at different bit rates.

In this paper, we formulate the algebraic structure of quasiorthogonal STBC with minimum decoding complexity (MDC-QOSTBC), whose maximum likelihood (ML) decoder only requires the joint detection of two real symbols, for any numbers of transmit antennas. We also propose a systematic method to construct an MDC-QOSTBC from an Orthogonal-STBC (O-STBC). The maximum code rate of the resultant MDC-QOSTBC is shown to be the same as that of the lower-order O-STBC used to construct it. We also find the optimum constellation rotation angle for the proposed MDC-QOSTBC construction to achieve full diversity and optimum coding gain. We can show that the proposed MDC-QOSTBC has more even power distribution over the transmit antennas, better scalability in adjusting the number of transmit antennas, and more superior decoding performance than the coordinate interleaved orthogonal design (CIOD) and asymmetric CIOD

Quasiorthogonal space-time block code (QO-STBC) may be preferred to orthogonal STBC as it can achieve a higher code rate and to nonorthogonal STBC as it can achieve a lower decoding complexity. In this paper, the algebraic structure of QO-STBC is derived. The search of QO-STBC with rate greater than one for four transmit and at least two receive antennas is carried out. With the aids of graph theory, it is found that if the transmit diversity level is four, the maximum code rate is limited to 5/4; while if the transmit diversity level is lowered to two, the maximum code rate can go up to 4. The maximum likelihood decoding of these QO-STBCs can be computed in two separated groups, hence lead to a lower decoding complexity as compared to other nonorthogonal STBCs.

Nakagami fading model is widely used in modeling wireless communication systems. In this paper, we present methods to generate Nakagami fading signals with arbitrary cross-correlation and fading parameters by taking the square root of correlated Gamma random variables (RVs) with the corresponding shape parameters. To generate correlated Gamma RVs with different noninteger values of m-parameters, two methods, namely the decomposition method and Sim's method, are proposed. The former is more flexible and efficient. The latter is mathematically exact but carries constraints on the permissible simulation parameters. Simulations show that both methods produce outputs that match well with the specifications.

Quasiorthogonal space-time block code (QO-STBC) can provide full-rate transmission and low decoding complexity. Full-level of transmit diversity can be achieved if constellation rotation technique has been used. In this paper, the design and application of noise whitening filter for the receiver of QO-STBC are discussed in detail. Such filter facilitates the use of standard equalizer or decoder that have been designed for white noise on the received QO-STBC signals, which tend to suffer from colored noise after matched filtering. This eliminates the need for dedicated decoding metric for signals with colored noise. It is found that only maximum likelihood decoding and sphere decoding can take advantage of the constellation rotation code design technique to achieve full diversity. Analytical results show that the SNR of the estimated symbol is independent to the angle of constellation rotation if symbol-by-symbol decoding schemes are used. This confirms with the simulation results where the performance of linear equalizers and decision feedback equalizers are the same for both constellation rotated and nonconstellation rotated QO-STBC.

Certain OFDM channels can be shown to exhibit different fading severity amongst the subcarriers. For such channels, we propose novel adaptive power control and bit loading algorithms to maximize the spectral efficiency of the OFDM system while minimizing the required transmit power at some specified BER level. Unlike its existing counterparts, our adaptive modulation algorithm adapts the subcarrier transmission parameters based on the subcarrier fading statistics rather than the instantaneous channel conditions. A new closed-form expression with good inversion property for QAM BER in Rician fading channel is also developed to facilitate the computation of the optimum adaptive modulation settings. Results show that the proposed scheme can achieve considerable performance enhancement over non-adaptive systems.

Nakagami's (1960) fading model is widely adopted for analyzing wireless communication systems and diversity combining techniques, hence the ability to generate Nakagami fading signals with arbitrary parameters is important for system design and simulations. In the literature, there are only algorithms for generating correlated Nakagami branches with the same fading parameter. We adopt a novel approach to generate Nakagami fading signals with arbitrary fading parameters and any correlation structure. The correlated Nakagami fading variables are generated from the corresponding uncorrelated gamma random variables (RVs). To achieve this, we propose a new decomposition method, and introduce Sim's (1992)method as a complementary approach, to generate correlated gamma RVs. The former is an approximate approach but can deal with any general correlation structures efficiently, while the latter is exact but has some constraints.

This paper studies and analyzes the performance of space-time coding (STC) with channel-matched Viterbi decoding in a frequency-selective multipath fading channel. Under the assumption that the perfect channel information is available to the receiver, we prove that space-time codes designed for flat fading channels can achieve additional diversity in the frequency selective multipath fading channel. Using computer simulation, the analysis results are confirmed.

Two ways for modifying the classical Jakes (1974) fading simulator to generate multiple uncorrelated fading waveforms are proposed. The 1/sup st/ and 2/sup nd/ order statistics of individual output waveforms are shown to agree well with theoretical expectations. The cross correlation between different waveforms is nearly zero. These fading simulators are expected to be useful in the investigations of frequency-selective and diversity-combined multipath fading channels.

A study on the effect of genetic parameters is carried out in order to find the optimum value of the parameters in genetic algorithms for solving personal communication service network planning problems. As for any genetic algorithm, one of the problems is its convergence behavior which is affected by many factors including the genetic parameters. The selection of these genetic parameters is generally quite ad hoc. However, we find in this study that proper parameter selection is critical because they significantly affect the convergence and optimality of the solution obtained.