This paper investigates covert communication of multi-antenna relaying system with probabilistic interference, where a multi-antenna Alice transmits its covert message to a single-antenna Bob through a multi-antenna relay. The relay operates under the amplify-and-forward (AF) protocol, and employs antenna selection based on the relay-Bob channel quality. To enhance the covert communication, a multi-antenna jammer probabilistically transmits jamming signals to degrade Willie’s detection performance. For this system, we formulate the covert communication problem as an optimization framework which maximizes the received signal-to-interference-plus-noise ratio (SINR) at Bob while ensuring covertness constraints, by jointly optimizing the beamforming vectors at Alice and the jammer, the jamming probability, and the relay’s amplification factor. To address this non-convex optimization problem, we first derive Willie’s detection error probabilities for both transmission phases. Next, we analytically determine Willie’s optimal detection threshold and minimum detection error probability by examining their monotonicity and extremal properties. Then, we transform the original problem into a tractable convex form based on these analytical results. We further propose an efficient alternating optimization algorithm to iteratively update the system parameters. Simulation results are finally demonstrated to show the effectiveness of the proposed scheme, showing that increasing the jamming power or the number of antennas at the relay significantly improves Bob’s received SINR.

Given the significant potential of orthogonal time frequency space (OTFS) signals in advancing integrated sensing and communication (ISAC) systems, this paper investigates the target sensing enhancements in ISAC-OTFS systems. Specifically, a customized inter-range-cell interference (IRCI)-free range reconstruction method is proposed for high-quality target sensing. The resultant mainlobe signal-to-noise ratio (SNR) is derived as a function of OTFS waveform parameters, and the achievable maximum SNR is deduced to provide a benchmark for subsequent simulations. The optimization of OTFS waveforms for maximizing the target sensing SNR is formulated with hardware realization constraints (i.e., peak to average power ratio (PAPR) and energy constraints). The resultant non-convex multi-ratio fractional programming problem is solved using a hybrid algorithm named multi-ratio fractional programming and partial successive convex approximation (MR-FP-PSCA). Finally, the numerical results, including the IRCI-free target sensing method and the proposed optimization algorithm, demonstrate the effectiveness of the developed schemes.

This paper introduces a novel finite Zak transform (FZT)-aided framework for constructing multiple zero-correlation zone (ZCZ) sequence sets with optimal correlation properties. Specifically, each sequence is perfect with zero auto-correlation sidelobes, each ZCZ sequence set meets the Tang-Fan-Matsufuji bound with equality, and the maximum inter-set cross-correlation of multiple sequence sets meets the Sarwate bound with equality. Our study shows that these sequences can be sparsely expressed in the Zak domain through properly selected index and phase matrices. Particularly, it is found that the maximum inter-set cross-correlation beats the Sarwate bound if every index matrix is a circular Florentine array. Several construction methods of multiple ZCZ sequence sets are proposed, demonstrating both the optimality and high flexibility. Additionally, it is shown that excellent synchronization performance can be achieved by the proposed sequences in orthogonal-time-frequency-space (OTFS) systems.

Jamming in some cases observed at its transmitting side could be modeled as a constant amplitude but random phase signal:J(t)= AJ · ejϕ(t). Such situation may happen in single-tone or multi-tone jammed frequency hopping systems, in cellular mobile systems under co-channel interference, etc. In order to appropriately handle this kind of jamming through channel coding technologies, a jamming model based log-likelihood ratio (LLR) initialization method is proposed to replace their conventional AWGN model based LLR initialization method. Accordingly, the optimal LLR initialization formula is derived based on the analysis of the probability density function of jammed signals. Then, the approximation of optimal LLR initialization as well as its robust variant are further provided, hence the complexity is significantly reduced and only the signal-to-noise ratio (SNR) and signal-to-jamming ratio (SJR) are required as thea prioriknowledge. The proposed LLR initialization method is tested in a Polar Code (PC) coded Orthogonal Frequency Division Multi-plexing (OFDM) system, where both the AWGN and the Rayleigh fading channel models are considered. The obtained simulation results demonstrate that the proposed method outperforms the conventional Gaussian model based one and other existed robust initialization methods.

Arrays with good correlation properties have emerged with promising applications in wireless communication, including ultra-wideband (UWB), two-dimensional (2- D) synchronization, and massive multiple-input multiple-output (MIMO). In this paper, we propose two efficient algorithms for designing arrays with low autocorrelation by minimizing the weighted integrated sidelobe level (WISL) and complementary ISL (CISL), respectively. We formulate these metrics as non-convex quartic problems under unimodular constraints in the frequency domain. Using the majorization-minimization (MM) framework, these problems are simplified into quadratic forms, ensuring monotonic convergence to a stationary point. Furthermore, a projection algorithm is introduced to relax unimodular constraints to peak-to-average power ratio (PAPR) constraints, enhancing the correlation performance of the optimized arrays. All proposed algorithms can be efficiently implemented using two-dimensional FFT/IFFT operations. Furthermore, a quasi-complementary array set (QCAS), generated via the proposed MM-ACISL algorithm, is applied as an omnidirectional precoding matrix in massive MIMO systems. Numerical experiments demonstrate that the proposed algorithms outperform existing methods in terms of both correlation performance and computational efficiency. Moreover, the resulting QCAS scheme effectively enables omnidirectional transmission in MIMO systems.

Recently, the pinching-antenna system (PASS) has emerged as a promising architecture owing to its ability to reconfigure large-scale path loss and signal phase by activating radiation points along a dielectric waveguide. However, existing studies mainly focus on line-shaped PASS architectures, whose limited spatial flexibility constrains their applicability in multiuser and indoor scenarios. In this paper, we propose a novel two-dimensional (2D) pinching-antenna system (2D-PASS) that extends the conventional line-shaped structure into a continuous dielectric waveguide plane, thereby forming a reconfigurable radiating plane capable of dynamic beam adaptation across a 2D spatial domain. An optimization framework is developed to maximize the minimum received signal-to-noise ratio (SNR) among user equipments (UEs) by adaptively adjusting the spatial configuration of pinching antennas (PAs), serving as an analog beamforming mechanism for dynamic spatial control. For the continuous-position scenario, a particle swarm optimization (PSO)-based algorithm is proposed to efficiently explore the nonconvex search space, while a discrete variant is introduced to accommodate practical hardware constraints with limited PA placement resolution. Simulation results demonstrate that the proposed 2D-PASS substantially improves the minimum SNR compared with conventional line-shaped PASS and fixed-position antenna (FPA) benchmarks, while maintaining robustness under varying user distributions and distances.

In this paper, we consider a novel optimization design for multi-waveguide pinching-antenna systems, aiming to maximize the weighted sum rate (WSR) by jointly optimizing beamforming coefficients and antenna position. To handle the formulated non-convex problem, a gradient-based meta-learning joint optimization (GML-JO) algorithm is proposed. Specifically, the original problem is initially decomposed into two sub-problems of beamforming optimization and antenna position optimization through equivalent substitution. Then, the convex approximation methods are used to deal with the nonconvex constraints of sub-problems, and two sub-neural networks are constructed to calculate the sub-problems separately. Different from alternating optimization (AO), where two sub-problems are solved alternately and the solutions are influenced by the initial values, two sub-neural networks of proposed GML-JO with fixed channel coefficients are considered as local sub-tasks and the computation results are used to calculate the loss function of joint optimization. Finally, the parameters of sub-networks are updated using the average loss function over different sub-tasks and the solution that is robust to the initial value is obtained. Simulation results demonstrate that the proposed GML-JO algorithm achieves 5.6 bits/s/Hz WSR within 100 iterations, yielding a 32.7% performance enhancement over conventional AO with substantially reduced computational complexity. Moreover, the proposed GML-JO algorithm is robust to different choices of initialization and yields better performance compared with the existing optimization methods.

Low ambiguity zone (LAZ) sequences play a crucial role in modern integrated sensing and communication (ISAC) systems. In this paper, we introduce a novel class of functions known as locally perfect nonlinear functions (LPNFs). By utilizing LPNFs and interleaving techniques, we propose three new classes of both periodic and aperiodic LAZ sequence sets with flexible parameters. The proposed periodic and aperiodic LAZ sequence sets are asymptotically optimal with respect to the periodic and aperiodic lower AF bounds, respectively, which were proposed recently in [IEEE J. Sel. Areas Commun. 40 (6): 1809-1822]. Notably, the aperiodic LAZ sequence sets are the first such sequence sets in the literature that satisfy the bound. Finally, we demonstrate that the proposed sequence sets are cyclically distinct.

Distributed computing has been widely applied in distributed edge networks for reducing the processing burden of high-dimensional data centralization, where a high-dimensional computational task is decomposed into multiple low-dimensional collaborative processing tasks or multiple edge nodes use distributed data to train a global model. However, the computing power of a single-edge node is limited, and collaborative computing will cause information leakage and excessive communication overhead. In this paper, we design a parallel collaborative distributed alternating direction method of multipliers (ADMM) and propose a three-phase parallel collaborative ADMM privacy computing (3P-ADMM-PC2) algorithm for distributed computing in edge networks, where the Paillier homomorphic encryption is utilized to protect data privacy during interactions. Especially, a quantization method is introduced, which maps the real numbers to a positive integer interval without affecting the homomorphic operations. To address the architectural mismatch between large- integer and Graphics Processing Unit (GPU) computing, we transform high-bitwidth computations into low-bitwidth matrix and vector operations. Thus the GPU can be utilized to implement parallel encryption and decryption computations with long keys. Finally, a GPU-accelerated 3P-ADMM-PC2 is proposed to optimize the collaborative computing tasks. Meanwhile, large-scale computational tasks are conducted in network topologies with varying numbers of edge nodes. Experimental results demonstrate that the proposed 3P-ADMM-PC2 has excellent mean square error performance, which is close to that of distributed ADMM without privacy-preserving. Compared to centralized ADMM and distributed ADMM implemented with Central Processing Unit (CPU) computation, the proposed scheme demonstrates a significant speedup ratio.

This paper proposes a novel multi-carrier modulation framework for high-mobility communication scenarios. Our key idea lies in spreading data symbols across the delay-Doppler (DD) domain through orthogonal chirp-Zak transform (CZT). To enable efficient signal multiplexing, the proposed modulation scheme employs a transmitter signal that maintains orthogonality with the inherent resolution characteristics of the DD plane. Termed as Orthogonal Chirp Delay-Doppler Division Multiplexing (CDDM), we demonstrate a synergistic integration of chirp waveform properties with the channel structure of the DD domain, thereby achieving advantages with both lower computational efficiency and improved detection performance. We introduce a novel CZT-based superimposed sparse pilot structure to enable simultaneous estimation of delay-Doppler shifts and channel coefficients. For enhanced performance, we further develop an embedded pilot scheme that demonstrates channel estimation performance comparable to that of Orthogonal Delay-Doppler Division Multiplexing (ODDM) systems. Simulation results demonstrate that CDDM achieves significant bit error rate (BER) improvements over existing modulation schemes , under perfect channel state information (CSI), as well as superior out-of-band emissions (OOBE). Further, for the imperfect CSI case, the proposed CZT-based superimposed pilot scheme leads to significantly reduced normalized mean square error (NMSE), whilst attaining equivalent estimation accuracy to that of ODDM with lower computational complexity.

Affine frequency division multiplexing (AFDM) has recently emerged as an excellent backward-compatible 6G waveform. In this paper, we study matched filtering (MF) assisted channel estimation (CE) for AFDM systems in complex doubly selective channels. By deriving the complete input-output relationship of the continuous-time signal, the inter-chirp-carrier interference, signal-to-interference-plus-noise ratio (SINR), and the effective SINR loss of AFDM, are investigated in discrete affine Fourier transform (DAFT) domain. Further, we propose two low-complexity methods for constructing the channel matrix by taking advantage of its inherent discrete Fourier transform structure and the staircase structure of the piecewise functions in the channel matrix, respectively. It is shown that complexity reduction by at least two orders of magnitude can be achieved for a large number of chirp subcarriers. For the CE problem in doubly selective channels, we introduce an MF assisted CE scheme. This allows us to sequentially estimate the parameters of each path by exploiting the separability and approximate orthogonality of different paths in the DAFT domain, thus leading to significantly reduced complexity. Furthermore, based on generalized Fibonacci search (GFS), an MF-GFS scheme is proposed to avoid significantly redundant computation, which can be extended to typical wide-band systems. Extensive simulation results indicate that the proposed schemes offer superior advantages in terms of their improved communication performance and lower complexity.

Satellite edge computing (SEC) ignites ubiquitous computation offloading across the Earth’s surface. However, non-uniformly distributed users generate uneven traffic demands, resulting in excessive load on particular satellites and insufficient use of satellite computing resources. In this paper, we explore the possibility of cooperative SEC using inter-satellite links, where we design a two-stage cooperative SEC algorithm in satellite-terrestrial networks. Specifically, in stage one, users can offload their partial computational tasks to their directly connected satellites and locally execute the rest of the tasks. In stage two, the connected satellites further offload partial computational tasks to the terrestrial station and available satellites in a cooperative manner, especially for high-traffic satellites. We aim to minimize the system weighted-sum energy consumption during SEC by jointly optimizing task allocation, power control, bandwidth allocation, task partition, and computing resource allocation under the constraints of maximum tolerated latency, computation capacity at each satellite, total bandwidth, and maximum allowable transmission power. Furthermore, we introduce an iterative algorithm by decomposing the original non-convex problem into several sub-problems and solve each sub-problem with attempts to derive theoretical analysis. Simulation results demonstrate that our introduced cooperative SEC algorithm can efficiently use satellite computing resources and significantly reduce system weighted-sum energy consumption compared to other algorithms.

Rate-splitting multiple access (RSMA) is a key technology for next-generation multiple access systems due to its robustness against imperfect channel state information (CSI). This makes RSMA particularly suitable for high-mobility autonomous driving, where ultra-reliable and low-latency communication (URLLC) is essential. To address the stringent requirements, this study enables RSMA finite blocklength (FBL) transmissions and explicitly evaluates the ergodic performance. We derive the closed-form lower bound for the ergodic sum-rate of RSMA, considering vital factors such as the vehicle velocities, vehicle positions, power allocation of each stream, blocklengths, and block error rates (BLERs). To further enhance the ergodic sum-rate while complying with quality of service (QoS) rate constraints, we jointly optimize the global power coefficient, private power distribution, and common rate splitting. Guided by gradient descent, we first adjust the global power coefficient based on its sum-rate solution. This parameter regulates the power state of the common stream, allowing for dynamic activation or deactivation: if active, we optimize the private power distribution and adjust the common rate splitting to meet minimum transmission constraints; if inactive, we use the sequential quadratic programming for private power distribution optimization. Simulation results confirm that our RSMA scheme significantly improves the ergodic performance, reduces blocklength and BLER, surpassing the RSMA counterpart with average private power and space division multiple access (SDMA). Furthermore, our approach is validated to guarantee the rates for users with the poorest channel conditions, thereby enhancing fairness across the network.

In the upcoming next-generation wireless communication systems, high-mobility support serves as an essential part. OTFS (Orthogonal Time Frequency Space) modulation is regarded as a candidate waveform for 6G systems for its robustness in high mobility scenarios. However, its performance degrades when Doppler spreads beyond the subcarrier spacing with ultra-high speed, a condition referred to as the out-of-range Doppler (OD) channel. We analyze the input-output relationship under OD channels and reveal that the Delay-Doppler (DD) domain received signal is a superposition of the transmitted signal with an additional phase term. Based on this insight, we design a cross-frame OTFS framework for channel estimation, utilizing coprime subcarrier configurations to observe varying responses across subframes, and the out-of-range parameters are resolved using the Chinese Remainder Theorem (CRT). Furthermore, an effective channel estimation method is developed to handle multipath scenarios by decomposing subframes and matching response taps using fractional and amplitude information, which transforms the OD channel estimation problem into multiple single-domain problems by processing DD domain signals in layers. Simulation results demonstrate the effectiveness of the proposed method in estimating OD channels, outperforming existing approaches that fail under such conditions.

Orthogonal time-frequency space (OTFS) is a potential waveform for integrated sensing and communications (ISAC) systems because it can manage communication and sensing metrics in one unified domain, and has better performance in high mobility scenarios. In practice, a target might come from far distance or with ultra-high speed. However, the max unambiguous range and max tolerable velocity of OTFS-ISAC system is limited by the unambiguous round-trip delay and Doppler shift, which are related to OTFS frame, i.e., time slots and subcarrier spacing, respectively. To enlarge the sensing range, a novel OTFS cross-frame ranging and velocity estimation model as well as its corresponding method based on the Chinese remainder theorem (CRT) are proposed in this paper. By designing co-prime numbers of subcarriers and time slots in different subframes, the difference in the responses of the subframes for a target can be used to estimate the distance and velocity of an out-of-range target. Several frame structures are further designed for specific sensing scenarios, such as target with ultra-high speed or at far distance. Simulation results show that the proposed method can achieve significantly better performance in NMSE compared with the classic sensing methods under the condition of same time and frequency resources.

As an emerging flexible antenna technology for wireless communications, pinching-antenna systems offer distinct advantages in terms of cost efficiency and deployment flexibility. This paper investigates joint transmission strategies of the base station (BS) and pinching antennas (PAS), focusing specifically on how to cooperate efficiently between the BS and waveguide-mounted pinching antennas for enhancing the performance of the user equipment (UE). By jointly considering the performance, flexibility, and complexity, we propose three BS-PAS joint transmission schemes along with the best beamforming designs, namely standalone deployment (SD), semi-cooperative deployment (SCD) and full-cooperative deployment (FCD). More specifically, for each BS-PAS joint transmission scheme, we conduct a comprehensive performance analysis in terms of the power allocation strategy, beamforming design, and practical implementation considerations. We also derive closed-form expressions for the average received SNR across the proposed BS-PAS joint transmission schemes, which are verified through Monte Carlo simulations. Finally, numerical results demonstrate that deploying pinching antennas in cellular networks, particularly through cooperation between the BS and PAS, can achieve significant performance gains. We further identify and characterize the key network parameters that influence the performance, providing insights for deploying pinching antennas.

Radio Frequency Fingerprinting Identification (RFFI) is a lightweight physical layer identity authentication technique. It identifies the radio frequency device by analyzing the signal feature differences caused by the inevitable minor hardware impairments. However, existing RFFI methods based on closed set recognition struggle to detect unknown unauthorized devices in open environments. Moreover, the feature interference among legitimate devices can further compromise identification accuracy. In this paper, we propose a joint radio frequency fingerprint prediction and siamese comparison (JRFFP-SC) framework for open set recognition. Specifically, we first employ a radio frequency fingerprint prediction network to predict the most probable category result. Then a detailed comparison among the test sample's features with registered samples is performed in a siamese network. The proposed JRFFP-SC framework eliminates inter-class interference and effectively addresses the challenges associated with open set identification. The simulation results show that our proposed JRFFP-SC framework can achieve excellent rogue device detection and generalization capability for classifying devices.

The advent of Cellular Vehicle-to-Everything (CV2X) technology has revolutionised intelligent transportation systems (ITS), but poses challenges for secure and efficient data sharing due to its dynamic nature. Traditional centralised systems are inadequate, prompting the need for decentralised solutions like blockchain. However, applying blockchain technologies in C-V2X always faces scalability issues. This paper proposes a scalable C-V2X blockchain network with a hierarchical consensus by integrating a dynamic load-balancing sharding mechanism and zero-knowledge proofs (ZKPs). Our scheme ensures scalability in the C-V2X environment through sharding while utilising ZKPs to enhance cross-shard validation efficiency, reducing its complexity to$O(1)$. Additionally, our approach reduces bandwidth consumption by 90.8% compared to Merkle tree-based solutions and its consensus time is lower than 360 ms.

In this work, a novel double-covertness framework is developed for integrated sensing and communication (ISAC) systems, where both the radar and communication signals are protected from being maliciously detected by adversaries. Specifically, a new measurement named joint intercept probability (JIP) is proposed for characterizing the double-covertness performance. Next, an optimal power allocation strategy is designed to minimize the JIP, subject to certain quality-of-service (QoS) requirements for communication and sensing. Simulation results verify the effectiveness of our proposed solution and demonstrate the intrinsic relationship among power allocation, JIP and different QoS requirements.

This paper proposes a novel multi-carrier modulation scheme, orthogonal chirp delay-Doppler division Multiplexing (CDDM), tailored for future high mobility communications. The core innovation lies in spreading data symbols over chirps in the Delay-Doppler (DD) domain using a novel chirp-Zak transform (CZT), and enabling orthogonal pulse transmission within the DD plane. This approach fully leverages the advantages of both chirp signals and DD channels. Key contributions of CDDM include: efficient implementation through pre-computation techniques, synergistic exploitation of chirp signal characteristics and DD channel properties, and novel data detection via DD chirp correlation. Simulation results demonstrate that CDDM achieves significant bit error rate (BER) improvements compared to benchmarking schemes, validating its potential for high mobility communication systems.

This paper proposes a spatial chirp-based joint optimization method for analog beamforming in RIS-assisted THz ultra-wideband communication systems. The THz band (0.1-10 THz) offers significantly wider bandwidth, enabling Tbps data rates, which are crucial for 6G communications. However, traditional phase shifters are optimized for a single frequency. In ultra-wideband systems, the phase response deviates as frequency varies, leading to beam squinting problem, which significantly degrades system performance. Existing solutions, such as true-time-delay architectures, impose excessive complexity, making them impractical for THz systems. In this work, we propose leveraging polynomial expansion to optimize the spatially dependent phase functions of RIS, Tx, and Rx. We formulate the optimization problem as maximizing system throughput, and develop a gradient ascent-based iterative algorithm to solve it. Simulation results demonstrate that our method outperforms conventional and state-of-the-art approaches in metrics including phase response, throughput, and BER, providing a cost-effective solution for optimizing analog beamforming.

This paper presents a priority random access (PRA) with non-orthogonal multiple access (NOMA)-ALOHA (PRANA) scheme to enable access priority control for machine-type devices (MTDs) with delay-sensitive and delay-tolerant requirements. To enhance the energy efficiency of random access, we also introduce a channel quality-based user barring algorithm (CQ-UBA) that can alleviate the user overload problem and reduce the average transmit power of MTDs. By extending CQ-UBA to the proposed PRA-NA scheme, simulation results demonstrate that the random access performance in terms of access delay and energy efficiency for delay-sensitive MTDs can be significantly improved compared to the conventional NOMA-ALOHA.

In this paper, a novel artificial noise (AN)-aided secure and covert integrated sensing and communication (ISAC) framework is established for uncrewed aerial vehicle (UAV) systems, to against malicious radar signal detection and communication eavesdropping. Specifically, we consider that besides the communication and sensing signals, the AN signal, which is used to interfere with the eavesdropper and conceal the existence of radar signal, will be transmitted by the UAV-enabled base station (UBS) with uncertainty on its power level. The closed-form expressions of intercept probability (IP) as well as the minimum detection error probability (M-DEP) are derived. Moreover, an efficient communication and sensing performance maximization strategy is designed by optimizing the beamforming vector of communication, covariance matrix of sensing, and UBS receiver filter jointly, to satisfy the IP, power and M-DEP constraints. Simulation results are provided to verify the effectiveness of our joint design by comparing it to benchmark strategy. Moreover, the impact of AN power uncertainty is examined via simulations.

Abstract Reconfigurable intelligent surfaces (RISs) have emerged as propitious solution to configure random wireless channel into suitable propagation environment by adjusting a large number of low‐cost passive reflecting elements. It is considered that narrowband downlink millimeter wave (mmWave) multiple‐input multiple‐output (MIMO) communication is aided by deploying an RIS. Large antenna arrays are used to counter the huge propagation loss suffered by the mmWave signals. Hybrid precoding in which precoding is performed in digital and analog domains is employed to reduce the number of costly and power‐consuming radio frequency (RF) chains. Passive beamforming at RIS is designed together with precoder and combiner through joint optimization problem to minimize the mean square error between the transmit signal and the estimate of signal at the receiver. The optimization problem is solved by an iterative procedure in which solution to the non‐convex reflecting coefficients design problem is approximated by extracting the phases of the solution to unconstrained problem without unit amplitude constraint of the reflecting elements. It is shown that the proposed design principle also applies to the wideband channel. Simulation results show that the proposed design delivers performance better than existing state‐of‐the‐art solutions, but at lower complexity.

Low peak-to-average power ratio (PAPR) orthogonal frequency division multiplexing (OFDM) waveform design is a crucial issue in integrated sensing and communications (ISAC). This paper introduces an OFDM-ISAC waveform design that utilizes the entire spectrum simultaneously for both communication and sensing by leveraging a novel degree of freedom (DoF): the frequency-domain phase difference (PD). Based on this concept, we develop a novel PD-based OFDM-ISAC waveform structure and utilize it to design a PD-based Low-PAPR OFDM-ISAC (PLPOI) waveform. The design is formulated as an optimization problem incorporating four key constraints: the time-frequency relationship equation, frequency-domain unimodular constraints, PD constraints, and time-domain low PAPR requirements. To solve this challenging non-convex problem, we develop an efficient algorithm, ADMM-PLPOI, based on the alternating direction method of multipliers (ADMM) framework. Extensive simulation results demonstrate that the proposed PLPOI waveform achieves significant improvements in both PAPR and bit error rate (BER) performance compared to conventional OFDM-ISAC waveforms.

In this work, we investigate an active reconfigurable intelligent surface (RIS)-aided non-orthogonal multiple access (NOMA)-enabled space-air-ground integrated network (SAGIN) with cognitive radio, leveraging the flexible deployment of an unmanned aerial vehicle (UAV) and the ubiquitous coverage of satellite networks. The UAV serves uplink and downlink users in the secondary network via NOMA and time division multiple access mechanisms, respectively, while satellites provide wireless backhaul for the UAV and primary users. We aim to maximize the weighted sum mean rate and energy efficiency for the secondary network by jointly the optimizing power allocation, the RIS reflection coefficients (RC), the user matching factors, and the UAV trajectory. We propose an alternating optimization framework based on the block coordinate ascent (BCA) technique, which decouples the problem into multiple variable blocks for alternating optimization until convergence. Moreover, we investigate the performance of energy-efficient active RIS with a sub-connected architecture, decoupling the RIS RC optimization into amplification factor and phase shift subproblems to be solved separately. Finally, simulation results validate the effectiveness of the proposed schemes, and demonstrate weakness of passive RIS and rationality and economics of sub-connected active RIS architecture.

Modern wireless systems face interference due to rising spectrum efficiency demands and increasingly aggressive network designs. Despite its optimality, the huge complexity of the maximum likelihood (ML) detection hinders its deployment in the future wireless communication systems, which require low latency and high energy efficiency. In this paper, we develop a novel generalized framework for data detection in interference channels. In particular, we factorize the joint likelihood function of the transmitted symbols to obtain the marginal distribution of a single symbol following the sum-product (SP) algorithm. Motivated by the fact that the complexity of the SP algorithm is dominated by the summation process, we introduce Gaussian and Gaussian mixture models to reduce the state space of symbols, which helps to reduce the detection complexity. The proposed hybrid detection framework consists of three kinds of symbol distributions, i.e., original discrete, Gaussian, and Gaussian mixture distributions. To strike a balance between complexity and error performance, we can simply modify the components of different symbol distributions, offering high flexibility in practical applications. Furthermore, we analyze the performance of our proposed detection scheme and discuss the design guidelines for the mixture Gaussian messages. Simulation results demonstrated the effectiveness of the proposed algorithm.

Integrated sensing and communication (ISAC) holds significant commercial potential in future 6G. The 3GPP technical report indicates that it is crucial to prioritize the sensing services of the ISAC system to ensure the efficient execution of critical services due to resource-constrained environments. Thus, this paper investigates priority-based resource allocation in ISAC systems, where an ISAC base station (BS) simultaneously serves a communication user (CU) and detects both a primary sensing target (PST) and a secondary sensing target (SST). First, A priority criteria for this ISAC system is proposed: 1) Above all, to ensure that the Cramer-Rao bound (CRB) for PST is maintained below a threshold; 2) Following this, the CRB of the SST should be optimized, while ensuring the communication spectral efficiency is preserved. Then, based on this criterion, three cases are derived, and for each case, a distinct resource allocation problem is formulated, yielding closed-form or semi-closed-form expressions of the optimal resource allocation for each problem. Furthermore, An algorithm is also proposed to determine the occurrence of each case and provide the corresponding optimal resource allocation. In addition, system performance under each case is analyzed based on these expressions. Finally, the simulation results demonstrate the impact of the priority strategy on performance, which is consistent with the previous performance analysis.

In this paper, by introducing nested parity-check (PC) functions, a new polarizing matrix extension-based hybrid automatic repeat request (PME-HARQ) scheme, called PC-PME-HARQ, is proposed. Such nested PC functions are constructed through a fixed-length cyclic shift register. This approach significantly enhances error correction capability, particularly for short and medium-length packet transmissions. Besides, a dynamic subchannel mapping and optimization framework is developed, which integrates subchannel reliability and distance spectrum to optimize bit-set configurations for varying transmission conditions. Furthermore, the information set approximation puncturing (ISAP) and reliability score-based puncturing (RSP) algorithms have been modified and optimized to outperform the traditional quasi-uniform puncturing (QUP) algorithm, providing improved rate compatibility and reducing Block Error Rate (BLER) in PME-HARQ systems. Simulation results demonstrate that the proposed PC-PME-HARQ scheme achieves substantial performance gains, offering both coding and diversity gains across multiple retransmissions, effectively surpassing the baseline schemes.

Pilot sequence design over doubly selective channels (DSC) is challenging due to the variations in both the time- and frequency-domains. Against this background, the contribution of this paper is twofold: Firstly, we investigate the optimal sequence design criteria for efficient channel estimation in orthogonal frequency division multiplexing systems under DSC. Secondly, to design pilot sequences that can satisfy the derived criteria, we propose a new metric called oversampled ambiguity function (O-AF), which considers both fractional and integer Doppler frequency shifts. Optimizing the sidelobes of O-AF through a modified iterative twisted approximation (ITROX) algorithm, we develop a new class of pilot sequences called “oversampled low ambiguity zone (O-LAZ) sequences”. Through numerical experiments, we evaluate the efficiency of the proposed O-LAZ sequences over the traditional low ambiguity zone (LAZ) sequences, Zadoff-Chu (ZC) sequences and m-sequences, by comparing their channel estimation performances over DSC.

In this paper, we consider the physical layer security (PLS) problem for integrated sensing and communication (ISAC) systems in the presence of hybrid-colluding eavesdroppers, where an active eavesdropper (AE) and a passive eavesdropper (PE) collude to intercept the confidential information. To ensure the accuracy of sensing while preventing the eavesdropping, a base station transmits a signal consisting of information symbols and sensing waveform, in which the sensing waveform can be also used as artificial noise to interfere with eavesdroppers. Under this setup, we propose an alternating optimization-based two stage scheme (AO-TSS) for improving the sensing and communication performance. In the first stage, based on the assumptions that the perfect channel state information (CSI) of the AE and statistical CSI of the PE are known, the communication and sensing beamforming problem is formulated with the objective of minimizing the weighted sum of the beampattern matching mean squared error (MSE) and cross-correlation, subject to the secure transmission constraint. To tackle the non-convexity, we propose a semi-definite relaxation (SDR) algorithm and a reduced-complexity zero-forcing (ZF) algorithm. Then, the scenarios are further extended to more general cases with imperfect AE CSI and unknown PE CSI. To further improve the communication performance, the second-stage problem is developed to optimize the secrecy rate threshold under the radar performance constraint. Finally, numerical results demonstrate the superiority of the proposed scheme in terms of sensing and secure communication.

In this paper, aimed to protect the transmitted waveform from being intercepted by advanced electronic support measure systems, a low probability of intercept (LPI) signal design, taking into account the transmitted waveform and precoder of communication users, is developed for multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) systems. The considered system simultaneously senses multiple targets and communicates with multiple users in the presence of various interferences. Specifically, based on the analysis of cyclic spectrum, the desired LPI performance is achieved by minimizing the cyclic frequency sidelobe level. Moreover, sensing and communication requirements are satisfied by respectively maximizing the minimum signal-to-interference-plus-noise ratio (SINR) of targets and constraining the received SINR lower bound of each user. Energy budget and peak-to-average power ratio constraints are introduced to meet the practical resources limits, while similarity constraint is constructed to further ensure the target sensing. Next, in order to solve the formulated nonconvex problem, an efficient iterative algorithm is proposed based on the idea of successive convex approximation (SCA). Finally, simulation results demonstrate the effectiveness of our proposed scheme and display the trade-off among LPI, sensing and communication performance.

In distributed edge networks employing frequency division duplex, the feedback of channel state information (CSI) from the edge devices to the edge server always consumes a lot of spectrum resources, resulting in a serious communication burden. In this paper, we first propose an end-to-end unfolding neural network framework inspired by the soft threshold iterative algorithm (U-ISTANet). The proposed U-ISTANet integrates the advantages of compression awareness and neural networks. Especially, the compression matrix and sparse transformation of channel matrix can be learned for accurate CSI compression and recovery. And a lightweight version of U-ISTANet, called U-ISTANet-L, is proposed to reduce the training parameters. To reduce the data transmission overhead in the centralized learning framework, we extend the proposed U-ISTANet-L to a federated U-ISTANet-L (FU-ISTANet-L), which can train a more generalizable model by increasing the number of edge devices to enlarge the data set in a distributed learning manner. The proposed FU-ISTANet-L reduces the transmission overhead and increases the training speed while achieving a performance close to that of centralized learning. Furthermore, we propose a personalized FU-ISTANet-L (P-FU-ISTANet-L) to solve the heterogeneous data training problem in different communication environments. Specifically, we first obtain a pre-trained model by federation unfolding learning, and then each edge device fine-tunes the model using only a small amount of train data to obtain a personalized model for local channel environment. Extensive experimental results are provided to show that the proposed networks achieve a significant performance over the benchmarking schemes in terms of the normalized mean square error.

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.

This paper studies Doppler resilient complementary sequences (DRCSs) whereby the ambiguity functions (AFs) of multiple element sequences are summed to attain low/zero AF values. We first derive a set of AF lower bounds for unimodular DRCS sets, which include the existing bounds on AFs as special cases. These bounds may be used as theoretical design guidelines to measure the optimality of DRCS sets against Doppler effect. In addition, we introduce some constructions of DRCS sets based on mathematical tools such as orthogonal matrices, circular Florentine rectangles and difference sets, which can generate the optimal DRCS set. Finally, we evaluate the feasibility of DRCSs for pulse train waveform design.

Nowadays, cross-device federated learning (FL) is the key to achieving personalization services for mobile users and has been widely employed by companies like Google, Microsoft, and Alibaba in production. With the explosive growth in the number of participants, the central FL server, which acts as the manager and aggregator of cross-device model training, would get overloaded, becoming the system bottlenecks. Inspired by the emerging wave of edge computing, an interesting question arises:Could edge clouds help cross-device FL systems overcome the bottleneck?This article provides a cautiously optimistic answer by proposingINP, a FL-specific In-Network Processing framework to achieve the goal. As in-network processing has broken the end-to-end principle of the involved communication and lacks the support of transport protocols, the key is to design domain-specific transport protocols forINP. To fill the gap, we propose the novel Model Download Protocol ofmdpand Model Upload Protocol ofmup. Withmdpandmup, edge cloud nodes along the paths inINPcan easily eliminate duplicated model downloads and pre-aggregate associated gradient uploads for the central FL server, thus alleviating its bottleneck effect, and further accelerating the entire training progress significantly.

Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided that workers will eventually participate in the synchronizations,$i)$the training still converges, even if only$p$workers take part in each round of synchronization, and$ii)$a larger$p$generally leads to a faster rate of convergence. These findings shed light on eliminating the bottleneck effects of parameter synchronization in large-scale data-parallel distributed training and have motivated several optimization designs. In this paper, we focus on optimizing the parameter synchronization forpeer-to-peerdistributed learning, where workers broadcast or multicast their updated parameters to others for synchronization, and proposeSelMcast, a suite of expressive and efficient multicast receiver selection algorithms, to achieve the goal. Compared with the state-of-the-art (SOTA) design, which randomly selects exactly$p$receivers for each worker’s multicast in a bandwidth-agnostic way,SelMcastchooses receivers based on the global view of their available bandwidth and loads, yielding two advantages, i.e., accelerated parameter synchronization for higher utilization of computing resources and enlarged average$p$values for faster convergence. Comprehensive evaluations show thatSelMcastis efficient for both peer-to-peer Bulk Synchronous Parallel (BSP) and Stale Synchronous Parallel (SSP) distributed training, outperforming the SOTA solution significantly.

The advent of reconfigurable intelligent surfaces (RIS) introduces a novel paradigm to address mutual interference issues within radar-communication coexistence (RCC) systems. However, the predominantly discrete reflection coefficients (DRC) of most real-world RISs pose new challenges for the design of interference-mitigating reflection coefficients. This paper investigates a RIS-assisted RCC system with DRC. First, a radar SINR maximization problem under the constraints of communication signal-to-interference plus noise ratio (SINR) requirements is formulated to obtain feasible designs for the DRC and radar beamforming vectors. Then, an alternating optimization approach is applied, decomposing the original problem into two sub-problems. For the DRC optimization sub-problem, a design approach based on semidefinite relaxation (SDR) and Gaussian randomization is proposed. For the radar beamforming sub-problem, the closed-form expressions for the optimal radar beamforming vectors are obtained by the generalized Rayleigh quotient and the Karush-Kuhn-Tucker (KKT) conditions. Finally, it is demonstrated that within a communication-centric scenario, the RIS needs to maintain orthogonality between the interference channel and the radar-target channel in order to achieve the upper bound of the radar SINR. Based on this conclusion, a low-complexity approach is proposed to optimize the radar SINR in a communication-centric scenario.

The advent of sixth-generation (6G) wireless communication systems represents a transformative leap in global connectivity, moving from traditional internet of things (IoT) frameworks to an advanced artificial intelligence of things (AIoT) paradigm. This evolution presents significant challenges, primarily due to exponential growth in data volume, complexity, and latency requirements. To address these challenges, over-the-air (OTA) computation has emerged as a breakthrough approach by integrating computational processes directly into the communication framework, overcoming the inefficiencies of traditional separate designs. In this study, we explore the integration of OTA computation with reconfigurable intelligent surfaces (RISs) within multi-cell multiple-input multiple-output (MIMO) networks. RIS technology enhances signal propagation, mitigates interference, and optimizes wireless coverage, thereby complementing the OTA computation paradigm’s ability to facilitate real-time data aggregation and processing. Specifically, we propose a novel joint optimization framework aimed at minimizing the mean squared error (MSE) in multi-cell environments. This framework addresses the complexity of beamforming design through an innovative power-iteration-based majorization-minimization approach and a successive alignment technique. In addition, we perform asymptotic analysis to elucidate the performance benefits of large-scale MIMO and RIS configurations. We also consider the fairness of MSE computation throughout the multi-cell system. Finally, numerical simulations validate the effectiveness of the proposed methods and provide additional insights based on the asymptotic analysis.

In this paper, aimed to against spoofing attack, we propose a novel physical layer authentication (PLA) framework for unmanned aerial vehicle (UAV) communication networks under Rayleigh and Rician channels. A new PLA metric, called authentication distance (AD), is defined by jointly considering the geographical locations, elevation angles, and channel randomness between a legitimate sensor and a malicious spoofer. For Rayleigh channel in dense urban environment, the closed-form expressions for the false alarm probability (FAP) and miss detection probability (MDP) are obtained by adopting method of convolution and integration by parts. Next, the PLA hypothesis test model with Rician channel is established in suburban environment where both the Rician factor and the path loss exponent are functions of UAV altitude. To proceed, the expressions for the FAP and MDP are derived based on the doubly non-centralFdistribution. In addition, MDP minimization solutions subject to certain FAP requirement are developed in both Rayleigh and Rician channels by optimizing the detection threshold and UAV altitude jointly. Simulation results show that our derived analytical expressions of FAP and MDP match the Monte Carlo simulations well. Moreover, simulation results also imply the effectiveness of the proposed PLA framework for UAV communication networks.

Massive machine-type communications (mMTC) are anticipated to be supported by grant-free random access (GF-RA) and multiple-input multiple-output (MIMO) techniques. Compressed sensing (CS) is extensively advocated to accommodate massive connectivity due to bursty data transmission. In this paper, we formulate the joint activity detection and channel estimation for the MIMO-enabled GF-RA system as a block single measurement vector (SMV) problem. First, the hierarchical block sparse Bayesian learning (BSBL) framework is developed to solve this problem, where the potential block sparse properties induced by multiantenna reception are exploited by assigning the structured hyperprior. Then, by integrating the generalized approximate message passing (GAMP) approach into the BSBL formulation to effectively approximate the posterior distribution, we propose a computationally efficient Bayesian learning algorithm named GAMP-BSBL. Fortunately, the proposed Bayesian algorithms enable automatic learning of block sparse solutions without requiring noise level and user sparsity ratio as explicit conditions. Simulation results show that the proposed algorithms provide improved performance gains as compared with the standard CS methods.

The vehicular networks are envisioned to support the future intelligent transportation system (ITS). Content dissemination is vital to achieving efficient data traffic management and real-time decision making. Limited resources constrain the vehicles and edge network, but the vehicle-to-vehicle (V2V) communication technology can support the conventional cellular communication link in content dissemination. To explore the full benefit of V2V, relay vehicles can be selected to support direct V2V links in situations where direct communication is not feasible due to distance or shadowing. However, not all vehicles will agree to participate in relaying without any reward. To counter this problem, we introduce an incentive mechanism to entice vehicles to assist in relaying. The trustworthiness between users is considered when establishing a V2V communication link. We jointly consider the content distribution mode and relay selection problems to maximize system utility. Moreover, improved auction algorithms were proposed to improve user utility. The proposed scheme is compared against some closely related baseline schemes.

Deep-learning (DL)-based channel state information (CSI) feedback has the potential to improve the recovery accuracy and reduce the feedback overhead in massive multiple-input-multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems. However, the length of input CSI and the number of feedback bits should be adjustable in different scenarios, which cannot be efficiently achieved by the existing CSI feedback models. Therefore, an adaptive bidirectional long short-term memory network (ABLNet) for CSI feedback is first designed to process various input CSI lengths, where the number of feedback bits is in proportion to the CSI length. Then, to realize a more flexible feedback bit number, a feedback bit control unit (FBCU) module is proposed to control the output length of feedback bits. Based on which, a target feedback performance can be adaptively achieved by a designed bit number adjusting (BNA) algorithm. Furthermore, a novel separate training approach is devised to solve the model protection problem that the user equipment and next-generation nodeB are from different manufacturers. Experiments demonstrate that the proposed ABLNet with FBCU can fit for different input CSI lengths and feedback bit numbers; the CSI feedback performance can be stabilized by the BNA algorithm; and the proposed separate training approach can maintain the feedback performance and reduce the complexity of the feedback model.

Edge computing is an efficient way to offload computational tasks for user equipment (UE) which has computation-intensive and latency-sensitive tasks in certain applications. However, UEs can not offload to ground edge servers when they are in remote areas. Mounting edge servers on low earth orbit (LEO) satellites can provide remote UEs with task offloading when the ground infrastructure is not available. In this paper, we introduce a multi-satellite-enabled edge computing system for offloading UEs’ computational tasks with the aim of minimizing system energy consumption by optimizing users’ association, power control, task scheduling, and computing resource allocation. Specifically, a UE’s partial task is executed locally and the rest of its task is offloaded to a satellite for processing. Such energy minimization problem is formulated as a mixed-integer nonlinear programming (MINLP) optimization problem. By decomposing the original problem into four sub-problems, we solve each sub-problem with convex optimization methods. In addition, an iterative algorithm is proposed to jointly optimize the task offloading and resource allocation strategy, which achieves a near optimal solution through several iterations. Finally, the complexity and convergence of the algorithm are verified. In our simulation results, the proposed algorithm is compared with different task offloading and resource allocation schemes in terms of system energy consumption, where 43% energy is saved.

In this paper, a general class of polyphase sequences called Generalized Zadoff-Chu (GZC) sequences is presented, which is based on a quadratic function with real-valued coefficients. The conventional ZC sequences, P3, and P4 codes are included as special cases of GZC sequences. It is shown that, with the help of grid search algorithm, the optimized GZC sequences have significantly lower PMEPR than the well-known sequences such as Golay sequences, m-sequences, P3 and P4 codes, and ZC sequences. Numerical simulations suggest that the minimum PMEPR tends to a small constant of less than 1.43 dB as the sequence length approaches infinity.

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user equipment (UE) and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle. Firstly, an efficient transmission structure for the ISAC scheme is developed, where a number of training sequences with orthogonal precoders and combiners are respectively utilized at BS and RSUs for channel parameter extraction. Then, the near-field static channel model between the STAR-RIS and in-vehicle UE as well as the far-field time-frequency selective BS-RIS-RSUs channel model are characterized. By utilizing the multidimensional orthogonal matching pursuit (MOMP) algorithm, the cascaded channel parameters (i.e., the delays, the Doppler frequency shifts, the angles of arrivals, and the angles of departure of the scattering paths) of the BS-RIS-RSUs links can be obtained at the RSUs. Thus, the vehicle localization and its velocity measurement can be acquired by jointly utilizing these extracted cascaded channel parameters of all RSUs. Note that the MOMP algorithm can be further utilized to extract the channel parameters of the BS-RIS-UE link for communication service. With the help of sensing results, the reflection and refraction phase shifts of the STAR-RIS are delicately designed, which can significantly improve the received signal strength for both the RSUs and the in-vehicle UE, and can finally enhance the sensing and communication performance. Moreover, the trade-off design for sensing and communication is proposed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

The Software Defined Network (SDN)-based space-ground integrated railway communication networks have attracted widespread attention from academia and industry. In such environments, the security of initial authentication and handover authentication for moving trains are two important challenges that need to be addressed. In this paper, a secure and efficient authentication key agreement scheme is proposed for the SDN-based space-ground integrated railway networks. Specifically, a lightweight mutual authentication mechanism based on the Number Theory Research Unit (NTRU) is proposed for the initial authentication process, which effectively prevents the unauthorized On-Board Unit (OBU) accessing networks. Then, according to the predictable path, we propose a key generation algorithm based on the hash chain and a fast key distribution mechanism based on the Chinese Remainder Theorem (CRT), which greatly reduce the calculation and communication burden of the key transmission process. On this basis, we adopt a hash-based message authentication code to achieve unified handover authentication in heterogeneous integrated railway networks. The Burrows-Abadi-Needham (BAN) logic proof and informal security analysis demonstrate that the proposed scheme can provide several robust security properties, including forward/backward security, universality, traceability, and resistance against quantum attacks. The performance evaluations show that our scheme outperforms other related schemes in computation cost, communication overhead, and performance under unknown attacks while guaranteeing higher security.

Cellular-based networks are deemed as one solution to provide communication links for the internet of things (IoT) due to its high reliability and wide coverage. However, due to the overloaded machine-type devices in IoT, the existing random access procedure in cellular networks suffers significant preamble collision problem and hardly meets the requirement of large random access. Despite the effort to cope with the preamble collision problem in conventional random access control schemes, other important performance requirements in random access are not well addressed, including access delay, energy consumption, and service priority. To improve the random access control scheme, we propose a novel hierarchical hybrid (HH) access class barring (ACB) and back-off (BO) scheme (HH ACB-BO scheme), where the hybrid ACB-BO is exploited to balance the delay-energy tradeoff, and the hierarchical structure is proposed to prioritize communication services. We mathematically formulate this random access control scheme to optimize the delay and energy performance jointly. With the fixed priority weights for each service priority, the closed-form of the optimal ACB factors and BO indicators adjustment result is derived. Moreover, in order to realize the adaptive prioritized random access control, we apply deep reinforcement learning (DRL) to the proposed random access scheme to dynamically adjust the ACB factors and BO indicators in an online manner. Considering the hierarchical structure and the action space complexity in DRL, a multi-agent DRL algorithm is designed for the HH ACB-BO scheme (multi-agent HH-DRL algorithm), where online policy transfer is applied to guarantee the policy effectiveness in the practical networks. Finally, simulation results verify the effectiveness of the proposed HH ACB-BO scheme and reveal that the multi-agent HH-DRL algorithm outperforms other algorithms in terms of average access success probability and energy consumption performance.

In an attempt to alleviate the cost and power consumption in millimeter wave (mmWave) multiple input multiple output (MIMO) systems, hybrid precoding has been put forth as a possible solution. The hybrid precoding replaces the conventional fully digital precoding with a combination of analog and digital precoding to achieve good performance using a significantly smaller number of RF chains compared to the number of antennas. In this paper, we consider a narrowband downlink multiple user multiple-input single-output (MU-MISO) system and propose a hybrid precoding method in order to minimize the mean squared error (MSE) for all users. The analog and digital precoding design subproblems are isolated from each other. Analog precoder design is cast as a trace maximization problem, and solved by an iterative procedure based on truncated singular value decomposition (SVD). The digital precoder is determined subsequently after fixing the analog precoder, using Lagrange’s method. The proposed hybrid precoder produces spectral and bit error rate (BER) performances quite close to fully digital precoder, and almost the same as existing high performance hybrid precoders, albeit at a much lower complexity. The proposed precoding method is extended to operate in wideband channel, where it exhibits equally good performance with less complexity.

The orthogonal time-frequency space (OTFS) modulation is an effective technique to deal with the high-mobility challenge in vehicular wireless communications, whose data detection depends heavily on accurate channel estimation (CE). In pilot-aided CE, reducing guard symbols can achieve higher spectral efficiency. However, data interference is inevitable, especially in fractional Doppler channels. Therefore, this paper investigates the impact of data interference on CE, and aims to mitigate such data interference by adding a non-rectangular window in the time-frequency (TF) domain. To fulfill this goal, a Cramer-Rao lower bound (CRLB) is derived by considering the presence of data interference and a non-rectangular window for CE in OTFS. Different from the existing CRLB analysis for embedded-pilot OTFS, data interference is considered and treated as noise interference, resulting in a tighter derived CRLB that effectively reflects the impact of data interference on CE. By minimizing the derived CRLB with the rectangular window, a new pilot sequence is obtained, which can achieve lower CRLB and better BER performance compared to the Zadoff-Chu (ZC) sequence. On the other hand, windowing can loosen the CRLB due to its ability to suppress data interference. Under the same main lobe width, it is found that the Kaiser window and Slepian window are more effective in reducing data interference than Dolph-Chebyshev (DC) window and rectangular window. Our simulation results indicate that the interference from data to the pilot can be significantly reduced by employing appropriate windowing techniques, and the windowing functions employed play an important role in pilot-aided CE.

Orthogonal time frequency space (OTFS) is a novel modulation scheme to handle the high Doppler effect under time-varying channels. In this paper, in order to improve channel estimation accuracy and to reduce pilot overhead, two types of two-dimensional (2D) pilots and the corresponding matched filters are designed for multi-antenna OTFS systems. Our 2D pilots are formed using perfect arrays (such as Frank array, Chu array, etc) or perfect-sequence based Kronecker array (PKA). Different from the previous multi-antenna OTFS pilots, these 2D pilots are placed on the same area in the delay-Doppler domain, using code division multiplexing to deal with the interference between pilots of different antennas, known as pilot pollution. To improve the channel estimation performance, matched filters are also designed to better compensate the phase shift of the 2D pilot response in the delay-Doppler domain. Compared with the conventional multi-antenna OTFS schemes, the proposed scheme achieves significantly better NMSE performance, while having lower pilot overhead under multi-antenna scenarios.

A robust transmission scheme for intelligent reflecting surface (IRS) aided cognitive radio systems is designed to provide security against internal eavesdropping. By considering the secondary receiver as an internal eavesdropper, a total transmit power (TTP) minimization problem is formulated. Through a novel optimization strategy which jointly considers the transmit beamforming vector at the primary transmitter, the transmit beamforming vector at the secondary transmitter and the phase shifts at the IRS, a solution to the formulated problem is presented. By considering perfect channel state information (CSI) and imperfect CSI scenarios, two optimization algorithms are proposed both aiming at reducing the TTP. For the former scenario, instead of applying the commonly used inner approximation (IA) algorithm which cannot handle maximum allowable leaked rate constraint, a novel penalty-based IA algorithm is proposed. For the latter scenario, a non-convex robust optimization problem is formulated. Because of its non-convex nature, a novel upper-bounding technique and S-procedure are used to transform it into a more tractable form which is then solved by deriving of a penalty convex-concave procedure based alternating optimization algorithm. Performance results show that, as compared to other baseline schemes, the proposed algorithms significantly reduce the TTP.

Spatial non-stationarity channel estimation for uplink massive MIMO systems can be formulated as a non-uniform block sparse signal recovery problem, in which the hierarchical sparsity of the channel matrix is classified as row sparsity and in-row sparsity. This paper proposes an efficient threshold-enhanced hierarchical estimation (TEHE) algorithm without prior information. More precisely, the non-zero rows of spatial non-stationarity channel matrix are estimated according to the in-row correlation in the first layer; while the non-zero elements of the estimated non-zero rows are further refined in the second layer. Different from the existing two-layer iteration algorithms, an adaptive threshold is designed to estimate the non-zero elements replacing the iterative algorithm in the second layer. In the proposed TEHE algorithm, row-wise sparse adaptive matching pursuit (SAMP) is used to find the non-zero rows in the first layer, which has high precision and lower complexity, compared to the conventional SAMP. To further improve the efficiency of the row estimation for larger antenna array, an adaptive threshold-enhanced hierarchical estimation (A-TEHE) algorithm is proposed. In addition, a sufficient condition and a halting condition for theoretical guarantee to obtain accurate row estimation are developed. Finally, the computation complexity is analyzed and compared. The simulation results demonstrate that the proposed threshold-enhanced hierarchical spatial non-stationary channel estimation algorithms achieve better performance compared to various state-of-the-art baselines in terms of support set estimation, channel coefficient estimation, and computational efficiency. Specifically, the proposed algorithms are robust to the in-row sparsity.

Grant-free (GF) low-density signature orthogonal frequency division multiplexing (LDS-OFDM) is one of the most generic grant-free non-orthogonal multiple access (GF-NOMA) schemes. This paper proposes a two-stage active user detection (AUD) consisting of the initial AUD stage and the false alarm correction stage. In the initial AUD stage, an initial active user set is efficiently estimated by the conventional cover decoder, which may still contain a few false alarms. Hence, a false alarm correction stage is further invoked, which consists of two cooperative and iterative detection components, namely, message passing algorithm (MPA) based data decoder and belief propagation (BP) based false alarm corrector. Based on users’ signature spreading sequences in the initial active user set, a tanner graph with potential redundant-edges is constructed, on which MPA is executed for data decoding. In turn, with the aid of decoded data symbols, the remaining false alarms in the initial active user set are further removed by the false alarm corrector. Hence, the tanner graph could evolve iteratively. Furthermore, the false alarm performance of the initial AUD stage is theoretically analyzed. Based on this analysis, the LDS matrix used in GF LDS-OFDM is optimized. Finally, the complexity of our proposal is also provided.

Integrated sensing and communication (ISAC) has become a promising technology for future communication system. In this paper, we consider a millimeter wave system over high mobility scenario, and propose a novel simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) aided ISAC scheme. To improve the communication service of the in-vehicle user and simultaneously track and sense the vehicle with the help of nearby roadside units (RSUs), a STAR-RIS is equipped on the outside surface of the vehicle to transmit and reflect the signal from the base station (BS). Firstly, an efficient transmission structure for the ISAC scheme is designed. Then, the time-frequency selective BS-RIS-RSUs channel model are characterized. Based on the estimated cascaded channel parameters (i.e., the delays, the Doppler frequency shifts, the angles of arrivals, and the angles of departure of the scattering paths) of the BS-RIS-RSUs links, the vehicle localization and its velocity can be acquired. With the help of sensing results, the reflection and refraction phase shifts of the STAR-RIS are designed for performance enhancememt. Moreover, the trade-off design for sensing and communication is proposed by optimizing the energy splitting factors of the STAR-RIS. Finally, simulation results are provided to validate the feasibility and effectiveness of our proposed STAR-RIS aided ISAC scheme.

This paper proposes a hierarchical sparse estimation of spatial non-stationarity channel for uplink massive multiple-input multiple-output (MIMO) systems without prior information. Especially, the non-zero rows of non-stationarity channel matrix are estimated according to the in-row correlation in the first layer; while the non-zero elements of the estimated non-zero rows are further refined in the second layer. A row-wise sparse adaptive matching pursuit (SAMP) is used to find the non-zero rows in the first layer of the proposed algorithms, and multiple non-zero rows can be estimated in one iteration, which has higher precision and lower complexity, compared to the conventional SAMP. Different from the existing two-layer iteration algorithms, a threshold is designed to estimate the non-zero elements replacing the iterative algorithm in the second layer. Further, the computation complexity is analyzed and compared. The simulation results demonstrate that the proposed threshold-enhanced hierarchical spatial non-stationary channel estimation algorithms achieve better performance compared to various state-of-the-art baselines in terms of channel coefficient estimation, and computational efficiency.

Orthogonal time frequency space (OTFS) based integrated sensing and communication (ISAC) is a potential technology for unmanned aerial vehicles (UAVs) navigation as it can provide high mobility communication and targets sensing simultaneously. However, in OTFS based ISAC system, we will get different delays and Doppler shifts corresponding to one target if more than one single symbol is used as sensing signal in the delay Doppler (DD) domain. Thus the delays and Doppler shifts are overlapped, introducing mutual interferences. In this paper, a multi-symbol compressive sensing model is proposed to combine the overlapped delays and Doppler shifts, leading to a compressive sensing framework for OTFS based ISAC system. This model reveals the relationship between sensing parameters and frame structure, and the resolution of sensing parameters is adjustable. We also show that the sensing performance influenced by frame structure could be analyzed based on this model. Simulations show the effectiveness.

The recently proposed orthogonal time frequency space (OTFS) modulation multiplexes data symbols in the delay-Doppler (DD) domain. Since the range and velocity, which can be derived from the delay and Doppler shifts, are the parameters of interest for radar sensing, it is natural to consider implementing DD signal processing for radar sensing. In this paper, we investigate the potential connections between the OTFS and DD domain radar signal processing. Our analysis shows that the range-Doppler matrix computing process in radar sensing is exactly the demodulation of OTFS with a rectangular pulse shaping filter. Furthermore, we propose a two-dimensional (2D) correlation-based algorithm to estimate the fractional delay and Doppler parameters for radar sensing. Simulation results show that the proposed algorithm can efficiently obtain the delay and Doppler shifts associated with multiple targets.

The advancements in vehicular networks and applications brings about a huge demand for vehicles to process computation-intensive tasks such as machine learning (ML) algorithms. There is a need for an efficient solution to meet up these challenges. Federated learning (FL), which is distributed and does not require the transmission of dataset to the server, is more suitable to the resource constraint vehicular edge network. However, not all vehicles will participate in FL training without any reward. This paper proposes an incentive-based federated learning scheme for vehicular networks. Vehicles participating in federated learning are selected using an auction algorithm based on maximized social welfare. Simulations are conducted to evaluate the performance of the proposed scheme.

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on the centralized cell-free massive MIMO network over spatially correlated fading channels. The minimum mean squared error (MMSE) estimator is employed to acquire the channel state information through the established ADC quantization model. Our results show that both imperfect ADCs and pilot contamination will cause error floors. Then, the optimal receive combining is established to maximize the uplink ergodic channel capacity. Furthermore, a closed-form expression for the achievable uplink spectral efficiency (SE) is obtained with the help of maximum ratio (MR) combining, which provides important insights into how finite-resolution ADCs affect the centralized cell-free massive MIMO. Simulation results show that the SE of MR combining rapidly saturates with the increase of ADC quantization bits, while MMSE combining requires more quantization bits to further improve the SE compared with ideal ADCs.

Phase noise is known as a main bottleneck in full-duplex (FD) communications, and limits the self-interference (SI) cancellation capability. There exist some works to mitigate the negative impact of phase noise, but usually relying on a perfect self-interference (SI) channel response information, which is difficult to be estimated accurately because of phase noise behavior. To improve the SI cancellation capability and suppress the impact of phase noise, we propose a multiple-downconversion FD transceiver design, which includes a new FD transceiver architecture and an SI cancellation algorithm. The proposed FD transceiver architecture deploys multiple receive chains to downconvert the signal split from the receive antenna. Particularly, the oscillator signal of each receive chain is originated from the transmit oscillator with a unique delay, such that the phase noises contained in the multipath SI components can be partially compensated. The SI cancellation algorithm includes a phase noise coefficients extraction step and an SI channel estimation step. As the extracted phase noise coefficients are used in the SI channel estimation, the estimation accuracy is improved. Simulation results show that, the limit of cancellation capability of the proposed FD transceiver with the proposed SI channel estimator is improved by 10 dB compared to that of the proposed FD transceiver with least square (LS) channel estimator.

A low complexity doubly fractional orthogonal time-frequency space (OTFS) channel estimation method is proposed in this paper. In order to deal with the channel spread caused by fractional delay and Doppler frequency shift, and to make full use of channel sparsity in the delay Doppler (DD) domain, the channel response is estimated in the fractional DD domain. In the fractional DD domain, the channel state information (CSI) could be approximately represented by several impulse responses and their corresponding delay Doppler information. Thus, we do not process the whole responses corresponding to the entire DD domain, but find only several impulse responses iteratively. To alleviate the coupling effect between the fractional delay and Doppler dimension, mixed one- and two-dimensional (1&2D) fractional models are used, and then these models are combined in an optimization problem to get the final CSI. To further reduce the complexity, limited Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) method is employed in this paper, thus resulting much reduced complexity, i.e., $\mathcal{O}\left( {\lambda {M_0}{N_0}} \right)$, compared with baseline schemes. Our simulation results show that, despite the quite low complexity, the NMSE performance of the proposed scheme is still superior to several classic methods.

In this paper, the Cell-Free (CF) massive MIMO (mMIMO) network with user equipment (UE) hardware impairments in spatially correlated channels is investigated. By using the established generic UE hardware impairment model, a minimum mean-squared error (MMSE) channel estimator is derived. Then, a lower bound on the uplink ergodic capacity is derived for CF mMIMO network with UE hardware impairments, and an optimal receive combining vector is also derived for obtaining maximum instantaneous signal-to-interference-and-noise ratio (SINR). Considering the computational complexity of the MMSE combining, the regularized zero-forcing (RZF) and maximum ratio (MR) combining schemes are presented as alternatives. Our results show that the RZF combining scheme has little loss in sum spectral efficiency (SE) compared to MMSE combining under different hardware impairments. Based on the use-and-then-forget (UatF) bound, a new closed-form uplink SE expression with MMSE estimator is derived for MR combining. Finally, we evaluate the tightness of the capacity bounds under different hardware impairments.

Abstract While Doppler resilient complementary waveforms (DRCWs) have previously been considered to suppress range sidelobes within a Doppler interval of interest in radar systems, their ability to provide Doppler resilience can be further improved. A new singular value decomposition (SVD)‐based DRCW construction is proposed, in which both transmit pulse trains (made up of complementary pairs) and receive pulse weights are jointly considered. Besides, using the proposed SVD‐based method, a theoretical bound is derived for the range sidelobes within the Doppler interval of interest. Moreover, based on the SVD solutions, a challenging non‐convex optimization problem is formulated and solved to maximise the signal‐to‐noise ratio (SNR) with the constraint of low range sidelobes. It is shown that, compared with existing DRCWs, the proposed SVD‐based DRCW has better Doppler resilience. Further, the new optimised SVD‐based DRCW has a higher SNR while maintaining the same Doppler resilience.

Unmanned aerial vehicles (UAVs) are widely used for surveillance and monitoring to complete target search tasks. However, the short battery life and moderate computational capability hinder UAVs to process computation-intensive tasks. The emerging edge computing technologies can alleviate this problem by offloading tasks to the ground edge servers. How to evaluate the search process so as to make optimal offloading decisions and make optimal flying trajectories represent fundamental research challenges. In this paper, we propose to utilize the concept of uncertainty to evaluate the search process, which reflects the reliability of the target search results. Thereafter, we propose a deep reinforcement learning (DRL) technique to jointly make optimal computation offloading decisions and flying orientation choices for multi-UAV cooperative target search. Specifically, we first formulate an uncertainty minimization problem based on the established system model. By introducing a reward function, we prove that the uncertainty minimization problem is equivalent to a reward maximization problem, which is further analyzed by a Markov decision process (MDP). To obtain the optimal task offloading decisions and flying orientation choices, a deep Q-network (DQN) based DRL architecture with a separated Q-network is then proposed. Finally, extensive simulations validate the effectiveness of the proposed techniques, and comprehensive discussions on how different parameters affect the search performance are given.

Spectrally constrained sequences (SCSs) play an important role in modern communication and radar systems operating over non-contiguous spectrum. Despite numerous research attempts over the past years, very few works are known on the constructions of optimal SCSs with low cross-correlations. In this paper, we address such a major problem by introducing a unifying framework to construct unimodular SCS families using circular Florentine rectangles (CFRs) and interleaving techniques. By leveraging the uniform power allocation in the frequency domain for all the admissible carriers (a necessary condition for beating the existing periodic correlation lower bound of SCSs), we present a tighter correlation lower bound and show that it is achievable by our proposed SCS families including multiple SCS sets with zero correlation zone properties.

Massive machine type communication (mMTC) is one of the core components of 6G communication systems to fulfil the demand of massive connectivity of billions of Internet-of-Things (IoT) devices. Due to its various advantages, grant-free random access schemes are considered as a promising technique to implement mMTC. The key to grant-free random access is active device detection at the base station. In this paper, firstly we introduce the odd total squared correlation (OTSC) of binary signature sets, then derive a corresponding lower bound. Systematic constructions of optimal signature sets based on the known odd periodic complementary sets (OPCS), almost binary sequences, large Kasami subsets and ideal sequences are presented, which are all optimal with respect to the derived OTSC lower bound. Next, it is demonstrated that the optimal OTSC signature sets can be effectively used in massive device activity detection. Using efficient approximate message passing with minimum mean squared error (AMP-MMSE) algorithm, it is shown that the sensing matrices arising from OTSC-optimal signature sets and periodic total squared correlation (PTSC)-optimal signature sets performs better than the popular Gaussian random matrix.

Recently, the abstraction ofcoflowis introduced to capture the collective data transmission patterns among modern distributed data-parallel applications. During processing, coflows generally act as barriers; accordingly, time-sensitive applications prefer their coflows to complete within deadlines, and deadline-aware coflow scheduling becomes very crucial. Regarding these data-parallel applications, we notice that many of them, includinglarge-scale query systems,distributed iterative training, anderasure codes enabled storage, are able to tolerate loss-bounded incomplete inputs by design. This tolerance indeed brings a flexible design space for the schedule of their coflows: when getting overloaded, the network can trade coflow completeness for the timeliness, and balance the completeness of different coflows on demand. Unfortunately, existing coflow schedulers neglect this tolerance, resulting in inflexible and inefficient bandwidth allocations. In this paper, we explore this fundamental trade-off and design POCO, a POlicy-based COflow scheduler, along with a transport layer enhancement scheme, to achieve customizable selective coflow completion for emerging time-sensitive distributed applications. Internally, POCO employs a suite of novel designs along with admission controls to makeflexible,work-conserving, andperformance-guaranteedrate allocation to online coflow requests very efficiently. Extensive trace-based simulations indicate that POCO is highly flexible and achieves optimal coflow schedules respecting the requirements specified by applications.

Scalable video caching is a promising technique to alleviate backbone traffic in sixth generation (6G) networks, and to serve users with video quality that adapts to varying channel conditions. In this paper, we develop a layer-based scalable video caching technique with non-orthogonal transmission by taking advantage of the layer feature in the scalable video. In addition, the impact of different serving base station selection algorithms is investigated. Our results indicate that both the caching placement design and transmission scheme design dominate the caching performance. To evaluate the interplay of these two policies, a tractable metric of Caching Aided Data Rate (CADR) is characterized and maximized by jointly optimizing the aforementioned two policies. Together with extensive Monte Carlo simulations, numerical results are also evaluated in this paper, demonstrating that the proposed Layer-based video Caching scheme with Non-Orthogonal Transmission (LCNOT) can achieve higher CADR performance than other baseline schemes.

Orthogonal time-frequency space (OTFS) is a promising technique for high mobility wireless communications, as it approaches a sparse representation of doubly-selective fading channel and can obtain delay-Doppler (DD) diversity gain. However, in practical OTFS systems, fractional channel parameter is a great challenge and will seriously deteriorates the sparsity and the performance of channel estimation. In this paper, we propose a novel channel estimation scheme for doubly selective channel in the presence of both fractional Doppler and fractional delay. First, we analyze the input-output relationship of single-input single-output (SISO) OTFS system based on the integer sampling DD grid, then extend it to the fractional DD grid. Compared with the integer DD domain, the model in the fractional DD domain is sparser and more accurate. Based on our analysis, two kinds of off-grid models, i.e., doubly fractional model and mixed one- and two-dimensional (1&2D) fractional models are proposed and compared. The mixed 1&2D fractional model has an advantage of low complexity, but encounters a tandem off-grid distortion. To tackle this distortion problem, as well as to balance the accuracy and the computational workload, a novel two dimensional off-grid decomposition and combination scheme is proposed based on the doubly and mixed 1&2D fractional model. Simulation results demonstrate the effectiveness and superiority of the proposed channel estimation schemes, compared with the existing work for OTFS systems under doubly selective channels.

Recent advances in distributed machine learning show theoretically and empirically that, for many models, provided workers would participate in the synchronizations eventually, i) the training still converges, even if only p workers take part in each round of synchronization, and ii) a larger p generally leads to a faster rate of convergence. These findings shed light on eliminating the bottleneck effects of parameter synchronization in large-scale data-parallel distributed training, having motivated several optimization designs.In this paper, we focus on optimizing the parameter synchronization for peer-to-peer distributed learning, in which workers generally broadcast or multicast their updated parameters to others for synchronization, and propose SELMCAST, an expressive and Pareto-optimal multicast receiver selection algorithm, to achieve the goal. Compared with the state-of-the-art design that randomly selects exactly p receivers for each worker’s multicast in a bandwidth-agnostic way, SELMCAST chooses receivers based on the global view of their available bandwidth and loads, yielding two advantages. Firstly, it could optimize the bottleneck sending rate, thus cutting down the time cost of parameter synchronization. Secondly, when more than p receivers are with sufficient bandwidth, they would be selected as many as possible, bringing benefits to the convergence of training. Extensive evaluations show that SELMCAST is efficient and always achieves near-optimal performance.

Nowadays, cross-device federated learning (FL) is the key to achieving personalization services for mobile users and has been widely employed by companies like Google, Microsoft, and Alibaba in production. With the explosive increase of participants, the central FL server, which acts as the manager and aggregator of cross-device model training, would get overloaded, becoming the system bottlenecks. Inspired by the emerging wave of edge computing, an interesting question is: could edge clouds help cross-device FL systems overcome the bottleneck?This article provides a cautiously optimistic answer by proposing INP, an FL-specific In-Network Processing framework, along with the novel Model Download Protocol of MDP and Model Upload Protocol of MUP. With MDP and MUP, edge cloud nodes along the paths in INP can easily eliminate duplicated model downloads and pre-aggregate associated gradient uploads for the central FL server, thus alleviating its bottleneck effect, and further accelerating the entire training progress significantly.

Sequences with low correlation sidelobes have found a wide range of applications in communication systems and RADAR. In this work, we have proposed a computational framework of aperiodic mismatched filtering sequence pairs using iterative twisted approximation (ITROX). A mismatched-filter is a filter where the transmitting sequence and receiving sequence are different (not matched). We have used singular value decomposition (SVD) instead of eigenvalue decomposition (EVD), which is more general, to obtain the resultant sequences. Numerical simulation shows that the resultant aperiodic mismatched filtering sequences have lower sidelobes without reducing too much loss-in-processing gain.

Orthogonal time frequency space (OTFS) is a novel scheme to combat the high Doppler effect under time-varying channel. In this paper, a pilot-aided channel estimation scheme based on Frank arrays, a matrix-form pilot structure, for multi-antenna OTFS systems is proposed for improving spectral efficiency. In the scheme, the pilots in delay-Doppler domain are constructed as several approximately orthogonal matrices based on Frank arrays. Different from the existing single pulse pilots and one-dimensional sequence pilots, these matrix-form pilots are placed on the same position on delay-Doppler domain, using code division multiplexing strategy to deal with the pilot pollution problem. Transceiver structures based on integer Doppler estimation are proposed, together with a simple but efficient threshold based channel estimation algorithm. It is shown via simulation results that, compared with the conventional multi-antenna OTFS schemes, the proposed scheme achieves better NMSE performance, while having low pilot overhead under the same conditions.

In Industrial Internet of Things (IIoT), reliability and latency are two important performance indicators. However, these two performance indicators are contradictory with each other, which are difficult to be enhanced simultaneously. In reality, many IIoT applications are in video or image format with critical requirements of both reliability and latency. In this article, we propose a scalable task offloading scheme for IIoT visual applications considering the unique scalable feature compared with general content. The proposed scheme demonstrates that partial content can be adaptively offloaded to a specific computing node to meet the reliability and latency requirements, meanwhile obtaining an excellent tradeoff between them. For optimization, a utility function is defined to characterize the tradeoff between reliability and latency. Then, a greedy algorithm is introduced to solve the problem of maximizing the utility function, where a near-optimal scheduling policy is adopted to achieve offloading association and properly offload data volume. Simulation results reveal that the proposed scalable task offloading scheme performs better than other benchmark schemes in balancing reliability and latency in IIoT visual applications, especially when the network traffic is moderate and channel conditions are undesirable.

Offloading computation tasks through cloud–edge collaboration has been a promising way to improve the Quality of Service (QoS) of applications. Usually, cloud server (CS) and edge server (ES) are selfish and rational and, therefore, it is imperative to develop incentive mechanisms, which can encourage idle ESs or the CS to participate in the task offloading process. In this article, we propose a computation offloading method based on the game theory, which is suitable for cloud–edge computing networks. It is considered that the CS has a lot of computation tasks to conduct, and ESs usually have idle computational resources. The CS can offload computation tasks to ESs with idle computational resources to reduce its own cost and pressure, and ESs can profit by selling their computational resources. The interaction between the CS and ESs is modeled as a Stackelberg game, and the proposed game is analyzed by using the backward induction method. It is proved that the game can achieve a unique Nash equilibrium. Then, a gradient-based iterative search algorithm (GISA) is proposed to obtain the optimal solution in order to maximize the utility of the CS and ESs. Finally, numerical simulation results show that our proposed method greatly outperforms other benchmark schemes under different scenarios, and can encourage ESs to trade their computational resources with the CS effectively.

In radar sensing and communications, designing Doppler resilient sequences (DRSs) with low ambiguity function for delay over the entire signal duration and Doppler shift over the entire signal bandwidth is an extremely difficult task. However, in practice, the Doppler frequency range is normally much smaller than the bandwidth of the transmitted signal, and it is relatively easy to attain quasi-synchronization for delays far less than the entire signal duration. Motivated by this observation, we propose a new concept called low ambiguity zone (LAZ) which is a small area of the corresponding ambiguity function of interest defined by the certain Doppler frequency and delay. Such an LAZ will reduce to a zero ambiguity zone (ZAZ) if the maximum ambiguity values of interest are zero. In this paper, we derive a set of theoretical bounds on periodic LAZ/ZAZ of unimodular DRSs with and without spectral constraints, which include the existing bounds on periodic global ambiguity function as special cases. These bounds may be used as theoretical design guidelines to measure the optimality of sequences against Doppler effect. We then introduce four optimal constructions of DRSs with respect to the derived ambiguity lower bounds based on some algebraic tools such as characters over finite field and cyclic difference sets.

Quasi-complementary sequence sets (QCSSs) can be seen as a generalized version of complete complementary codes (CCCs), which enables multicarrier communication systems to support more users. The contribution of this work is two-fold. First, we propose a systematic construction of Florentine rectangles. Secondly, we propose several sets of CCCs and QCSSs, using Florentine rectangles. The CCCs and QCSSs are constructed over$\mathbb {Z}_{N}$, where$N\geq 2$is any integer. The cross-correlation magnitude of any two of the constructed CCCs is upper bounded by$N$. By combining the proposed CCCs, we propose asymptotically optimal and near-optimal QCSSs with new parameters.

For high mobility communication scenario, the recently emerged orthogonal time frequency space (OTFS) modulation introduces a new delay-Doppler domain signal space, and can provide better communication performance than traditional orthogonal frequency division multiplexing system. This article focuses on the joint channel estimation and data detection (JCEDD) for hybrid reconfigurable intelligent surface (HRIS) aided millimeter wave (mmWave) OTFS systems. Firstly, a new transmission structure is designed. Within the pilot durations of the designed structure, partial HRIS elements are alternatively activated. The time domain channel model is then exhibited. Secondly, the received signal model for both the HRIS over time domain and the base station over delay-Doppler domain are studied. Thirdly, by utilizing channel parameters acquired at the HRIS, an HRIS beamforming design strategy is proposed. For the OTFS transmission, we propose a JCEDD scheme over delay-Doppler domain. In this scheme, message passing (MP) algorithm is designed to simultaneously obtain the equivalent channel gain and the data symbols. On the other hand, the channel parameters, i.e., the Doppler shift, the channel sparsity, and the channel variance, are updated through expectation-maximization (EM) algorithm. By iteratively executing the MP and EM algorithm, both the channel and the unknown data symbols can be accurately acquired. Finally, simulation results are provided to validate the effectiveness of our proposed JCEDD scheme.

In the past few years, the industry and academia have been working hard to establish and standardise vehicle-to-everything (V2X) communications, which is one of the vital emerging services for next generation wireless networks. Therewith, to control radio resource properly with balanced implementation complexity, a full-duplex V2V discovery mechanism in a 5G-V2X network based on a random backoff procedure is proposed to better utilize radio resources on which nearby vehicles establish their links. The main aims are to reduce complexity, latency, improve throughput and guarantee stability for V2V communication. The unemployed cellular network channel operates in full-duplex mode and leaves the channel as soon as they are informed that someone is in place through latency and throughput analysis technique. The channel sensing and identification are done in a cooperative manner before transmission. Furthermore, two effective resource distribution algorithms are proposed that grant the optimum resource distribution for V2V and V2I-Users. The detection and the throughput of full-duplex V2V communication in the proposed scheme have been formulated and the performance of the proposed scheme and validity of corresponding analysis are verified through simulation experiments.

Security and efficiency are crucial considerations for Long Term Evolution for Railway (LTE-R) which bears real-time transmission of train control information for future high-speed railway. In this article, we first analyze the vulnerabilities of LTE-R access authentication protocol, and then propose a proxy signature based authentication scheme to enhance the security of LTE-R without sacrificing efficiency. The proposed scheme consists of three main security mechanisms: a novel Elliptic Curve Cryptosystem based Certificateless Proxy Signature (ECC-CLPS) designed for authentication security, a hash-based puzzle introduced to defend against denial of service (DoS) attack and a key pre-generation mechanism used to improve the efficiency of fast handover authentication. The security analysis and performance simulation show that our scheme has advantages over existing LTE-based authentication schemes in terms of security, functionality, computation and communication costs. Moreover, the authentication requirements for security and efficiency in different LTE-R communication scenarios are fully considered, which makes our scheme more suitable for the access authentication in the future LTE-R based high-speed railway.

Due to their beautiful aperiodic correlation properties and low peak-to-average power ratio (PAPR), Golay complementary pairs (GCPs) and complementary sequence sets (CSSs) have been well studied. However, conventional GCPs and CSSs can only adapt to contiguous spectral resource allocation in wireless communication systems, and it is difficult for them to be compatible with non-contiguous resource allocation. Inspired by recent constructions of non-contiguous GCPs given by Şahin and Yang, we propose a new construction of non-contiguous GCPs, which can be regarded as an extension of Turyn’s construction. Besides, we then propose some constructions of non-contiguous CSSs, which can adapt to flexible resource allocation, having more flexible lengths, and lower time domain cross correlation than non-contiguous GCPs. Finally, we propose a CSS-based physical uplink control channels (PUCCH) scheme for up to 2 uplink control information (UCI) bits. Simulations show that its performance is similar to the GCP-based PUCCH scheme.

Grant-Free Non-Orthogonal Multiple Access (GF-NOMA) is considered as a promising technology to support the massive connectivity of Machine-Type Communications (MTC). The design of efficient and high-performance multi-user detection (MUD) scheme is a challenging issue of GF-NOMA, especially when the number of active users is unknown and relatively high. This paper adopts Sparse Bayesian Learning (SBL) approaches to solve the MUD problem of GF-NOMA in MTC. The MUD problem within a certain access slot is formulated as a Single Measurement Vector (SMV) model and efficiently solved via SBL-based methods. To further improve the MUD performance, we set up a Multiple Measurement Vector (MMV) model and develop block SBL-based MUD methods, by exploiting the temporal correlation of user activity over successive access slots. Then to extend the usage of the aforementioned algorithms to the scenarios with relatively high, or quasi-sparse, user activity, we propose novel SBL-based MUD algorithms via post sparse error recovery methodology, for both the SMV and MMV problem models. Simulation results show that the proposed SBL-based MUD algorithms achieve substantial performance gain over traditional ones, especially when the number of active users is unknown and relatively high.

Since its inception in 2013, Golay complementary sequences with periodic zero autocorrelation zones (ZACZs) and cross-correlation zones (ZCCZs), or Golay-ZCZ (zero correlation zone) sequences have special importance in modern communication systems. Till date, all the proposed complementary sequences with periodic ZACZs and ZCCZs have lengths in the form$2^{m}$, where$m$is a natural number. In this paper, we extend such complementary sequences with large periodic ZACZs and ZCCZs to more flexible lengths in the form of non-power-of-two.

The increase of mobile applications in the internet of vehicles (IoVs), necessitates the demand for higher computation capabilities. Vehicles can transfer related applications to another nodes for processing. In this paper, an efficient task offloading scheme for cellular vehicle to everything (C-V2X) is proposed to improve offloading reliability and latency. Vehicles are grouped into clusters, where vehicles in need of assistance can transfer their task to other vehicles for processing through the vehicle to vehicle (V2V) link, or transfer their task to the mobile edge computing (MEC) server via the vehicle to network (V2N) link. Matching theory is exploited for the task assignments. Simulation results reveals that the proposed scheme performs better than the existing schemes.

Multiuser detection (MUD) algorithms is regarded as a challenging issue that need to be resolved for Machine-Type Communications (MTC). In this paper, the MUD is formulated as a sparse signal recovery problem. Note that transmitted signals have sparse characteristics naturally existing in MTC communications, it is desirable to use Bayesian Compressive Sensing (BCS) technique to improve the efficiency of MUD problem. By exploiting the sparsity of transmitted signal, we first propose two-layer hierarchical model based sparse Bayesian learning on variational expectation maximization (SBL- VEM) algorithm to recover the transmitted signals. Furthermore, in order to make use of the support set information of the transmitted signal, the third layer is added, and we propose a novel sparse Bayesian learning support learning (SBL-SL) algorithm. The proposed two algorithms overcome the difficulty of unknown user activity factor in MUD. Simulation results show that the proposed two algorithms achieve better recovery performance than the conventional MUD solutions in MTC.

Benefiting from the powerful cyclic redundancy check aided successive cancellation list (CA-SCL) decoder, the polar codes have achieved competitive performances with respect to LDCP codes. However, its short codeword performance needs to be further improved for supporting reliable short package transmissions typically in massive machine-type communications (mMTC). In this paper, the short polar coding problem is tackled by developing a bit-flipping aided CA-SCL detection method. First, we reveal that error distribution of SCL decoding strongly relates to the row weights of generator matrix. Accordingly, we determine the flipping indices according to both their row weights and their polarized bit-channel reliabilities. The simulation results show that our row weight based successive cancellation list flipping (RWB-SCL-Flipping) decoding achieves around 0.3 dB gain compared with its counterpart in [1] while transmitting short codewords over AWGN channels.

Freshness and quality are two important metrics in real-time video applications in surveillance networks. However, these two metrics conflict with each other in many cases. To enhance the performance of real-time video services, it is very important, but also challenging, to find a good tradeoff between freshness and quality. In this article, we focus on studying the tradeoff between freshness and quality, where video packets are encoded with scalable video coding (SVC) and adaptive random network coding (ARNC). Moreover, the Age of Information (AoI) is applied to model video freshness, while the video quality is modeled by the number of layers received by users. A utility function is defined to capture the tradeoff between video freshness and video quality. By maximizing the defined utilities, an excellent tradeoff between freshness and quality could be obtained. To solve the formulated optimization problem, the maximization of the utility function is characterized as a Markov decision process (MDP) problem, which is effectively solved by a heuristic algorithm and a deep$Q$network (DQN)-based algorithm. Simulation results indicate that the applied ARNC technique can achieve higher utility performance than other benchmark transmission techniques, and the DQN-based algorithm outperforms the heuristic algorithm in most cases.

This article focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Secondly, the 3D-Newtonized orthogonal matching pursuit algorithm is utilized for the extraction of the UL channel parameters, including channel gains, directions of arrival, delays, and Doppler frequencies, over the antenna-time-frequency domain. Thirdly, we carefully analyze energy dispersion and power leakage of the 3D angle-delay-Doppler channels. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides and to assure that the observation regions for different users do not overlap over the 3D cubic area, i.e., angle-delay-Doppler domain. After scheduling, different users can map their respective data to the scheduled delay-Doppler domain grids, and simultaneously send the data to base station (BS) without inter-user interference in the same OTFS block. Correspondingly, the signals at desired grids within the 3D resource space of BS are separately collected to implement the 3D channel estimation and maximal ratio combining-based data recovery over the angle-delay-Doppler domain. Then, we construct a low complexity beamforming scheme over the angle-delay-Doppler domain to achieve inter-user interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.

A pair of sequences is called a Z-complementary pair (ZCP) if it has zero aperiodic autocorrelation sums at each of the non-zero time-shifts within a certain region, called the zero correlation zone (ZCZ). ZCPs are categorised into two types: Type-I ZCPs and Type-II ZCPs. Type-I ZCPs have the ZCZ around the in-phase position and Type-II ZCPs have the ZCZ around the end-shift position. Till now only a few constructions of Type-II ZCPs are reported in the literature, and all have lengths of the form 2m±1 or N+1 where N=2a10b26cand a, b, c are non-negative integers. In this paper, we propose a recursive construction of ZCPs based on concatenation of sequences. Inspired by Turyn's construction of Golay complementary pairs, we also propose a construction of Type-II ZCPs from known ones. The proposed constructions can generate optimal Type-II ZCPs with new flexible parameters and Z-optimal Type-II ZCPs with any odd length. In addition, we give upper bounds for the PMEPR of the proposed ZCPs. It turns out that our constructions lead to ZCPs with low PMEPR.

One objective of this paper is to propose a novel class of sequence pairs, called “quasi-orthogonal Z-complementary pairs (QOZCPs)”, each depicting Z-complementary property for their aperiodic auto-correlation sums and also having a low correlation zone when their aperiodic cross-correlation is considered. Construction of QOZCPs based on Successively Distributed Algorithms under Majorization Minimization (SDAMM) is presented. Another objective of this paper is to apply the proposed QOZCPs in fully polarimetric radar systems and analyse the corresponding ambiguity functions. It turns out that QOZCP waveforms are much more Doppler resilient than the known Golay complementary waveforms.

This paper focuses on a new path division multiple access (PDMA) for both uplink (UL) and downlink (DL) massive multiple-input multiple-output network over a high mobility scenario, where the orthogonal time frequency space (OTFS) is adopted. First, the 3D UL channel model and the received signal model in the angle-delay-Doppler domain are studied. Then, along UL, we design a path scheduling algorithm to properly assign angle-domain resources at user sides. Correspondingly, the signals at desired grids within the 3D resource space of the base station are separately collected to implement the 3D channel estimation and maximal ratio combining-based data detection. Then, we construct a low-complexity beamforming scheme over the angle-delay-Domain domain to achieve interuser interference free DL communication. Simulation results are provided to demonstrate the validity of our proposed unified UL/DL PDMA scheme.

We study the problem of computation offloading and resourceallocation in multi-user multi-channel mobile edge computing (MEC) systems. Each user equipment (UE) in the system has a computation-intensive and time-sensitive task that needs to be executed either locally or remotely in an MEC server. All UE tasks have individual deadline constraints that are treated as soft constraints. The MEC server has limited computational resources, which impose hard constraints on the overall offloading computation capacity. The objective is to minimize a cost function that is expressed as a weighted sum of energy consumption, delay, and deadline penalty of all UEs. The optimum design is performed with respect to three decision parameters: whether to offload a given task, which wireless channel to use during offloading, and how much MEC resources should be allocated for an offloaded task. We apply a Deep Reinforcement Learning approach known as Deep Deterministic Policy Gradient to solve the problem. Simulation results demonstrate that the proposed algorithm outperforms other existing schemes such as Deep Q-Network (DQN).

Recently, the abstraction of coflow is introduced to capture the collective data transmission patterns among modern distributed data-parallel application. During processing, coflows generally act as barriers; accordingly, time-sensitive applications prefer their coflows to complete within deadlines and deadline-aware coflow scheduling becomes very crucial.

Over the past few years, industry and academia have worked hard to develop and standardize vehicle-to-everything (V2X) communication, which is one of the important emerging service for next-generation wireless networks (5G). Thereby, to manage radio resource efficiently with realistic execution complexity, a vehicle density based two-stage resource management scheme for 5G-V2X network is presented in this paper, whose key targets are to reduce latency, improve throughput and guarantee reliability for V2V-UEs and V2N-UEs. Particularly, during the first level, the resource distribution strategy depend on vehicles density information (VDI), that is different compared to channel state information (CSI) and queuing state information (QSI) used during the second level by considering buffer state information. Two efficient resource management algorithms are presented that grants the optimum resource distribution for V2N-UEs and V2V-UEs. Simulation experiments reveal the promising performance of proposed scheme compared with existing scheme.

In this paper, a cooperative roadside unit (RSU) caching policy is proposed for vehicular content delivery networks (VCDNs) in two-way road with a T-junction to reduce the average download delay of contents. In our system model, due to the constraint of base station (BS) transmission capacity, vehicles can achieve better download delay performance if the requested contents are stored in the cache-enabled RSUs. According to the traffic situations, vehicles are classified into four types. With this framework, the closed-from result of the average download delay is derived based on the proposed cooperative RSU caching policy, the arrival rates of vehicles belonging to different types, and the popularity distributions of contents. The problem of minimizing the average download delay with the proposed cooperative caching policy is formulated subject to the cache capacity constraint of RSUs. Since the optimization problem falls into an NP-hard problem, we adopt a fast simulated anneal (FSA) algorithm for content allocation. Simulation results show that the proposed FSA algorithm based cooperative RSU caching policy is more efficient for the average download delay reduction compared with the conventional popularity based caching policy.

With the explosion of the video content industry, the demand for large file downloads is increasing rapidly. Different from text-based contents, video contents require lossless transmission with an ordered constraint to ensure the best utility. Meanwhile, providing low latency and stable downloads under high mobility scenarios, such as vehicle users in a highway, is one of the major challenges in wireless communications. In this work, a caching strategy is adopted to address the problem of downloading large order-sensitive contents, supporting user mobility. Particularly, a multi-layer caching framework is proposed based on a simple yet efficient adaptive chunk-based caching (ACBC) algorithm. The multi-layer caching framework increases the flexibility in deploying file chunks and shortens the distance between user and content with affordable memory cost, while ACBC algorithm introduces a feedback mechanism to provide stable and high-efficiency chunk deployment scheme. The cooperation of the two ensures reliable downloads for high-mobility users with minimal access delay and caching space requirements. Simulation results indicate that our solution achieves a very small content access delay with a low cache space occupation compared with other mobility-based caching algorithms (such as MAP and RICH).

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 paper, a multiple-input multiple-output (MIMO) full-duplex (FD) transceiver design is proposed, which can mitigate the impact of phase noise and provide an enhanced self-interference cancellation capability. This design is featured by finely tuning the delay of the transmit oscillator signal and using it as the receive oscillator signal. To maximize the cancellation capability, a delay selection principle is developed to calculate the optimal oscillator delay. To further reduce the number of parameters needed in the delay calculation, a simplified principle is also developed with acceptable loss of cancellation capability. Compared to the traditional transceiver with a shared oscillator for its transmitter and receiver, the proposed design achieves an obvious improvement about 10 dB in terms of cancellation capability.

Machine-to-Machine (M2M) communication is now playing a market-changing role in a wide range of business world. However, in event-driven M2M communications, a large number of devices activate within a short period of time, which in turn causes high radio congestion and severe access delay. In this paper, a semi-distribution congestion control algorithm (CCA) for event-driven M2M communications in LTE-Advanced networks is presented. This CCA uses the random access preamble idleness rate in a small number of slots to estimate the channel load. The eNodeB broadcasts the estimated value of traffic, so the M2M devices can calculate the control factor to control the flow. It is shown that, the throughput and delay performance of the proposed semi-distribution congestion control algorithm are significantly improved compared with the existing schemes.

The most promising feature of the fifth generation (5G) wireless networks is to support the Internet of Things (IoT) application, such as massive machine-type communications (mMTC). In mMTC scenario, a large number of users are connected to an access point, but very few of them are active at the same time, which motivates us to exploit Low-Activity Code Division Multiple Access (LA-CDMA) as the multiple access technology for MTC. The optimal maximum a posterior probability (MAP) assumes that the user activity factor is exactly known, which is in fact unknown in the practical detection scenarios. In this paper, we first formulate the LA-CDMA uplink into a multiple measurement vector (MMV) model for several continuous time slots, then we introduce a novel iterative reweighed (IR) algorithm to reconstruct the sparse signal. The new scheme overcomes the difficulty of unknown user activity factor and the simulation results over massive MTC systems demonstrate that the proposed algorithm achieves substantial performance gain over traditional detectors considerably.

As the key infrastructure for emerging 5G and Internet-of-Things (IoT) applications, micro data centers would be widely deployed at network edges to provide high-bandwidth low-latency cloud service. In these systems, applications would deliver large-size data objects among servers for various purposes like service deployment, application scale-up, and data duplication on demand. Accordingly, reducing delivery time is crucial for the optimization of service delay and system utilization. To accelerate the delivery, this article proposes a multisource-aware adaptive data transmission solution, Parallel Push (PPUSH), by leveraging the fact that data objects in the cloud are generally replicated among servers by design. At the high level, PPUSH achieves efficient delivery of multisource data by launching multiple push flows in parallel; and at the low level, it decouples transfers from different sources by encoding data objects with rateless RaptorQ code, and further employing novel congestion controls to prioritize the bandwidth allocation of concurrent tasks respecting their remaining sizes. Fluid model analysis along with Mininet-based test and packet-level simulation shows that, unlike DCTCP and other proposals, push is robust to packet loss and achieves provable prioritized bandwidth allocation. Extensive simulation results imply that, with above advantages, PPUSH could achieve very efficient data delivery by making use of all available data sources: for instance, compared with the straightforward design of equal-size task split and fair bandwidth allocation, its adaptive task assignment and prioritized traffic scheduling reduce the average task completion time in a tested scenario by 1.495× and 1.329×, respectively, demonstrating a total improvement of 1.586×, when enabled at the same time.

In this paper, we consider a downlink non-orthogonal multiple access (NOMA) enabled heterogeneous small cells network (HSCN), where the macro base station simultaneously communicates with multiple small cell base stations (SBSs) through wireless backhaul. In each small cell, users are grouped by NOMA bases and then served by their respective SBS. The proposed framework considers the realistic imperfect channel state information and quality of service requirements of users. The goal is to investigate an energy-efficient joint power, and bandwidth allocation scheme, which aims to maximize the energy efficiency (EE) of the small cells in downlink NOMA-HSCN constrained by the maximum transmit power and the minimum required data rate simultaneously. The optimization problem is non-convex due to the fractional objective function and non-convex constraint and thus challenging to obtain an exact solution efficiently. To this end, the joint optimization is first decomposed into two subproblems. Then, an iterative algorithm to solve the power optimization subproblem is proposed with guaranteed convergence. Furthermore, we derive a closed-form solution for the bandwidth allocation subproblem. Simulation results reveal that the effectiveness of the proposed schemes in terms of EE compared to the existing NOMA and the orthogonal multiple access schemes.

In recent years, complete complementary codes (CCCs) and quasi-complementary sequence sets (QCSSs) have found many important applications in multi-carrier code-division multiple-access (MC-CDMA) systems for their good correlation properties. In this paper, we propose a generic construction of multiple sets of CCCs over ZN, consisting of sequences of length N, where N ≥ 3 is an arbitrary odd integer. Interestingly, the maximum inter-set aperiodic cross-correlation magnitude of the proposed CCCs is upper bounded by N. It turns out that the combination of the generated CCCs results in a new set of sequences to obtain asymptotically optimal and near-optimal aperiodic QCSSs. The proposed construction includes a recent optimal construction of QCSSs with prime length as a special case and leads to asymptotically optimal QCSSs with new flexible parameters.

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.

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.

Scalable video coding (SVC) and video caching are two promising techniques in the 5th generation networks to improve the users' quality of experience (QoE) in terms of video retrieval. In this paper, we study the video content retrieval in cache-aided device-to-device (D2D) networks, where each video content is coded into multiple layers via SVC. In video caching placement phase, the probabilistic caching placement policy is applied, while in content retrieval phase, the non-orthogonal transmission scheme is utilized. Besides, different D2D transmitter selection algorithms are considered and cache-aided data rate (CADR) is formulated as a metric in this paper, and it is maximized by jointly optimizing the probability caching policy and the power allocation policy in two phases. Analytical results show that probabilistic caching policy incorporated with power domain non-orthogonal transmission scheme can achieve significant benefits compared with other benchmark schemes.

In this paper, we propose the full-duplex and cloud radio access network (C-RAN) based multi-cell non-orthogonal multiple access schemes over 5G mobile wireless networks. To cope with the severe intra-cell and inter-cell interferences as well as perform the centralized optimization, we adopt the C-RAN architecture, where the baseband processing and resource management are conducted at a central unit (CU). With the goal of maximizing the weighted sum achievable rate, we formulate the sum rate maximization power allocation problem as a non-convex problem. Thanks to the hidden monotonicity structure of the considered problem, the optimal power allocation algorithm is developed by the monotonic optimization method. Besides, we propose another suboptimal algorithm by employing successive convex approximation method to obtain the close-to-optimal solution with a significantly reduced computational complexity. Extensive simulations are conducted to verify the effectiveness of our proposed power allocation schemes, and confirm the superiority of our proposed C-RAN architecture.

To the best of our knowledge, Belief Propagation List (BPL) decoding achieves the best error-correction performance within the class of BP based polar code decoding algorithms. It regards different permutations of the polar code factor graph as a range of available "decoding lists" for individually executing the BP algorithm. Then, a minimum Euclidean distance criterion is employed to combine these individual decisions. However, we demonstrated that simply increasing the number of lists involved in the BPL algorithm does not always improve its performance. Instead, we exploit the extrinsic information transfer (EXIT) chart technology to verify the efficiency of a decoding list. Based on this analysis, an efficient list selection scheme is proposed. In the same spirit, in order to mitigate the impact of relatively inferior decoding lists on other selected decoding lists, new decision fusion schemes are also designed. Benefiting from these improvements of BPL algorithm, performance gains are evidenced in our simulation results.

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.

In this paper, a clustering based resource management scheme for latency and sum rate optimization in V2X networks is proposed to identify distinguished demands for different kinds of vehicular links, namely cellular-vehicle-to-vehicle (C-V2V) links and cellular-vehicle-to-infrastructure (C-V2I) links, and to improve the performance of cellular user in terms of sum rate, latency and throughput for C-V2I links while guaranteeing reliability for every C-V2V connection. To address the fast channel changes due to high mobility, we present a clustering based resource management model to achieve band sharing and efficient power management that depends on large scale fading. Besides, we have also considered the resource management problem of cellular V2X and VANETs users to reduce data transmission impacts. Firstly, the total average sum rate of each C-V2I links is used as an optimization goal to increase the throughput and to reduce latency of the entire C-V2I link. Secondly, cluster based optimum algorithms are proposed which give the optimum resource management. Simulation results reveal that the proposed scheme outperforms the existing scheme.

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.

To achieve low-latency and high-reliability (LLHR) for the vehicle-to-everything (V2X) services, the Long Term Evolution (LTE)-based solution has been considered as a promising technology. Since the existing LTE networks are based on the orthogonal multiple access (OMA), the limited spectrum resources have not been fully and efficiently utilized, so that severe data congestion and low access efficiency cannot be avoided in dense networks. Non-OMA (NOMA) provides a new solution for 5G V2X services to mitigate traffic congestion and reduce latency. Different from existing works, we propose two relay-assisted NOMA transmission schemes for 5G V2X communications, i.e., half-duplex relay-assisted NOMA (HDR-NOMA) broadcasting/multicasting and full-duplex relay-assisted NOMA (FDR-NOMA) broadcasting/multicasting, and investigate the optimal power allocation problems for them. To improve the quality of service (QoS) for the users with the poor channel conditions and to guarantee the fairness, the power allocation problems are formulated to maximize the minimum achievable rate for all users. Even though both of the formulated problems are neither concave nor convex, it is shown that the considered problems are quasi-concave. Hence, a bisection-based power allocation algorithm is proposed to obtain the optimal solutions to the problems. Numerical results demonstrate that the proposed schemes outperform the scheme with fixed power allocation obviously and achieve a significant performance improvement with respect to a suboptimal fractional transmit power allocation (FTPA) method and the optimized time division multiple access (TDMA) scheme. Besides, compared with HDR-NOMA, better performance can be achieved by FDR-NOMA scheme when the self-interference is sufficiently suppressed.

We study the optimum designs of the downlink of user-centric ultra-dense Internet of Things (IoT) networks with fiber-wireless communications (FWCs). A large number of low power radio access points (RAPs) are densely deployed in the network to provide service to spatially distributed IoT physical devices (PDs). The RAPs are connected to a central unit (CU) through optical fiber (OF) front-hauls. Radio-frequency-over-fiber (RFoF) is employed in the optical front-hauls to reduce RAP complexity, cost, and energy consumption. With RFoF front-hauls, wireless signals received by PDs are subject to distortions accumulated through the optical and wireless links, including optical loss, optical chromatic distortion, optical and thermal noises, wireless pathloss, and small scale fading. The optimum designs are performed across the optical and wireless domains with the help of a newly developed model that quantifies the combined effects of the optical and wireless links. One of the main challenges faced by the design of an ultra-dense IoT network is the high energy consumption due to dense RAP deployment. The objective of this paper is to minimize the total energy consumption of the entire IoT network, including both optical and wireless links, by jointly optimize RAP power allocation and RAP-PD association, subject to quality-of-service (QoS) constraints for each PD. We propose a low complexity suboptimum binary forcing gradient search (BFGS) algorithm, which performs a gradient-based search based on the unique structure of the problem. Simulation results show that the optical front-hauls have significant impacts on the performance and design of ultra-dense IoT networks.

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.

This paper investigates the performance of non-orthogonal multiple access (NOMA) systems over time-correlated Rayleigh fading channels, where the users have heterogeneous quality of service requirements, e.g., a latency-critical user with a low target rate and a delay-tolerant user with a large target rate. In order to meet the different requirements of the users, two partial hybrid automatic repeat request (HARQ) schemes, including partial HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR), are proposed. The closed-form expressions of outage probabilities for NOMA with and without re-transmission are derived. With the developed outage probabilities, a condition on the superiority of NOMA to orthogonal multiple access (OMA) is obtained. In particular, the condition is characterized by the transmit powers for NOMA without re-transmission and is obtained by using the bisection method in the case with re-transmission. To further improve the performance of the HARQ enabled NOMA schemes, we consider an average transmit power minimization problem by optimizing the transmit power among different transmission rounds with outage constraints. However, due to the complexity of the developed outage probabilities, the formulated problem is non-convex and challenging to solve. Then we approximate the original problem by deriving the upper-bound approximations of the outage probabilities and solve it by using the geometric programming method. Simulation results demonstrate the accuracy of the developed analytical results. It is shown that the performance of NOMA is superior to OMA, only when the obtained condition is satisfied. HARQ-CC and HARQ-IR can enhance the outage performance of NOMA over time-correlated fading channels and the HARQ-IR has excellent performance in terms of energy efficiency.

Caching is an effective way to address the challenges due to explosive data traffic growth and massive device connectivity in fifth-generation (5G) networks. Currently, few works on caching pay attention to the impact of the time duration for which content is stored, called retention time, on caching optimization. The research on retention time is motivated by two practical issues, i.e., flash memory damage and storage rental cost in cloud networks, together giving rise to the storage cost. How to optimize caching contents taking the storage cost into consideration is a challenging problem, especially for the scenarios with cache-enabled mobile nodes. In this paper, a retention-aware caching problem (RACP) in vehicular networks is formulated, considering the impact of the storage cost. The problem's complexity analysis is provided. For symmetric cases, an optimal dynamic programming (DP) algorithm with polynomial time complexity is derived. For general cases, a low complexity and effective retention aware multi-helper caching algorithm (RAMA) is proposed. Numerical results are used to verify the effectiveness of the algorithms.

This paper considers the co-existence of two important communication techniques, non-orthogonal multiple access (NOMA) and mobile edge computing (MEC). Both NOMA uplink and downlink transmissions are applied to MEC, and analytical results are developed to demonstrate that the use of NOMA can efficiently reduce the latency and energy consumption of MEC offloading. In addition, various asymptotic studies are carried out to reveal the impact of the users' channel conditions and transmit powers on the application of NOMA to MEC are quite different from those in conventional NOMA scenarios. Computer simulation results are also provided to facilitate the performance evaluation of NOMA-MEC and also verify the accuracy of the developed analytical results.

Grant-free transmission is an important feature to be supported by future wireless networks since it reduces the signaling overhead caused by conventional grant-based schemes. However, for grant-free transmission, the number of users admitted to the same channel is not capped, which can lead to a failure of multi-user detection. This paper proposes non-orthogonal multiple-access (NOMA) assisted semi-grant-free (SGF) transmission, which is a compromise between grant-free and grant-based schemes. In particular, instead of reserving channels either for grant-based users or grant-free users, the focus here is on an SGF communication scenario, where users are admitted to the same channel via a combination of grant-based and grant-free protocols. As a result, a channel reserved by a grant-based user can be shared by grant-free users, which improves both connectivity and spectral efficiency. Two NOMA assisted SGF contention control mechanisms are developed to ensure that, with a small amount of signaling overhead, the number of admitted grant-free users is carefully controlled and the interference from the grant-free users to the grant-based users is effectively suppressed. Analytical results are provided to demonstrate that the two proposed SGF mechanisms employing different successive interference cancelation decoding orders are applicable to different practical network scenarios.

This paper considers a challenging communication scenario, in which users have heterogenous mobility profiles, e.g., some users are moving at high speeds and some users are static. A new non-orthogonal multiple-access (NOMA) transmission protocol that incorporates orthogonal time frequency space (OTFS) modulation is proposed. Thereby, users with different mobility profiles are grouped together for the implementation of NOMA. The proposed OTFS-NOMA protocol is shown to be applicable to both uplink and downlink transmission, where sophisticated transmit and receive strategies are developed to remove inter-symbol interference and harvest both multi-path and multi-user diversity. Analytical results demonstrate that both the high-mobility and the low-mobility users benefit from the application of OTFS-NOMA. In particular, the use of NOMA allows the spreading of the high-mobility users’ signals over a large amount of time-frequency resources, which enhances the OTFS resolution and improves the detection reliability. In addition, OTFS-NOMA ensures that low-mobility users have access to bandwidth resources which in conventional OTFS-orthogonal multiple access (OTFS-OMA) would be solely occupied by the high-mobility users. Thus, OTFS-NOMA improves the spectral efficiency and reduces latency.

Caching is a promising technique for 5G networks to reduce the backhual traffic and increase the overall network efficiency. In this paper, we study the caching placement policy with consideration of cooperative transmission for a two-hop relay-enabled device-to-device (D2D) network. In the caching placement phase, the probabilistic caching placement policy is considered, and in the content transmission phase, the hybrid automatic repeat request (HARQ) scheme with soft information combining [i.e., energy accumulation (EA) and mutual-information accumulation (MIA)] is utilized to improve the content retrieval experience. Cache-aided successful transmission probability (CSTP) is adopted as the main performance metric in this paper. By using tools from stochastic geometry, analytical expressions for the CSTP under different transmission schemes are derived. In moderate or high SIR regime, the optimal caching placement policy is identified based on the analytical expression and the CSTP performance is maximized accordingly. Evaluation results suggest that the MIA-based caching strategy performs better as opposed to existing strategies in most cases, but this outperformance vanishes when the transmission environment becomes severe or when users’ requests become concentrated.

In vehicular ad hoc networks (VANETs), cellular vehicle-to-everything (C-V2X) is an emerging technology for communications between vehicle-to-infrastructure, vehicle-to-pedestrian, and vehicle-to-network which improves traffic efficiency, road safety, and the availability of infotainment services. Herein, a novel V2V-enabled resource allocation scheme based on C-V2X technology is proposed to improve the reliability and latency of VANETs. The key idea is that V2V communications based on cellular-V2X technology among vehicles remove the contention latency and can assist for longer distance communications. Particularly, we propose a hybrid architecture, where the V2V links are controlled by the cellular eNodeB in the overlay scheme. In this scheme, every vehicle periodically checks its packet lifetime and requests the cellular eNodeB to determine V2V links. The optimum resource allocation problem at the cellular eNodeB is to choose optimum receiver vehicles to determine V2V links and allocate suitable channels to minimize the total latency. This problem is equivalent to the maximum weighted independent set problem (MWIS-AW) with associated weights, which is NP-hard. In order to compute the weights, an analytical approach is developed to model the expected latency and packet delivery ratio. Moreover, a greedy cellular-based V2V link selection algorithm is proposed to solve MWIS-AW problem and develop a theoretical performance lower bound. Simulation results show that the proposed scheme significantly outperforms the existing schemes in terms of latency, throughput, and packet delivery ratio.

In vehicular ad hoc networks (VANETs), communication links break more frequently due to the high-speed vehicles. In this paper, a novel cluster-based VANET oriented evolving graph (CVoEG) model is proposed by extending the existing VoEG model to improve the reliability of vehicular communications. Here, the link reliability is used as a criterion for cluster members (CMs) and cluster heads (CHs) selection. The proposed CVoEG model divides VANET nodes (vehicles) into an optimal number of clusters (ONC) by using Eigen gap heuristic. In a given cluster, a vehicle will be selected as a CH, if it has a maximum Eigen-centrality score. Based on the CVoEG model, a reliable routing scheme called CEG-RAODV is proposed to find the most reliable journey (MRJ) from source to destination. Our simulation results show that the proposed scheme significantly outperforms the existing schemes in terms of reliability, reliable routing request (RRR), packet delivery ratio (PDR), end to end (E2E) delay, and throughput.

This paper investigates the power allocation problems for non-orthogonal multiple access with coordinated direct and relay transmission (CDRT-NOMA), where a base station (BS) communicates with its nearby user directly, while communicating with its far user only through a dedicated relay node (RN). The RN is assumed to operate in either half-duplex relaying (HDR) mode or full-duplex relaying (FDR) mode. Based on instantaneous channel state information (CSI), the dynamic power allocation problems under HDR and FDR schemes are formulated respectively, with the objective of maximizing the minimum user achievable rate. After demonstrating the quasi-concavity of the considered problems, we derive the optimal closed-form power allocation policies under the HDR scheme and the FDR scheme. Then, a hybrid relaying scheme dynamically switching between HDR and FDR schemes is further designed. Moreover, we also study the fixed power allocation problems for the considered CDRT-NOMA systems based on statistical CSI so as to optimize the long-term system performance. The simulations show that our proposed power allocation policies can significantly enhance the performance of CDRT-NOMA systems.

In this paper, the joint design of user clustering and power allocation is investigated in a downlink non-orthogonal multiple access-based millimeter wave (mm-wave-NOMA) system. To reduce the system overhead, hybrid precoding techniques are adopted at the base station by using the channel state information of cluster heads (CHs) only. In order to maximize the sum rate of the system, Stackelberg game-based optimization problems are formulated for two cases with different quality of some targets: Case 1 focuses on improving the data rates of CHs; and Case 2 aims to increase the rates of cluster members when the CHs' data rates are caped. With the aid of coalitional game theory, a low complexity algorithm is proposed to dynamically allocate users into different clusters. Then, the optimal power allocation coefficients of the users in each cluster are obtained by the derived closed-form expressions. The properties of the proposed joint algorithms are analyzed in terms of Stackelberg equilibrium, the complexity, the convergence and the stability. The simulation results demonstrate that: 1) the proposed algorithms can significantly improve the sum rate and reduce the outage probability of the mm-wave-NOMA system and 2) the application of NOMA in mm-wave systems is capable of achieving promising gains over conventional orthogonal multiple access-based frameworks in both cases.

In this paper, the joint design of user clustering and power allocation is investigated in a downlink non-orthogonal multiple access based millimeter wave (mmWave-NOMA) system. In order to maximize the sum rate of the system, the Stackelberg game based optimization problem is formulated. With the aid of game theory, a low complexity algorithm is proposed to dynamically allocate users into different clusters. By deriving the closed-form expressions, the optimal power allocation coefficients of the users in each cluster are obtained. The properties of the proposed joint algorithm, including complexity, convergence and stability, are analyzed. Simulation results demonstrate that: i) the proposed algorithm can significantly improve the sum rate and reduce the outage probability of the mmWave- NOMA system; ii) the application of NOMA in mmWave systems is capable of achieving promising gains over conventional orthogonal multiple access (OMA) based frameworks in both cases.

This paper investigates the impact of non-orthogonal multiple access (NOMA) on wireless caching. Two NOMA caching strategies are developed, namely the push-then- deliver strategy and the push-and-deliver strategy, with the objective to improve the spectral efficiency of the two caching phases, content pushing and content delivery, compared to the conventional orthogonal multiple-access (OMA) based strategy. Both analytical and computer simulation results are provided to demonstrate the performance of the proposed caching strategies and verify the accuracy of the developed analytical results.

Machine learning has been used to develop efficiently optimizing algorithms for practical communication systems. This paper investigates the user clustering and power allocation problem in the millimeter wave non-orthogonal multiple access (mmWave-NOMA) transmission scenario, where we assume that the users' locations of different clusters follows a Poisson cluster process (PCP). Specifically, we develop a machine learning based user clustering algorithm for the application of NOMA. Moreover, to investigate the performance of the proposed mmWave-NOMA system, we derive the optimal power allocation coefficients in closed-form by assuming equal power on each beam. In the simulation results, we firstly investigate the impact of the number of clusters on the system performance. We further show the validation of the proposed machine-learning based user clustering algorithm in the mmWave-NOMA system.

In the future vehicular communication scenarios, real-time video streams should be provided to drivers and passengers with significantly strict delay guarantees. With cache-enabled roadside units (RSUs), video contents can be obtained from local caches instead of remote servers, which can help to avoid large playback delay resulting from limited backhaul capacity. However, the existing works mainly focus on the average delay with cache-enabled RSUs in steady scenarios where the content delivery rates are assumed to be constant. In this paper, a stochastic network calculus (SNC) based method is proposed to obtain the stochastic playback delay upper bounds of vehicular video content delivery networks with cache-enabled RSUs. Consider the stochastic characteristics of wireless channels, the stochastic strict service curve of RSU to vehicle (R-V) channel, and equivalent base station (BS) to RSU to vehicle (B-R-V) channel are derived, respectively. With this framework, the stochastic playback delay upper bounds are obtained. Simulation results prove that the upper bounds are reasonably tight. Compared with the conventional RSUs, the superiority of cache-enabled RSUs to guarantee stochastic playback delay performance is validated.

This paper proposes 3D non-stationary multipleinput multiple-output (MIMO) geometry-based stochastic models (GBSMs) for high-speed train (HST) tunnel channels. Considering the line-of-sight (LoS), single-bounced (SB), and double-bounced (DB) components from the geometrical tunnel scattering model, a reference HST tunnel channel model under the assumption that scatterers are uniformly distributed on the tunnel walls is first derived. Then, by using the modified method of equal areas (MMEA), the corresponding simulation model is developed. Based on the proposed tunnel channel models, the correlation properties in time and space domains are investigated. A good agreement of statistical properties between the reference model and simulation model can be obtained. Furthermore, the simulation results show that the proposed model can be applied to mimic the nonstationarity of HST tunnel channels.

In this paper, an improved coordinated multichannel media access control (IC-MAC) scheme by efficient use of idle service channels is proposed for vehicular ad-hoc networks (VANETs). Different from the existing coordinated multichannel MAC (C-MAC) scheme based on IEEE 802.11p and 1609.4, an improved mechanism for service channel reservation and service message transmission is introduced in the proposed IC-MAC schemes. With the proposed mechanism, the idle service channel of the reservation time can be used to transmit service messages when the quality-of-service (QoS) of safety messages is guaranteed. To evaluate the performance of the IC-MAC scheme, the throughput and delay of service messages are derived. Simulation results demonstrate that the throughput and delay performance can be improved by using the proposed IC-MAC scheme.

Recently, it has been shown that the time-varying multiple-access channel (MAC) with perfect channel state information (CSI) at the receiver and delayed feedback CSI at the transmitters can be modeled as the finite state MAC (FS-MAC) with delayed state feedback, where the time variation of the channel is characterized by the statistics of the underlying state process. To study the fundamental limit of the secure transmission over multi-user wireless communication systems, we re-visit the FS-MAC with delayed state feedback by considering an external eavesdropper, which we call the finite state multiple-access wiretap channel (FS-MAC-WT) with delayed feedback. The main contribution of this paper is to show that taking full advantage of the delayed channel output feedback helps to increase the secrecy rate region of the FS-MAC-WT with delayed state feedback. Moreover, by a degraded Gaussian fading example, we show the effects of feedback delay and channel memory on the secrecy sum rate of the FS-MAC-WT with delayed feedback.

Conventional wireless caching assumes that content can be pushed to local caching infrastructure during off-peak hours in an error-free manner; however, this assumption is not applicable if local caches need to be frequently updated via wireless transmission. This paper investigates a new approach to wireless caching for situations in which the cache content has to be updated during on-peak hours. Two non-orthogonal multiple access (NOMA)-assisted caching strategies are developed, namely, the push-then-deliver strategy and the push-and-deliver strategy. In the push-then-deliver strategy, the NOMA principle is applied to push more content files to the content servers during a short time interval reserved for content pushing during on-peak hours and to provide more connectivity for content delivery, compared with the conventional orthogonal multiple access (OMA) strategy. The push-and-deliver strategy is motivated by the fact that some users’ requests cannot be accommodated locally and the base station has to serve them directly. These events during the content delivery phase are exploited as opportunities for content pushing, which further facilitates the frequent update of the files cached at the content servers. It is also shown that this strategy can be straightforwardly extended to device-to-device caching, and various analytical results are developed to illustrate the superiority of the proposed caching strategies compared with OMA based schemes.

Ultra-dense networks (UDNs) are the promising technology for the fifth-generation wireless networks and beyond to significantly boost network capacity and improve network coverage by exploiting spatial spectrum reuse through the deployment of massive base stations (BSs). In this paper, the fundamental tradeoffs of non-orthogonal multicast, multicast, and unicast in the UDN are studied, to understand the impact of network densitification on them and provide some insights on UDN deployment. Non-orthogonal multicast with imperfect channel estimation and successive interference cancellation is also investigated. To evaluate the performance, a tractable model for performance analysis is developed by using stochastic geometry, and then the analytical expressions for downlink signal-to-interference-plus-noise ratio coverage probability, spectrum efficiency, area traffic capacity, and energy efficiency are derived. The numerical results together with the Monte Carlo simulations are also provided. The results demonstrate that non-orthogonal multicast can further improve the performance of multicast and achieve higher spectrum efficiency, area traffic capacity, and energy efficiency than unicast from the low-to-high BS density regions, but suffers from inferior performance to unicast in the very high BS density region. The results also show that the non-orthogonal multicast and multicast exhibit different performance trends from unicast.

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access scheme to be used in fifth-generation wireless networks. In this paper, multiple-input and multiple-output (MIMO) techniques are applied to NOMA systems by considering two types of imperfect channel state information-channel distribution information (CDI) and channel estimation uncertainty. Based on the two considered channel models, the power allocation and beamforming vectors are jointly designed to maximize the system utility of MIMO-NOMA, subjected to probabilistic constraints. Due to the implementation of successive interference cancellation in NOMA, the power allocation coefficients of the users in each cluster become coupled, which complicates the rate outage probability constraints and results in two challenging non-convex problems. For the optimization problem under CDI, we propose an efficient successive convex approximation (SCA) algorithm based on first-order approximation and semidefinite programming (SDP). For the optimization problem under channel estimation uncertainty, a new algorithm for the joint power allocation and receive beamforming design is developed to maximize the system utility based on SCA and an efficient 1-D search. In addition, the convergence and the feasibility are discussed for the two formulated problems. Furthermore, an efficient method to find a feasible initial solution is provided. Finally, the presented simulation results validate that the proposed two algorithms outperform MIMO-orthogonal multiple access and MIMO fixed NOMA (MIMO F-NOMA) with fixed power allocation and beamforming.

Millimeter-wave non-orthogonal multiple access (mm-wave-NOMA) systems exploit the power domain for multiple accesses to further enhance the spectral efficiency. User clustering and power allocation can effectively exploit the potential of NOMA in mm-wave systems. This paper investigates the sum rate maximization problem of mm-wave-NOMA systems under the constraints of the total transmission power and users' predefined rate requirements. The formulated optimization problem is a non-linear programming problem and, thus, is non-convex and challenging to solve, especially when the number of users becomes large. Sparked by the correlation features of the users' channels in mm-wave-NOMA systems, we develop a K-means-based machine learning algorithm for user clustering. Moreover, for a practical dynamic scenario where the new users keep arriving in a continuous fashion, we propose a K-means-based online user clustering algorithm to reduce the computational complexity. Furthermore, to further enhance the performance of the proposed mm-wave-NOMA system, we derive the optimal power allocation policy in a closed form by exploiting the successive decoding feature. Simulation results reveal that: 1) the proposed machine learning framework enhances the performance of mm-wave-NOMA systems compared to the conventional user clustering algorithms and 2) the proposed K-means-based online user clustering algorithm provides a comparable performance to the conventional K-means algorithm and strikes a good balance between performance and computational complexity.

This paper investigates the application of non-orthogonal multiple access (NOMA) in millimeter wave (mm-Wave) communications by exploiting beamforming, user scheduling, and power allocation. Random beamforming is invoked for reducing the feedback overhead of the considered system. A non-convex optimization problem for maximizing the sum rate is formulated, which is proved to be NP-hard. The branch and bound approach is invoked to obtain the ∈-optimal power allocation policy, which is proved to converge to a global optimal solution. To elaborate further, a low-complexity suboptimal approach is developed for striking a good computational complexity-optimality tradeoff, where the matching theory and successive convex approximation techniques are invoked for tackling the user scheduling and power allocation problems, respectively. Simulation results reveal that: 1) the proposed low complexity solution achieves a near-optimal performance and 2) the proposed mm-Wave NOMA system is capable of outperforming conventional mm-Wave orthogonal multiple access systems in terms of sum rate and the number of served users.

Quality of experience (QoE) is an important indicator in the fifth generation (5G) wireless communication systems. For characterizing user-base station (BS) association, subchannel assignment, and power allocation, we investigate the resource allocation problem in multi-cell multicarrier non-orthogonal multiple access (MC-NOMA) networks. An optimization problem is formulated with the objective of maximizing the sum mean opinion scores (MOSs) of users in the networks. To solve the challenging mixed integer programming problem, we first decompose it into two subproblems, which are characterized by combinational variables and continuous variables, respectively. For the combinational subproblem, a 3-D matching problem is proposed for modeling the relation among users, BSs, and subchannels. Then, a two-step approach is proposed to attain a suboptimal solution. For the continuous power allocation subproblem, the branch and bound approach is invoked to obtain the optimal solution. Furthermore, a low complexity suboptimal approach based on successive convex approximation techniques is developed for striking a good computational complexity-optimality tradeoff. Simulation results reveal that: 1) the proposed NOMA networks is capable of outperforming conventional orthogonal multiple access networks in terms of QoE and 2) the proposed algorithms for sum-MOS maximization can achieve significant fairness improvement against the sum-rate maximization scheme.

Caching popular files at the user equipments (UEs) provides an effective way to alleviate the burden of the backhaul networks. Generally, popularity-based caching is not a system-wide optimal strategy, especially for user mobility scenarios. Motivated by this observation, we consider optimal caching with the presence of mobility. A cost-optimal caching problem (COCP) for device-to-device (D2D) networks is modeled, in which the impact of user mobility, cache size, and total number of encoded segments are all taken into account. The hardness of the problem is proved via a reduction from the satisfiability problem. Next, a lower-bounding function of the objective function is derived. By the function, an approximation of COCP (ACOCP) achieving linearization is obtained, which features two advantages. First, the ACOCP approach can use an off-the-shelf integer linear programming algorithm to obtain the global optimal solution, and it can effectively deliver solutions for small-scale and medium-scale system scenarios. Second, and more importantly, based on the ACOCP approach, one can derive a lower bound of global optimum of COCP, thus enabling performance benchmarking of any sub-optimal algorithm. To tackle large scenarios with low complexity, we first prove that the optimal caching placement of one user, giving other users' caching placements, can be derived in polynomial time. Then, based on this proof, a mobility aware multi-user algorithm is developed. Simulation results verify the effectivenesses of the two approaches by comparing them to the lower bound of global optimum and conventional caching algorithms.

We consider the detection of an integer vector x̂ ∈ ℤnfrom the linear observation y = Ax̂ + v, where A ∈ ℝm×nis a random matrix with independent and identically distributed (i.i.d.) standard Gaussian N (0, 1) entries, and ν ∈ ℝmis a noise vector with i.i.d. N (0, σ2) entries with given σ. In digital communications, x̂ is typically uniformly distributed over an n-dimensional box B. For this detection problem, successive interference cancellation decoders are popular due to their low complexity, and a detailed analysis of their word error rates (WERs) is highly useful. In this paper, we derive closed-form WER expressions for two cases: (1) x̂ ∈ℤnis fixed and (2) x̂ is uniformly distributed over B. We also investigate some of their properties in detail and show that they agree closely with simulated word error probabilities.

This paper investigates the power allocation problem of full-duplex cooperative non-orthogonal multiple access (FD-CNOMA) systems, in which the strong users relay data for the weak users via a full duplex relaying mode. For the purpose of fairness, our goal is to maximize the minimum achievable user rate in a NOMA user pair. More specifically, we consider the power optimization problem for two different relaying schemes, i.e., the fixed relaying power scheme and the adaptive relaying power scheme. For the fixed relaying scheme, we demonstrate that the power allocation problem is quasi-concave and a closed-form optimal solution is obtained. Then, based on the derived results of the fixed relaying scheme, the optimal power allocation policy for the adaptive relaying scheme is also obtained by transforming the optimization objective function as a univariate function of the relay transmit power $P_R$. Simulation results show that the proposed FD- CNOMA scheme with adaptive relaying can always achieve better or at least the same performance as the conventional NOMA scheme. In addition, there exists a switching point between FD-CNOMA and half- duplex cooperative NOMA.

This paper investigates the application of nonorthogonal multiple access (NOMA) in millimeter wave (mmWave) communication with beamforming, user selection and power allocation. To overcome the burden of feedback, random beamforming to mmWave NOMA systems is considered. We then formulate an optimization problem to maximize the sum rate of the proposed mmWave NOMA systems, which is nonconvex. To solve the challenging problem, we invoke the branch and bound (BB) technique to develop an optimal power allocation algorithm. Then a low complexity suboptimal algorithm based on matching theory is proposed to realize user selection. Simulation results are provided for validating the effectiveness of the proposed algorithms and to show that the sum rate performance of the mmWave NOMA systems can be substantially improved by the proposed algorithms compared to the conventional mmWave orthogonal multiple access (OMA) systems.

Caching popular files at user equipments (UEs) provides an effective way to alleviate the burden of the backhaul networks. Generally, popularity based caching is not a system-wide optimal strategy, especially for mobility scenarios. Motivated by this observation, an optimal caching problem with respect to user mobility is investigated. To be specific, a cost-optimal caching problem (COCP) for device-to-device (D2D) networks is formulated, in which the impact of user mobility, cache size, and total number of encoded file segments are considered. Compared with the related studies, our investigation guarantees that the collected segments are non-overlapping, takes into account the cost of downloading from the network, and provides a rigorous complexity analysis. For problem solving, we first prove that the optimal caching placement of one user, giving other users' caching placements, can be derived in polynomial time. Then, based on this proof, a fast yet effective caching placement algorithm for all users is developed. Simulation results verify the effectiveness of this algorithm by comparing it to conventional caching algorithms.

This paper studies the power minimization problem for non-orthogonal multiple access (NOMA) downlink systems, in which nodes are equipped with multiple antennas. We develop a joint power allocation and receive beamforming algorithm using the fixed-point update power allocation method under two types of channel state information (CSI). Particularly, we first assume that perfect CSI is available for the base station (BS) and the users, where a closed-form expression for every receive detection vector is derived. Then, we consider only channel distribution information (CDI) is known to the BS and the users, where one-dimension search and semi-definite programming (SDP) relaxation are used to derive the receive detection vectors.

In this paper, the application of non-orthogonal multiple access (NOMA) to millimeter-wave (mmWave) communications is considered, where random beamforming is used in order to reduce the system overhead. Stochastic geometry is used to characterize the performance of the proposed mmWave-NOMA transmission scheme, by using the key features of mmWave systems, e.g., mmWave transmission is highly directional and potential blockages will thin the user distribution. The provided analytical and numerical results demonstrate that the combination of NOMA and mmWave yields significant gains in terms of sum rates and outage probabilities, compared to conventional orthogonal multiple access based mmWave systems.

We study the optimum designs of a wireless ad hoc network that employs a random multiple access protocol and operates in an interference-limited environment. The nodes are assumed to form a homogeneous Poisson point process (PPP). With the random multiple access protocol, each node transmits with a certain probability, the value of which is critical to system performance. A higher transmission probability means more transmission opportunities for a node, but at the cost of more interference from other nodes in the network due to more simultaneous transmissions. For half-duplex nodes that cannot transmit and receive at the same time, a higher transmission probability will also decrease the probability that a node is in the receiving mode. The objective of this paper is to identify the optimum transmission probabilities of the ad hoc network. Two performance metrics are considered, the connectivity probability and the random access transport capacity (RATC), which are derived as closed-form expressions of system parameters by considering the effects of channel propagations, interference, and the stochastic geometric of node distributions. The optimum transmission probabilities that can maximize the connectivity probability or RATC are then obtained for half-duplex and full-duplex systems, respectively.

The power allocation for multi-user sparse code multiple access (SCMA) downlink systems with finite- alphabet constraints is investigated. An explicit expression for the achievable rate for downlink SCMA systems with finite-alphabet inputs is derived, which is applicable to arbitrary number of users. Moreover, a novel power allocation scheme that can ensure users' fairness for multi-user SCMA downlink systems is proposed. In an effort to solve the formulated non-convex optimization problem, a low- complexity polynomial algorithm is proposed, which yields an optimal solution. Simulation results demonstrate that the proposed power allocation algorithm is capable of enhancing the performance significantly compared to the equal power allocation scheme.

In this paper, the challenging problem of data detection for orthogonal frequency division multiplexing (OFDM) systems under rapidly time- varying channels is considered. Time-varying channels within a multicarrier symbol will lead to a loss of sub-channel orthogonality, and result in inter-channel interference (ICI) and an irreducible error floor in traditional receivers. The genetic algorithm (GA) assisted data detection for the single input single output (SISO) and single input multiple output (SIMO) OFDM systems are presented, respectively. Theoretical analysis and simulations show that the proposed algorithms are valid compared with minimum mean square error (MMSE) and minimum mean square error successive interference cancellation (MMSE-SIC). The GA-assisted data detection for OFDM systems under rapidly time- varying channels is flexible to provide tradeoff between performance and complexity. To accelerate the convergence of GA for SIMO OFDM, the individuals of GA are selected based on the concept of Pareto optimality.

In this paper, three-dimensional (3D) regular-shaped geometry-based stochastic model (RS-GBSM) is developed for wideband multiple-input multiple- output (MIMO) high speed railway communication system under non-isotropic scattering condition. Correlations of the proposed model in space, time and frequency are studied. Numerical result shows that the simulation model fits the reference model well.

The convergence of communications, caching and computing (i.e., 3C) has attracted a lot of interests recently. To save communication resources, caching has been introduced into information-centric communication networks. Meanwhile, computing has also been exploited to improve the efficiency of communication networks. However, the combination of both communication, caching and computing is still at its infancy and significant research challenges remain to be addressed. Different from the previous works, we consider a virtualized 3C network, in which multiple service providers (SPs) are able to share the physical resources (i.e., bandwidth, storage and computation resources) from the same infrastructure provider (InP), and the users can dynamically choose the services from different SPs to maximize their utility. The dynamics of resource sharing between multiple SPs and users are modeled via a game theoretical approach. More specifically, the evolution and the dynamic behaviors of users are modeled as an evolutionary game. The competition among the SPs is formulated as a non-cooperative game, and an iterative algorithm is proposed to obtain its Nash equilibrium. Simulation results are presented to show the dynamics and equilibrium of the proposed game model.

Non‐orthogonal multiple access (NOMA) is a promising multiple access scheme to enhance the spectrum efficiency of fifth generation networks. In this study, the authors study a downlink multiple‐input single‐output (MISO) system combined with NOMA, where a single beamforming (BF) vector is shared by a group of users. A joint BF and power allocation algorithm is proposed to maximise the sum rate of the users with better channel conditions while guaranteeing the quality of service at the user with poor channel conditions. The formulated problem for sum rate maximisation can be shown as non‐deterministic Polynomial‐time‐hard, and therefore an effective solution based on branch and bound (BB) techniques is proposed. Numerical results are provided to verify that the proposed NOMA‐BF system with the BB algorithm improves the sum capacity significantly compared with the NOMA‐BF system with zero forcing.

Multimedia broadcast/multicast service (MBMS) transmission, which distributes the media content to multiple users on the same radio resources by using point-to-multipoint communications, is a highly spectrum efficient mechanism for multimedia communications. In this paper, we study the application of power domain non-orthogonal transmission to MBMS enhancements in a K-tier heterogeneous network, in order to satisfy the ever-increasing demands for emerging applications and performance requirements. Then, we present non-orthogonal multi-rate MBMS transmission (NOMRMT) and non-orthogonal multi-service MBMS transmission schemes and investigate their performance by using stochastic geometry. A tractable mode is developed to analyze the performance of asynchronous and synchronous non-orthogonal MBMS transmission. Based on this model, analytical expressions for the signal-to-interference-plus-noise ratio coverage probability, average number of served users, and sum rate are derived. The results demonstrate that non-orthogonal MBMS transmission can achieve better performance than the orthogonal one, while synchronous non-orthogonal MBMS transmission is superior to the asynchronous one.

Millimeter wave (mmWave) wireless networks can operate in single-cell point-to-multipoint mode to provide local multicast services efficiently. In this paper, the performance of multicast mmWave wireless networks is studied, through stochastic geometry. Then, the use of power domain non-orthogonal multiple access (NOMA) for enhancing mmWave multicasting is also investigated. Furthermore, we study multicasting in two-tier mmWave heterogeneous networks, and propose a novel cooperative NOMA multicast scheme. Analytical expressions for the signal-to-interference-plus-noise ratio coverage probability, the average number of served users, and the sum multicast rate are derived, in order to assess the performance of these schemes. Finally, we discuss the maximum sum multicast rates, by formulating them as optimization problems, and also develop efficient golden section search algorithms to solve them. The offered solutions reveal the impact of data transmission rate and power allocation on the sum multicast rate. Both analytical and numerical results demonstrate that NOMA can significantly improve the mmWave multicasting, while the proposed cooperative NOMA mmWave multicast scheme can further improve the NOMA mmWave multicasting.

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.

For the sake of supporting massive connectivity in the future 5G networks, non-orthogonal multiple access (NOMA) techniques are advocated. As a promising NOMA technique, in recent years sparse code multiple access (SCMA) has attracted substantial attention. However, there is a paucity of studies on the theoretical analysis of its error-freely achievable data rate, especially, in the downlink context. Hence, we derive the cutoff rate of SCMA in downlink broadcast channels, which indicates the lower-bound of a system's error-freely achievable rate. However, we will demonstrate that when considering the conventional categorization of pairwise error events, the accuracy of the cutoff rate rapidly degrades in the low-SNR region owing to the fact that multi-user SCMA systems typically encounter an extremely large constellation size. Alternatively, by invoking Bergmans' concept from 1973 in the categorization of pairwise error events, we obtain a more accurate cutoff rate both in the low- SNR regions and the high-SNR regions. Moreover, we provide insights into the cutoff rate derivation process, which reveals some general guidelines for designing a beneficial codebook, capable of improving SCMA with respect to its original low-density signature-based counterpart.

In this paper, a cooperative non-orthogonal multiple access (NOMA) network is considered, where a source communicates with two users through an energy harvesting relay. The impact of two types of NOMA power allocation policies, namely NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), on the considered cooperative simultaneous wireless information and power transfer (SWIPT) system is investigated. In particular, closed-form expressions for the outage probability and their high SNR approximations are derived to characterize the performance of SWIPT-F-NOMA and SWIPT-CR-NOMA. These developed analytical results demonstrate that the two power allocation policies realize different tradeoffs between the reception reliability, user fairness and system complexity. Compared with the conventional SWIPT relaying networks with orthogonal multiple access (OMA), the proposed NOMA schemes can effectively reduce the outage probability, although all of them realize the same diversity gain.

In this paper, we study the connectivity probability of linear multi-hop ad hoc networks operating in an interference-limited environment. The nodes in the network are assumed to form a Poisson point process (PPP), and they generate mutual interference during communications. A node communicates to other nodes in the network by using its intermediate neighbors as relays. The average node density plays a crucial role on the communication quality. A higher node density means shorter transmission distance for each hop, but also a stronger interference from surrounding nodes. We characterize this tradeoff relationship by deriving the analytical probability that all nodes in a finite section of the network are connected, that is, the signal-to-interference ratios (SIRs) of all adjacent node pairs in the finite network section are no less than a threshold. The connectivity probability is expressed as an explicit closed-form function of various system parameters, such as average node density, the length of the network section, pathlosss exponent, and the SIR threshold. It is shown through theoretical analysis that the connectivity probability is a quasi-concave function of the node density, and the optimum node density that can maximize the connectivity probability is identified.

In this paper, we study the end-to-end delay of a linear multihop network with mobile relays located on the line connecting the source-destination pair. The network operates in an interference-limited environment, where the interfering signals are from nodes with positions following the Poisson point process (PPP) on a two-dimensional plane. Due to node mobilities, the relay nodes are assumed to move randomly on the source-destination line by following a random waypoint mobility (RWPM) model. Based on the statistical properties of RWPM, we first analyze the probability distributions of the inter-node distance of the mobile relays. The results are then used to derive the exact analytical expressions of the end-to-end delay between a given source-destination pair. The analytical results explicitly quantify the impacts of several system parameters on the end-to-end delay, including the source-destination distance, the number of relays, the propagation parameters, and the density of interferers, etc. The optimum number of relays that can minimize the end-to-end delay is obtained. Both analytical and simulation results show that the mobility of the relay nodes can decrease the end-to-end delay.

Sparse code multiple access (SCMA) is a new non- orthogonal multiple access scheme, which effectively exploits the shaping gain of multi-dimensional codebook. In this paper, a multi-dimensional SCMA (MD-SCMA) codebook design based on constellation rotation and interleaving method is proposed for downlink SCMA systems. In particular, the first dimension of mother constellation is constructed by subset of lattice Z2. Then the other dimensions are obtained by rotating the first dimension. Further, the interleaving is used for even dimensions to improve the performance in fading channels. In this way, we can design different codebooks for the aim of spectral efficiency or power efficiency. And the simulation results show that the bit error rate (BER) performance of MD-SCMA codebooks outperforms that of the existing SCMA codebooks and low density signature (LDS) in downlink Rayleigh fading channels.

In high speed rail wireless networks, handover between evolved Node Bs(eNBs) occurs frequently, and the probability of handover failure is dramatically increased, thus seriously degrading the users' experience. To tackle this issue, this paper proposes a network-assisted mobile relay (MR)- controlled fast handover scheme. Under this scheme, the serving eNB provides, in advance, the network assistance information about the available resources of the target eNB, to MR. The MR then makes the handover decision based on continual measurements. Different from the conventional handover scheme, in the proposed scheme, the handover decision is made by the MR instead of eNB. Simulation results show that this scheme can effectively improve handover performance, in terms of handover failure and communication interruption probabilities.

Vehicular Ad-hoc Network (VANETs) is a collection of intelligent vehicles aims to get control over transportation problems and reduce accidental ratio. Clustering refers to the classification of vehicles in groups in order to minimize the traffic overhead by selecting a responsible node called Cluster Head. In this paper, we introduce a new Triple cluster based routing protocol (TCRP) for cluster formation & Vehicle Head (VH) selection by using modified K- Means & Floyd-Warshall algorithm. By assuming truncated normal distribution, the confidence interval of vehicles velocities is defined. The modified K-means divides the vehicles into three clusters within their velocities confidence range. Floyd-Warshall algorithm calculates the shortest distance for all pair of vehicles of VANETs. A vehicle having smallest average distance to the rest of vehicles & having least variance in velocity will be selected as Vehicle Head (VH). Our analysis and simulation results show that the proposed TCRP makes the cluster shape consistent and avoid reselection of VH in further rounds, thus forming quite stable groups of vehicles.

Interference alignment (IA) is a well-known transmission strategy for interference channels, with which the sum rate can linearly scale with the number of users at high signal-to-noise ratio (SNR). However, most existing works on IA considered only the maximization of the sum rate without taking into account the bit error rates (BER) performance. In this work, we firstly propose a novel IA algorithm to improve the BER performance for MIMO-OFDM interference channels on a per-subcarrier basis. The proposed IA algorithm is then combined with the sparse code multiple access (SCMA) technique to further enhance the BER performance by exploiting the inherent frequency diversity in the MIMO-OFDM systems. At the receiver, a linear filter is first used to mitigate the inter-user aligned interference and separate the spatial streams, and then an iterative frequency-domain message passing algorithm (MPA) is applied to efficiently separate the overlapped multiple codewords. Simulation results demonstrate that the proposed joint IA and SCMA strategy can provide significant BER gain compared to the existing IA algorithms.

With the high speed railway (HSR) systems becoming increasingly popular and the explosive growth in the number of mobile devices, the provision of a better user experience on high speed trains is a pressing problem. The user experience includes not only the transmission rate, but also the transmission delay caused by handover failure. The special linear topology of HSR system leads to the fact that the transmission rate has high dependence on the position of trains. However, the linear topology also makes it possible to implement efficient power allocation (PA) along the railway to improve the system performance. In this paper, we seek to find an appropriate PA scheme to achieve the trade-off between the transmission rate and the delay. A sub- optimum PA scheme is proposed which can, under a certain delay constraint, maximize the average capacity. The simulation results show that the proposed PA scheme can reduce the delay at the cost of slightly reduced capacity, hence providing some insights into the optimal system design.

In this paper, a general framework of the multiple- input multiple-output (MIMO) transceiver design for 5G multiple access (5GMA), including both non- orthogonal multiple access (NOMA) and sparsity code multiple access (SCMA), is developed to enhance the system throughput of next generation of communication systems. By applying generalized singular value decomposition (GSVD), MIMO channels can be decomposed into multiple single-input single- output (SISO) channels, to which the concept of NOMA and SCMA can ideally be applied. GSVD based precoding is proposed to both uplink and downlink 5GMA transmissions, and simulation results are provided to demonstrate the performance of the proposed schemes.

In this paper, an optimized codebook design based on rotated angles and extrinsic information transfer (EXIT) chart for a non-orthogonal multiple access scheme, called irregular sparse code multiple access (IrSCMA), is presented. Unlike regular SCMA, in IrSCMA, the defined degree of layer's resource nodes is different, which is beneficial to system performance.It is demonstrated that the new codebook can greatly improve the BER performance especially when the signal-to-noise ratio(SNR) is high, without sacrificing the low detection complexity, compared with the existing codebooks and LDS.

In this letter, we study a downlink non-orthogonal multiple access (NOMA) transmission system, where only the average channel state information (CSI) is available at the transmitter. Two criteria in terms of transmit power and user fairness for NOMA systems are used to formulate two optimization problems, subjected to outage probabilistic constraints and the optimal decoding order. We first investigate the optimal decoding order when the transmitter knows only the average CSI, and then, we develop the optimal power allocation schemes in closed form by employing the feature of the NOMA principle for the two problems. Furthermore, the power difference between NOMA systems and OMA systems under outage constraints is obtained.

In this paper, a downlink single-cell non-orthogonal multiple access (NOMA) network with uniformly deployed users is considered and an analytical framework to evaluate its performance is developed. Particularly, the performance of NOMA is studied by assuming two types of partial channel state information (CSI). For the first one, which is based on imperfect CSI, we present a simple closed-form approximation for the outage probability and the average sum rate, as well as their high signal-to-noise ratio (SNR) expressions. For the second type of CSI, which is based on second order statistics (SOS), we derive a closed-form expression for the outage probability and an approximate expression for the average sum rate for the special case two users. For the addressed scenario with the two types of partial CSI, the results demonstrate that NOMA can achieve superior performance compared to the traditional orthogonal multiple access (OMA). Moreover, SOS-based NOMA always achieves better performance than that with imperfect CSI, while it can achieve similar performance to the NOMA with perfect CSI at the low SNR region. The provided numerical results confirm that the derived expressions for the outage probability and the average sum rate match well with the Monte Carlo simulations.

We propose a novel two-timeslot two-way full-duplex (FD) relaying scheme, in which the access link and the backhaul link are divided in the time domain, and we study the average end-to-end rate and the outage performance. According to the user equipment capability and services, we investigate two scenarios: three-node I- and four-node Y-relaying channels. Among various relaying protocols, the well-known amplify-and-forward and decode-and-forward are considered. Closed-form expressions for the average end-to-end rate and the outage probability, under the effect of residual self-interference and inter-user interference, are presented. The results show that the proposed two-timeslot two-way FD relaying scheme can achieve higher rate and better outage performance than the half-duplex one, when residual self-interference is below a certain level. Therefore, this relaying scheme presents a reasonable tradeoff between performance and complexity, and so, it could be efficiently used in the fifth-generation wireless networks.

The accurate statistical modeling of synthetic aperture radar (SAR) images is a crucial problem in the context of effective SAR image processing, interpretation, and application. In this paper, a semi-parametric approach is designed within the framework of finite mixture models based on the generalized Gamma distribution in view of its flexibility and compact form. Specifically, we develop a generalized Gamma mixture model to implement an effective statistical analysis of high-resolution SAR images and prove the identifiability of such mixtures. A low-complexity unsupervised estimation method is derived by combining the proposed histogram-based expectation-conditional maximization algorithm and the Figueiredo-Jain algorithm. This results in a numerical maximum-likelihood (ML) estimator that can simultaneously determine the ML estimates of component parameters and the optimal number of mixture components. Finally, the state-of-the-art performance of this proposed method is verified by experiments with a wide range of high-resolution SAR images.

In this paper, a novel dynamic power allocation scheme is proposed to downlink and uplink non-orthogonal multiple access (NOMA) scenarios with two users for more flexibly meeting various quality of service requirements. The exact expressions for the outage probability and the average rate achieved by the proposed scheme, as well as their high signal-to-noise ratio approximations, are established. Compared with the existing works, such as NOMA with fixed power allocation and cognitive radio inspired NOMA, the proposed scheme can: 1) strictly guarantee a performance gain over conventional orthogonal multiple access; and 2) offer more flexibility to realize different tradeoffs between the user fairness and system throughput. Monte Carlo simulation results are provided to demonstrate the accuracy of the developed analytical results and the performance gain of the proposed power allocation scheme.

Abstract In this paper, we consider switch‐and‐stay combining (SSC) in two‐way relay systems with two amplify‐and‐forward relays, one of which is activated to assist the information exchange between the two sources. The system operates in either analog network coding (ANC) protocol where the communication is only achieved with the help of the active relay or time‐division broadcast (TDBC) protocol where the direct link between two sources can be utilized to exploit more diversity gain. In both cases, we study the outage probability and bit error rate (BER) for Rayleigh fading channels. In particular, we derive closed‐form lower bounds for the outage probability and the average BER, which remain tight for different fading conditions. We also present asymptotic analysis for both the outage probability and the average BER at high signal‐to‐noise ratio. It is shown that SSC can achieve the full diversity order in two‐way relay systems for both ANC and TDBC protocols with proper switching thresholds. Copyright © 2014 John Wiley & Sons, Ltd.

The aim of this paper is to study how to enlarge the performance gap between non-orthogonal multiple access (NOMA) and conventional orthogonal multiple access (MA) by applying user pairing. Particularly two user pairing schemes achieving different quality of service (QoS) requirements, namely NOMA with fixed power allocation (F-NOMA) and cognitive radio inspired NOMA (CR-NOMA), are proposed. For F-NOMA, both analytical and numerical results are provided to demonstrate that F-NOMA can offer a larger sum rate than orthogonal MA, and the performance gain of F-NOMA over conventional MA can be further enlarged by selecting users whose channel conditions are more distinctive. For CR-NOMA, the QoS requirements for users with poorer channel conditions can be guaranteed since the transmit power allocated to other users is constrained following the concept of cognitive radio networks. Because of this constraint, CR-NOMA has different behavior compared to F-NOMA, as demonstrated by the developed analytical results.

This paper studies the maximum Doppler diversity order of high mobility systems operating in the presence of imperfect channel state information (CSI). High mobility of wireless terminals causes large Doppler spread, which provides Doppler diversity. However, channel estimation errors are usually inevitable in such systems due to fast time-varying fading caused by large Doppler spread, and they will negatively impact system performance. We study the fundamental tradeoff between Doppler diversity and channel estimation errors by identifying the exact analytical expression of the maximum Doppler diversity order achievable in the presence of imperfect CSI. The analytical study is enabled by a simple repetition code at the transmitter and a new optimum diversity receiver, which is developed by analyzing the statistical properties of channel estimation errors. The analytical results show that if the energy of pilot symbols scales linearly with that of data symbols, then systems with imperfect CSI can achieve the same Doppler diversity order as those with perfect CSI. A non-linear scaling between the energy of pilot and data symbols always results in a loss of Doppler diversity for systems with imperfect CSI.

This paper studies spectral efficient Doppler diversity transmissions in the presence of imperfect channel state information (CSI). Fast time-varying fading in high mobility communication systems introduces Doppler diversity that can benefit system performance. On the other hand, it is more difficult to estimate and track fast time-varying channel, thus channel estimation errors might seriously degrade system performance in high mobility systems. We propose a practical pilot- assisted system design that can balance the tradeoff between Doppler diversity and channel estimation errors. At the transmitter, the data symbols are precoded with a rate-1 Doppler domain multiplexing scheme to achieve maximum Doppler diversity without sacrificing spectral efficiency. At the receiver, the information is detected by using imperfect CSI and a block decision feedback equalizer (BDFE), which is developed by exploiting the statistical properties of channel estimation errors. The analytical pairwise error probability (PEP) and a bit error rate (BER) lower bound are developed by considering a large number of design parameters, such as the percentage of pilot symbols in the transmitted symbols, the energy allocation between pilot and data symbols, and the maximum Doppler spread of the channel. Both simulation and analytical results show that maximum Doppler diversity can be achieved through optimizations of the various design parameters in the presence of imperfect CSI.

This research studies the optimum system designs of high mobility wireless communication systems with imperfect channel state information. In high mobility systems, the channel estimation error is usually inevitable and might have significant impacts on system performance. The impacts of channel estimation errors on system performance are quantified through the development of a spectral efficiency lower bound. With the help of the asymptotic analysis of the channel estimation mean squared error, the spectral efficiency lower bound is expressed as explicit functions of a number of system parameters, such as the maximum Doppler frequency, the percentage of pilots in the transmitted symbols, and the ratio of energy allocated between data symbols and pilot symbols. The optimum pilot percentage and energy allocation factor that jointly maximise the spectral efficiency lower bound are identified through analytical studies. It is discovered that, in term of maximising spectral efficiency, the optimum performance can be achieved by optimising the energy allocation factor when the pilots sample the fast time‐varying fading at its Nyquist rate.

The objective of this paper is to quantify the fundamental tradeoff between Doppler diversity and channel estimation errors in high mobility systems. Fast fading variation in high mobility systems introduces Doppler diversity that can benefit system performance. On the other hand, it is more difficult to estimate and track a fast changing channel, thus channel estimation errors are non-negligible and they may seriously degrade system performance. Such a tradeoff relationship is studied by identifying the exact analytical expressions of two performance metrics: the maximum Doppler diversity order achievable with imperfect channel state information (CSI), and the loss in signal-to-noise ratio (SNR) caused by channel estimation errors. The analytical study is enabled by a simple repetition code at the transmitter, and a new optimum diversity receiver developed by analyzing the statistical properties of channel estimation errors. With the analytical results, the optimum allocation of transmission energy between pilot symbols and data symbols are obtained to simultaneously maximize the Doppler diversity order and minimize the SNR loss, thus achieve the optimum tradeoff between the two. The results reveal the fundamental performance limits of Doppler diversity systems operating in the presence of imperfect CSI, and they can be used to guide the design of practical systems.

In this paper, we study a downlink multiuser network in which one base station sends its message to one of multiple users, assisted by one of multiple intermediate decode-and-forward (DF) relays. Multiple eavesdroppers exist at the receiver side, and they can overhear the message from the base station, which brings out the issue of information security. We consider moderate shadowing environments so that direct links from the base station to the users and eavesdroppers exist. Maximal ratio combing (MRC) technique is employed at the receivers to combine the signals from the relaying and direct links. We select the best user and relay pair by maximizing the received signal-to-noise ratio (SNR) at the user. We study the effects of the selection on the system secrecy performance by deriving the closed-form expression of secrecy outage probability. We also provide the asymptotic secrecy outage probability with high transmit power and high main-to-eavesdropper ratio (MER). From the asymptotic expression, we can find that the system can achieve the diversity of the total number of users and relays, irrespective of the number of eavesdroppers. Simulation and numerical results are finally demonstrated to verify the proposed studies.

This paper studies the maximum Doppler diversity transmissions in the presence of imperfect channel state information (CSI) in high mobility systems. Due to fast time-varying fading in high mobility systems, channel estimation error is usually inevitable and they might have significant impacts on system performance. On the other hand, Doppler spread caused by fast time-varying fading introduces Doppler diversity that can benefit system performance. The fundamental tradeoff between channel estimation errors and Doppler diversity are studied by deriving the analytical symbol error rate (SER), which captures both effects in a single expression. It is shown that conventional maximal ratio combining (MRC) receiver is no longer optimum with channel estimation errors. A new optimum receiver that can collect the maximum Doppler diversity with imperfect CSI is developed by analyzing the statistical properties of the estimated channel coefficients. The SER of the new receiver is then derived and evaluated. The analytical and simulation results are used to identify the system parameters that can render the optimum tradeoff between Doppler diversity and channel estimation error.

In this paper, a new partial power and rate adaptation scheme for orthogonal frequency division multiplexing (OFDM) systems is proposed in the presence of carrier frequency offset (CFO). The conventional adaptive scheme is shown to be a special case of the partially adaptive scheme technique which enables the resulting non-convex optimization problem, solved in a feasible way. It leads to a solution for optimal power adaptation that maximizes the spectral efficiency of an OFDM system using M-ary quadrature amplitude modulation (MQAM) under average power and instantaneous BER constraints. Closed-form expressions for the average spectral efficiency (ASE) of adaptive OFDM systems are derived. The theoretical results and computer simulations show that the range of the partial adaptation becomes narrow and the performance of constant power and continuous rate is very close to that of the partially adaptive power and continuous rate for higher CFO or high signal noise ratio (SNR) values.

This paper studies the performance and design of high speed railway communications that employ massive MIMO systems. The users onboard the train communicate with the BS by using antennas mounted on top of the train as gateways. Two antenna layouts on the train are considered, one has co-located antennas (CA) at the center of the train, and the other has distributed antennas (DA) uniformly placed along the train. The analytical uplink ergodic capacities for both layouts are derived by averaging over all the locations in a cell. For the CA layout, the average uplink cell capacity is concave in the number of antennas on the train, and the optimum number of antennas on the train is identified by maximizing the average uplink capacity. For the DA layout, the average cell capacity is upper and lower bounded by two concave functions, which are then used to find a sub-optimum value of the number of antennas on the train. Both analytical and numerical results demonstrate that the CA layout has a higher average cell capacity while the DA layout has less variations in terms of uplink capacity at different locations inside a cell.

This study focuses on the design of downlink transmission protocols in multi‐cell multi‐user mobile networks, where co‐channel interference has been recognised as a challenging issue particularly for the users close to the boundary of cells. The key idea of this study is to jointly apply interference alignment (IA) and pre‐cancellation to the addressed scenario, where the former technique can effectively increase the overall system throughput and the latter can significantly boost the diversity gains and reception reliability. To ensure the diversity gains can be achieved with zero‐forcing IA, a precoder optimisation scheme is proposed based on the well‐known iterative interference leakage minimisation scheme. Both analytic and numerical results have been developed to show the capacity and diversity gains obtained by using the proposed scheme. Besides, the computational complexity of the proposed scheme and the effects of imperfect channel state information on the performance are studied as well.

A new detection method for data‐dependent superimposed training with equi‐spaced amplitude modulation or equi‐spaced square quadrature amplitude modulation is presented in this study. Symbol and bit error floor of the proposed detection method is analysed. To remove the error floor, a data coding method is also proposed. Analysis and simulation results show that the proposed detection method outperforms the existing methods, and the data detection performance can be further improved via data coding.

The performance of non-orthogonal multiple access (NOMA) is investigated in a cellular downlink scenario with randomly deployed users. The developed analytical results show that NOMA can achieve superior performance in terms of ergodic sum rates; however, the outage performance of NOMA depends critically on the choices of the users' targeted data rates and allocated power. In particular, a wrong choice of the targeted data rates and allocated power can lead to a situation in which the user's outage probability is always one, i.e. the user's targeted quality of service will never be met.

In this paper, we consider a two-way relaying scenario with one pair of source nodes, one relay and one eavesdropper. All nodes are equipped with multiple antennas, and we study the impact of antenna selection on such a secure communication scenario. Three transmission schemes with different tradeoff between secure performance and complexity are investigated respectively. Particularly, when antenna selection is implemented at the relay and no artificial noise is introduced, the condition to realize secure transmissions is established. Then by allowing the sources to inject artificial noise into the system, the secure performance is evaluated by focusing on different eavesdropping strategies. When both the relay and the sources send artificial noise, a low complexity strategy of antenna selection is proposed to efficiently utilize the antennas at the sources and the relay. The developed asymptotic results demonstrate that, by adding more artificial noise and performing joint antenna selection, a better secure performance, such as a larger secrecy rate and a lower outage probability, can be realized at a price of imposing more complexity on the system. Simulation results are also provided to demonstrate the accuracy of the developed analytical results.

In this paper, switch-and-stay combining (SSC) is considered in two-way relay systems with amplify-and-forward (AF) relays, among which one is activated to assist the information exchange between two sources. We assume that the two-way relay system operates in the analog network coding (ANC) protocol where the communication can only be achieved through the active relay without the direct link. The outage probability and the average bit error rate (BER) for Rayleigh fading channels are studied. In particular, we derive closed-form lower bounds for the outage probability and the average BER, which is tight for different fading conditions. Moreover, we present asymptotic analysis for both the outage probability and the average BER at high signal-to-noise ratio (SNR) region. It is shown that SSC can achieve the full diversity order in two-way relay systems with proper switching thresholds. Numerical and simulation results are also provided to verify the theoretical study.

This study analyses the delay‐limited throughput performance of an uplink cellular relay network utilising hybrid automatic repeat request (HARQ) protocol in which one mobile station (MS) communicates with one base station (BS) with the aid of one amplify‐and‐forward relay station (RS). The authors’ analysis allows for a practical fading scenario where MS → RS and RS → BS channels undergo independent Rayleigh and Rician fading, respectively. Delay‐limited throughput expressions for two HARQ protocols are derived, respectively, namely Repetition Time Diversity and Incremental Redundancy. Furthermore, the impact of different parameters on the achievable throughput, such as the maximum HARQ round number L , line‐of‐sight signal K , information rate R etc., is analysed. The authors’ analysis unveils that, for the presumed dual‐hop cooperation model with the specified tolerable delay requirement and given channel condition, there exists an optimal information rate R * that achieves the maximum delay‐limited throughput for the two HARQ protocols. Moreover, it is disclosed that, both HARQ protocols with the adaptive optimised information rate outperform the counterpart with the fixed information rate in low and moderate SNR regions, suggesting that an adaptive transmission scheme should be employed to achieve a better throughput performance.

Synthetic aperture radar (SAR) images are inherently affected by multiplicative speckle noise, which will degrade the human interpretation and computer-aided scene analysis. In this paper, we propose a novel Bayesian multiscale method for SAR image despeckling in the non-homomorphic framework. To address the multiplicative nature, we first make the speckle contribution additive by a linear decomposition. Then, in the stationary wavelet transform domain, a two-sided generalized Gamma distribution (GTD) is introduced as a prior to capture the heavy-tailed nature of wavelet coefficients of the noise-free reflectivity. By exploiting this prior together with a Gaussian likelihood, an analytical wavelet shrinkage function is derived based on maximum a posteriori criteria, which further adopts heterogeneity-adaptive thresholding technique to achieve better estimates of noise-free wavelet coefficients. Moreover, a pilot-signal-assisted strategy is proposed to estimate the parameters of two-sided GTD with the estimator based on second-kind cumulants. Finally, experimental results, carried out on the synthetic and actual SAR images, are given to demonstrate the validity of the proposed despeckling method.

The security and privacy preservation issues are prerequisites for vehicular ad hoc networks. Recently, secure and privacy enhancing communication schemes (SPECS) was proposed and focused on intervehicle communications. SPECS provided a software-based solution to satisfy the privacy requirement and gave lower message overhead and higher successful rate than previous solutions in the message verification phase. SPECS also presented the first group communication protocol to allow vehicles to authenticate and securely communicate with others in a group of known vehicles. Unfortunately, we find out that SPECS is vulnerable to impersonation attack. SPECS has a flow such that a malicious vehicle can force arbitrary vehicles to broadcast fake messages to other vehicles or even a malicious vehicle in the group can counterfeit another group member to send fake messages securely among themselves. In this paper, we provide a secure scheme that can achieve the security and privacy requirements, and overcome the weaknesses of SPECS. Moreover, we show the efficiency merits of our scheme through performance evaluations in terms of verification delay and transmission overhead.

This paper studies the multi-cell cooperative transmission based on interference alignment (IA). A new macro-diversity approach is presented in which each BS not only transmits data to its serving users but also transmits data to the users belonging to the cooperative BSs. By coherently designing the precoding and detection matrices, desired signals from multi-BSs are detected to achieve macro-diversity while undesired signals are aligned to the same dimension before zero-forced by the detector. Additionally, an iterative algorithm based on the well-known leakage interference minimization approach is presented for the implementation of interference alignment in macro-diversity scenario. Simulation results show the proposed scheme has better outage probability performance at the high signal-to-noise ratio (SNR) region compared with conventional interference alignment, confirming the macro-diversity is achieved.

In this paper, the effect of power and rate adaptation on the spectral efficiency of orthogonal frequency division multiplexing (OFDM) systems using M-ary quadrature amplitude modulation (MQAM) is investigated, in the presence of the fast fading channels, under power and instantaneous bit error rate (BER) constraints. A lower bound on the maximum spectral efficiency of adaptive OFDM/MQAM systems is obtained, together with a closed-form expression for the average spectral efficiency of adaptive OFDM systems. Theoretical and numerical results show that the adaptive MQAM/OFDM systems under fast fading channel have substantial gain in spectral efficiency over the non-adaptive counterparts.

Although V-OFDM system and DFT scrambling vector OFDM system are suitable for frequency-selective fading channels, channel estimation error may significantly degrade the achievable BER performance. In this paper, a new frequency-domain scrambling differential detection and equalization is proposed for DFT-SV-OFDM system, where a scrambling decision feedback is used at the receiver to provide a reliable noise-reference signal, thus reducing the error propagation. Compared with the two existing systems, the new scheme shows a comparable performance but more robust against the channel estimation error.

Existing work has demonstrated that network coding can significantly reduce system delay in multi-source and multi-destination scenarios. However, such a scheme doesn't take consideration in terms of throughput In this paper, we propose to use rateless coding scheme to network-coded multi-source and multi-destination scenarios, which is a practical minimizing system delay and maximizing throughput approach. In particular, the analytical result based on outage probability has been developed. Numerical results have also been provided to show the delay and throughput performance of the proposed protocol.

Abstract In this paper, large code division multiple access (CDMA) random access systems employing the decorrelator and minimum mean square error (MMSE) detectors are investigated over Rayleigh fading channels under the assumption that both the number of users and the spreading gain tend to infinity, but their ratio converges to a constant. The signal to interference ratio (SIR) is shown to converge almost surely to a constant and the bit‐error rate (BER) is expressed as a function of the traffic load, transmission probability, channel coefficient, and distribution of transmission power. Furthermore, the throughput, the spectrum efficiency, and the stability region are analyzed and simulated. For dominating systems, it is shown that the MMSE detector achieves much higher throughput and spectral efficiency than decorrelator detector. Besides, it is also disclosed that, when the signal to noise ratio (SNR) is larger than an optimum value, the spectrum efficiency increases as the ratio of bit energy to noise power spectrum density (Eb/N0) increases; however, when SNR is smaller than the optimum value, the spectrum efficiency decreases as Eb/N0 increases. For ordinary stable systems, it is demonstrated that their stability region gets narrower as the traffic load increases. Copyright © 2010 John Wiley & Sons, Ltd.

This paper proposes a pre-equalized transmission scheme based on a finite field transform. Unlike orthogonal frequency division multiplexing (OFDM) which transmits signals on orthogonal sub- carriers at the transmitter and explores post-equalization to migrate the effect of multi-path fading at the receiver, in the proposed scheme, signals are processed with the finite field transform called basefield Hartley transform (BHT) before transmission. Since BHT has similar convolution property as discrete Fourier transform (DFT), the inter-symbol interference (ISI) can be mitigated by applying a finite field pre-equalizer and quantization pre-equalizer at the transmitter. By allowing the padded redundancy to take values in finite field, the proposed scheme has the inherent error correction capability with various coding rate. Since the received signals are equivalent to certain block error correction codes, only a decoding scheme is required to recover the source data without any DFT operations that are used in conventional OFDM receiver, which leads to a simple receiving structure with low process complexity. Our simulation results show that the proposed scheme reduces the peak-to-average power ratio (PAPR) and provides better bit error rate (BER) performance at the high signal-to-noise ratio (SNR) in comparison with the coded OFDM system.

Relay is one of the key technologies used by both the Third Generation Partnership Project (3GPP) Long Term Evolution-Advanced (LTE-Advanced) and IEEE 802.16m to ameliorate cell edge throughput. Despite the throughput enhancement achieved by deploying relay stations (RSs), wireless systems supporting relay are vulnerable to frequent handoffs, which deteriorate the dropping performance of realtime communications. This paper proposes a mathematical model to appraise the dropping performance of the eNB-first channel access policy in LTE-Advanced relay networks. The eNB-first policy prefers an eNB-channel over a RS-channel when determining an access channel for a new and a handoff call. Closed-form analytical result is obtained for the dropping probability of the eNB-first policy and based on the result a numerical study is conducted to evaluate the influences of various parameters on the dropping performance. The result developed in this paper can be used to adjust the call admission control strategies to get acceptable dropping performance in LTE-Advanced relay networks.

A general construction of inter-group complementary (IGC) codes is proposed based on perfect complementary (PC) codes, interleaving operation, and the orthogonal matrix. The correlation properties of the newly constructed IGC codes can be described as follows: (1) the autocorrelation sidelobes of the codes are zeros in the zero correlation zone (ZCZ); (2) the cross-correlation functions (CCFs) between any two different codes of the same group are zeros in the ZCZ; (3) the CCFs between any two codes of different groups are zeros everywhere. The key point of this construction is that the ZCZ length of the generated IGC codes can be chosen flexibly. It is well known that there is a limitation between the ZCZ length and the number of mates; that is, the smaller is the length of ZCZ, the more are the IGC codes that can be generated. Therefore, if we can choose the ZCZ length of the IGC codes flexibly according to the requirement of the system, more users can be accommodated in the system.

The conventional binary complementary pairs exist only for very limited lengths, while binaryZ-complementary pairs exist for many more lengths. In this letter, an upper bound on zero correlation zone (ZCZ) width of binaryZ-complementary pairs of odd length is established. The existence conjecture of binaryZ-complementary pairs with ZCZ widths 2, 3, 4, 5 and 6 is completely solved, and a new recursive construction of binaryZ-complementary pairs is proposed.

Seven infinite classes of relative difference families with variable block sizes are presented explicitly. In particular, a balanced (gv,g,K,1)-DF withg=Σk∈K[(k2-k)/2] is explicitly given for: (i)K={3,4,5} and everyvcoprime to 6; (ii)K={3,4,6}, {3,5,6} or {3,4,5,6} and everyvcoprime to 30. As far as the authors are aware, these difference families can be viewed as the first explicit constructions of infinite classes of optimal variable-weight optical orthogonal codes with more than two weights. It is observed, however, that there are infinitely many values ofvfor which an optimal (v,W,1,Q) -OOC exists, whatever the set of weightsWand the weight distribution sequenceQare.

Variable-weight optical orthogonal code (OOC) was introduced by Yang for multimedia optical CDMA systems with multiple quality of service (QoS) requirements. In this paper, four general constructions for optimal cyclic packings and optimal variable-weight OOCs are presented. Many new infinite classes of optimal (v, W,1,Q)-OOCs are obtained. New infinite classes of optimal (v, W,1,Q)-OOCs for |W| ≥ 4 are easily obtained by the constructions.

The performance of two-way amplify-and-forward (AF) relaying networks over independently but not necessarily identically distributed (i.n.i.d.) Nakagami-m fading channels, with integer and integer plus one-half values of fading parameter m, is studied. Closed-form expressions for the cumulative distribution function (CDF), probability density function (PDF), and moment generating function (MGF) of the end-to-end signal-to-noise ratio (SNR) are presented. Utilizing these results, we analyze the performance of two-way AF relaying system in terms of outage probability, average symbol error rate (SER), and average sum-rate. Simulations are performed to verify the correctness of our theoretical analysis.

In this paper, an exponential model is proposed to approximate the error performance of Turbo codes within low and intermediate SNR region. Our analysis unveils that, the proposed exponential model offers a simple but effective method to approximate the waterfall region performance of Turbo codes, which is generally intricate for analytical bounding techniques. By combining the proposed exponential model with the well known union bound estimate, the paper derives a combined performance model to extend the performance estimate within moderate to high SNR region. Moreover, it is shown that, only the free distance term of the weight spectrum is not always enough for a reasonable estimate of the error floor region performance, especially when the free distance multiplicity is small. Numerical results are presented to validate the applicability of the proposed performance model.

In this letter, we introduce a simple approach to generate Z-periodic complementary set (ZPCS) by performing phase shift operation on periodic complementary sequence set (PCS). Favorable correlation properties of ZPCS enable its applications not only in MIMO channel estimation as optimal training sequences, but also in MC-CDMA system as spreading sequences to efficiently handle multipath interference (MI) and multiple access interference (MAI). Our approach provides flexible choices for zero correlation zone (ZCZ) length and set size. The resultant ZPCS is optimal with respect to the theoretical bound.

In the context of synthetic aperture radar (SAR) image processing and applications, the precise modeling of statistical knowledge is a crucial problem. In this paper, an efficient and flexible statistical model, called generalized Gamma Rayleigh (G¿R) distribution, for amplitude SAR images is proposed by assuming a two-sided generalized Gamma distribution for the real and imaginary parts of the complex SAR backscattered signal. It is shown that the Rayleigh and recently proposed generalized Gaussian Rayleigh distributions can be regarded as special cases of G¿R distribution. Considering that the probability density function estimation problem is formulated as a parameter estimation one for the parametric statistical analysis of SAR images, a two-stage estimator based on second-kind cumulants is derived for the parameters of G¿R distribution. Furthermore, experimental results on several actual SAR images are given to demonstrate the validity and flexibility of the proposed model.

In this paper, two types of multiple binary zero correlation zone (ZCZ) sequence sets are constructed: 1. Each set is a binary(2n+1Zcz,2n,Zcz)-ZCZ sequence set, which is one of the best cases in the known constructions. 2. Among all the sets, the sequences still possess good cross-correlation property within the zone of lengthZ≤Zcz. In particular, the first type sequence sets have a common zero correlation zone of lengthZ, which solves the previous research problem. These multiple binary ZCZ sequence sets are suitable for multiuser environments.

Variable-weight optical orthogonal code (OOC) was introduced by Yang for multimedia optical CDMA systems with multiple quality of service (QoS) requirements. In this paper, the upper bound on the size of variable-weight OOCs is improved, a cyclic$t\hbox{-}(v, W, \lambda , Q)$packing is introduced to construct a variable-weight OOC, an upper bound for the number of blocks of$t\hbox{-}(v, W, \lambda , Q)$packings is obtained, and an equivalence between optimal cyclic packing and optimal variable-weight optical orthogonal code is established. Recursive constructions for optimal$2\hbox{-}{\rm CP}(W, 1, Q;v)$s are also presented. By using skew starters and these constructions, infinite classes of optimal$(v, W, 1, \{1/2, 1/2\})$-OOCs are obtained for$W=\{3, 4\}$, and$\{4, 5\}$.

Variable-weight Optical orthogonal code (OOC) was introduced by G-C Yang for multimedia optical CDMA systems with multiple quality of service (QoS) requirement. In this paper, a construction for optimal variable-weight OOCs via cyclic difference families is given. Several new constructions for cyclic difference families are also given. By using these constructions, new optimal (n,W, 1,Q)-OOCs for 2 ≤ |W| ≤ 4 are constructed.

The random access systems, exploiting retransmission diversity (RD), over large random spreading CDMA channel subject to fading is investigated, under the assumption of infinite number of users and infinite spreading gain but with their ratio converging to a constant. The SIR, throughput and spectrum efficiency are analyzed. Simulation and numerical results show that RD exploitation can significantly increase the throughput and spectrum efficiency in channels both with and without fading.

In this note, a closed-form expression is derived for the outage probability considered by Min et al. [1].

An approach to generate generalized pairwise Z-complementary (GPZ) codes, which works in pairs in order to offer a zero correlation zone (ZCZ) in the vicinity of zero phase shift and fit extremely well in power efficient quadrature carrier modems, is introduced in this letter. Each GPZ code has MK sequences, each of length 4NK, where M is the number of Z-complementary mates, K is a factor to perform Walsh-Hadamard expansions, and N is the sequence length of the Z-complementary code. The proposed GPZ codes include the generalized pairwise complementary (GPC) codes as special cases.

In mobile communications, navigation, and radar applications, good periodic binary sequences are normally used with matched-filter technique. However, from the viewpoint of signal processing, there is no need to only consider the filter coefficients as binary. This letter presents a theoretical bound for real-valued mismatched sequence sets with zero correlation zone (ZCZ) and then proposes a method for constructing sets of optimal mismatched ZCZ sequences with respect to the theoretical bound for application in mismatched filtering.

A call admission control scheme named thinning scheme, which smoothly throttles the admission rates of calls according to their priorities and aims to provide multiple prioritized traffic with a desired quality of service, is investigated under more general conditions. Using the theory of multidimensional Markov birth-death process, analytical formulas for call blocking probabilities are derived.

In this paper, based on a belief-propagation decoding strategy, a class of generalized parity-check codes called complex rotary codes is investigated. It is shown that, by using iterative sum-product decoding, the complex rotary codes have a much lower decoding complexity than Turbo codes, but have almost the same performance for the high code rate and short frame case (frame length< 500 bits). It is also shown that the prime block size of complex rotary codes is essential to achieve better performance because of its uniform checking characteristic.

This paper proposes a blind watermarking algorithm based on the significant difference of wavelet coefficient quantization for copyright protection. Every seven nonoverlap wavelet coefficients of the host image are grouped into a block. The largest two coefficients in a block are called significant coefficients in this paper and their difference is called significant difference. We quantized the local maximum wavelet coefficient in a block by comparing the significant difference value in a block with the average significant difference value in all blocks. The maximum wavelet coefficients are so quantized that their significant difference between watermark bit 0 and watermark bit 1 exhibits a large energy difference which can be used for watermark extraction. During the extraction, an adaptive threshold value is designed to extract the watermark from the watermarked image under different attacks. We compare the adaptive threshold value to the significant difference which was quantized in a block to determine the watermark bit. The experimental results show that the proposed method is quite effective against JPEG compression, low-pass filtering, and Gaussian noise; the PSNR value of a watermarked image is greater than 40 dB.

In this paper, the requirement on optimal training sequences for single-carrier multi-antenna channel estimation with space-time block-coding is firstly derived based on time-domain analysis. It is then shown that the optimality requirement relates to a new concept called Z-periodic complementarity, and the desired optimal training sequences are in fact a class of Z-periodic complementary sequences which include the conventional periodic complementary sequences and L-perfect sequences as special cases. Definitions and constructions of the Z-periodic complementary binary sequences and their mates are given.

We study the downlink precoding schemes for MIMO Gaussian broadcast channels (MIMO GBC). A novel low-complexity zero-forcing dirty-paper-coding (DPC) scheme, named as Successive ZF-DPC, and a multiuser scheduling algorithm are proposed to asymptotically approach the sum capacity of MIMO GBC. After the ordered subset of active users determined by the scheduling algorithm, the successive ZF-DPC successively puts the precoding matrix of each user in the null space of the composite matrix stacked by channel matrices of preceding users in the subset such that each user is not subject to the interference caused by the following users in the subset. Then, DPC can be used to eliminate the interference caused by the preceding users, which ultimately nullifies the mutual interference among users. Our analysis and simulations show that the Successive ZF-DPC is asymptotically sum-rate optimal in both spatially uncorrelated and correlated channels even if each user has multiple antennas. Furthermore, it is shown that the multiuser scheduling algorithm can make efficient use of the multiuser diversity to increase the achievable maximal sum rate and accelerate the approach to the sum capacity of the MIMO GBC.

In this paper, a program is first divided into several data computing blocks (DCBs) by the branch instruction; each DCB can then be recognized as an image. We then use the one dimension discrete cosine transform (1-D DCT) to compute each DCB to generate several signatures including 5-bits relay DCT signature (R-DCT-S) and 32-bits final DCT signature (F-DCT-S). These generated signatures are embedded into the instruction memory and then used to do the run time error checking. The watchdog should not reduce the processor performance, not increase the fault detection latency and not increase the memory overhead to store the signatures; in this paper, the processor degradation can be improved by doing the whole block error checking after the branch instruction, the fault detection latency is improved by doing the intermediate error checking at the R-type instruction, and the memory overhead is reduced by storing the R-DCT-S to the R-type instruction. The experimental results show that the proposed watchdog has very high error detection coverage and shortest error detection latency to detect either single fault or multi-faults, no matter what the fault is transient or intermittent.

In this letter, Z-complementary sequences are introduced. These sequences include the conventional complementary sequences as special cases. It is shown that, different from the normal complementary pair of binary sequences which exist only for very limited lengths, i.e., 2a10b26cfor a,b,cges0, a Z-complementary pair of binary sequences exists for many more lengths. In addition, for a Z-complementary set with zero correlation zone Z, P binary sequences, each having length N, the maximum number of distinct Z-complementary mates is smaller than or equal to P[N/Z].

In this letter, a general construction of orthogonal variable spreading factor (OVSF) codes with zero correlation zone (ZCZ) property is presented based on orthogonal sequence sets and -complementary sets. It is shown that the proposed general construction includes the conventional binary and ternary OVSF codes as special cases and can accommodate more flexible rate assignment in direct sequence code division multiple access (DS-CDMA) systems.

In this correspondence, we present a family of binary 2nsequences of period 2(2n-1) where n is an integer, which can be seen as a generalization of nonlinear binary sequences obtained from Z4sequences and recently constructed GKW (Gold, Kasami, and Welch)-like sequences. The sequences have low correlations and are useful in code-division multiple-access (CDMA) communication systems and cryptography

In this paper, the inverse discrete Fourier transform (IDFT) results of oversampled Walsh Hadamard (WH) sequences and WH-based complementary (CP) sequences are derived, based on which, the peak factors (PF) of MC-CDMA signals are investigated for both the two sequence sets. The upper bounds of PF for both BPSK and QPSK modulation are presented in this paper and the PF bounds presented in B.J. Choi and L. Hanzo (2003) can be regarded as special cases of the bounds derived here. Compared with accurate computing which requires prohibitive computations, the upper bounds obtained provide a more effective way to estimate the PF of BPSK and QPSK modulated MC-CDMA signals employing WH or WH-based CP sequences. In addition, based on the IDFT properties of WH sequences, a simplified search for optimum WH subset in the context of BPSK modulation is derived.

In this letter, an implicit direct current (dc) free multiple-input multiple-output (MIMO) channel estimation scheme is proposed by employing a set of zero correlation zone (ZCZ) training sequences. The new implicit MIMO channel estimation scheme includes, as a special case, the conventional implicit single-input single-output (SISO) channel estimation scheme, which is also considered and analyzed. For implicit SISO channel estimation, by choosing properly a set of ZCZ sequences with balanced property, the harmful dc-offset can be completely removed, thus exhibiting significantly better estimation performance and resulting in simpler implementation when compared with the conventional implicit training schemes without dc-offset elimination and with dc-offset elimination based on perfect sequences, respectively

A new light-weight approach, based on the cross-correlation of system call sequences, is presented to identify normal or intrusive program behavior. The program behavior is represented by the cross-correlation value which can be used to indicate the similarity between two sequences. If two sequences are same, the cross-correlation between them will achieve the maximum value. This method of characterizing program behavior by using cross-correlation offers significant computational advantages over HMM (hidden Markov model) or NN (neural network) methods due to the absence of unnecessary training process. Our experiments using UNM (University of New Mexico) audit data show that the cross-correlation based method can effectively detect intrusive attacks and achieve a low false positive rate.

An important aspect of network security is to ensure the confidentiality, integrity, fairness and validity of the data on the network. Non-repudiation is one of the most important network security services and it is mainly used in information processing system and e-commerce. Currently, most non-repudiation protocols have the trusted third party and are connection-oriented. Robert et al. suggested a transmission-oriented non-repudiation protocol CMP. This protocol has some limitations: 1) Cannot make data integrity proof for transferred information; 2) When transmitted data M is prodigious, each step of protocol for information transmission will be prodigious too. In this paper, we proposed a new transmission-oriented electronic evidence and non-repudiation protocol TEENP based on a trusted third part resolved the repudiation problem for receiver and sender, and extended the semantics and reasoning rules of the belief logic BAN. We also analyzed TEENP protocol with logic BAN. The protocol is fair, secure and efficient, compared with the corresponding protocols.

In this correspondence, several errors related to the distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks in (B. Krishnamachari et al., 2004) are spotted and corrected.

In this correspondence, we present a new family of nonbinary sequences with three-level nontrivial correlations and large linear complexity. The sequences may be considered as nonlinear analogues of the well-known sequences by Trachtenberg and Helleseth. It is shown that the family is optimal with respect to the Welch bound in terms of root mean square of all nontrivial correlations. We also determine the correlation distribution of the new family.

In this paper, a new family of hopping sequences with very large family size suitable for use in time/frequency-hopping (TH/FH) code-division multiple-access (CDMA) systems is proposed and investigated. The construction of the new hopping sequences is based upon the polynomial theory over the finite field, and the new hopping sequences are called the polynomial hopping sequences. It is shown that the new time/frequency-hopping sequences include the well-known cubic hopping sequences, quadratic hopping sequences, and linear hopping sequences as special cases, and are good candidates for the large-capacity ultrawide-bandwidth (UWB) radio systems.

Two simple but effective methods for reducing the average complexity (and power consumption) of the conventional turbo-cyclic-redundancy-check decoding scheme with negligible performance degradation in a wideband direct-sequence code-division multiple-access (W-CDMA) environment are introduced. When applied to a W-CDMA turbo code with frame length 640 b at a bit-error rate (BER) of 10/sup -6/, the resultant modified schemes can save up to 73% of the average decoding complexity, relative to the conventional scheme. In general, the proposed schemes are more attractive for short-frame and low-BER applications.

This paper presents the generalized Udaya Sidiqi sequences which are the interleaved version of Gold like binary sequences of period satisfying the Welch bound maximum out of phase correlations. The sequences have large linear complexity and low correlations and are useful in code division multiple access (CDMA) communication systems and cryptography. A direct method to compute the correlation using the trace sequence representation and a sequence from a quadratic form are presented.

Frequency hopping (FH) sequences have found wide applications in various modern FH spread-spectrum communications and radar systems. In FH spread-spectrum communications, the interference occurs when two distinct transmitters use the same frequency simultaneously. In order to evaluate the goodness of FH sequence design, the Hamming correlation function is used as an important measure. In this correspondence, by considering separately the maximum Hamming autocorrelation sidelobe H/sub a/, and the maximum Hamming cross correlation H/sub c/, several new lower bounds on the size p of the frequency slot set F, the sequence length L, the family size M, and correlation properties are established. The new periodic bounds include the known Lempel-Greenberger bounds as special case when M=2, and are tighter than the Seay bounds under certain conditions when M>2. Furthermore, the new bounds disclose more information on the relationship between the maximum autocorrelation sidelobe and the maximum cross correlation compared with the Lempel-Greenberger bounds and Seay bounds. Besides, the aperiodic FH bounds which have not yet been previously reported are also presented and discussed in this correspondence.

In this paper, an adaptive coded modulation scheme associated with improved HARQ is proposed. In the scheme, non-uniform constellation Gray encoding for the modulation with alphabet size M>4 is adopted in the retransmissions. Combining with the symbol bits rearrangement before modulation mapping and non-uniform constellation for retransmission in HARQ scheme, the reliability of each bit inputting to the turbo decoder is averaged and improved. As a result, the system throughput is increased effectively, and it is noticed that the throughput of 1/2 code rate 8PSK with the improved HARQ is shown to be better than 3/4 code rate QPSK with conventional HARQ. So instead of 3/4 code rate QPSK, a novel adaptive coded modulation scheme associated with improved HARQ is proposed, and the system throughput gets a significant performance improvement.

In this paper, the performance of type III hybrid ARQ with RCPC codes is considered. The combining of multiple retransmitted RCPC codes in type III hybrid ARQ scheme leads to the so-called composite code combining. A new concept, i.e. combined puncturing pattern, is proposed to illuminate its effect. Due to the composite combining, different code symbol may exhibit different effective SNR owing to the accumulative effect, which can be described by the combined puncturing pattern. The closed-form of the pairwise error probability (PEP) is derived for the combined RCPC codes. It is shown that the PEP is dependent on the sum of the symbols' SNR of the incorrect path. Accordingly, the upper and lower bounds, along with the approximation of the symbols' SNR, are proposed to evaluate the residual error rate of the combined RCPC codes in type III hybrid ARQ scheme. By using the state-diagram analysis, we obtain the bounds and approximations of the performance of type III hybrid ARQ with RCPC codes. Finally, the applicability of both the proposed bounds and the approximation are validated through numerical analysis and computer simulations.

By using multiple repeated signal replicas to formulate the accumulative observed noisy signal sequence (AONSS) in the hybrid ARQ system, a novel data-aided maximum likelihood (DA ML) SNR estimation is proposed for the AWGN channel. Based on the AONSS, new lower hounds, i.e., the generalized deterministic and random Cramer-Rao lower bounds (GCRLB's), which include traditional Cramer-Rao lower bounds (CRLB'') as special cases, are derived. It is indicated that the conventional DA ML estimate is a special case of the novel DA ML estimate. An alternative differential observed noisy signal sequence (DONSS) is also proposed, yielding a blind ML SNR estimation technique. It is shown by numerical analysis and simulation results that both the proposed DA ML and the proposed blind ML SNR estimation techniques can offer satisfaction SNR estimation.

A simple but effective method for reducing the average complexity (and power consumption) of turbo decoding with negligible performance degradation in a W-CDMA environment is introduced. It modifies the conventional turbo-CRC decoding scheme by performing two CRC tests per iteration to detect at the earliest a correctly converged decoding process. When applied to a W-CDMA turbo code with frame length 640 bits at a BER of 10/sup -6/, it can save about 50% of the average decoding complexity, relative to the conventional scheme. Further complexity reduction can be achieved by integrating the CRC test as an intermediate step in the component SlSO decoder. In general, the proposed scheme is more attractive for short-frame and low-BER applications.

In this paper, a general periodic correlation bound named generalized Sarwate bound for complex roots of unity sequence set with zero or low correlation zone (ZCZ/LCZ), with respect to family size, sequence length, maximum autocorrelation sidelobe, maximum crosscorrelation value and the zero or low correlation zone, are derived. It is shown that all the previous periodic binary sequence bounds, such as Sarwate bound, Welch bound, Tang-Fan bound and Peng-Fan bound, are only special cases of the generalized Sarwate bound.

With the increase of teletraffic demands in mobile cellular system, hierarchical cellular systems (HCSs) have been adopted extensively for more efficient channel utilization and better GoS (Grade of Services). A practical issue related to HCS is to design a scheme for controlling and allocating call traffic to different layers. There are several strategies to deal with this problem, such as no call-overflow scheme, unidirectional call-overflow scheme and bidirectional call-overflow scheme. The objective of this paper is to investigate a bidirectional call-overflow scheme, based on the velocity of the mobile making the calls. To ensure that hand off calls are given higher priorities, it is assumed that guard channels are assigned in both macrocells and microcells. In order to evaluate the performance of the new scheme and compare the performance of several related schemes, two now models based on a one-dimensional Markov process are developed and analytical results are derived. Theoretical analysis and numerical evaluation show that the proposed scheme outperforms others in terms of average hew call blocking and hand off failure probability of the system. In addition, when the teletraffic to the HCS reaches a certain grade, the GoS is insensitive to the maximum velocity and the velocity threshold which is used to assign calls to different layers in our scheme.

Abstract The hybrid automatic repeats request (ARQ) scheme, together with code combining, is an effective way to provide reliable transmission of data packets over noisy channel. In this paper, it is shown that the application of rate‐compatible punctured convolutional (RCPC) codes in truncated type III hybrid ARQ scheme leads to the so‐called composite code combining. In order to illuminate the effect of the involved composite code combining, a new concept, i.e. combined puncturing pattern, is introduced. By state‐diagram analysis, it is revealed that the overall performance of the truncated type III hybrid ARQ with code combining is dependent on both the maximum transmission number and the residual error probability after code combining. By extending the analysis to the convolutional codes whose symbols exhibit different effective signal‐to‐noise ratio owing to the composite code combining, a closed form of the residual error probability of the combined RCPC codes is derived. Based on the combined puncturing patterns, upper and lower bounds on the error performance are derived, together with an approximation method which is shown to be quite satisfactory by numerical and simulation results. Copyright © 2003 John Wiley & Sons, Ltd.

A general construction for orthogonal sets of quadriphase sequences based on the sequence family A discovered by Sole (1989), Boztas, Hammons, and Kumar (1992) is presented. The sequence family A is equivalent to the S(0) family that belongs to a chain of sequence families S(i),i=0,1,2,..., m with each family in the chain containing the preceding family. Therefore, a number of orthogonal subsets can be generated for an arbitrary family S(m). The algorithm for an efficient implementation of the bank of correlators corresponding to any orthogonal subset of family S(m) is derived as well.

A new class of pseudonoise sequences over GF(p), based on Gordon-Mills-Welch (1962) sequences, is constructed. The sequences have the property that, in a specified zone, the out-of-phase autocorrelation and cross-correlation values are all equal to -1. Such sequences with low correlation zone (LCZ) are suitable for approximately synchronized code-division multiple-access (CDMA) system.

A signal design method for the uplink in cellular mobile CDMA systems is simulated. The simulated method uses binary complete complementary code as comb-spectrum pseudo-periodic sequences. The designed signals are without co-channel interference nor influence of multipath fading, when the system is synchronized approximately. Any approximation of synchronization is acceptable by arranging the signal design. The results of simulation shows us the usefulness of this signal design without co-channel interference for an approximately synchronized uplink in cellular CDMA systems.

Superimposed codes for multiple-access communication in a binary adder channel are analyzed. The superposition mechanism used in this correspondence is ordinary addition. Each user is assigned a codeword from a superimposed code. It is proved that every constant-weight code C of weight w and maximal correlation c corresponds to a subclass of a disjunctive code D of order m>

Disjoint difference sets with regard to the largest elements are used to construct multiple-burst error-correcting codes using majority-logic decoding. Almost all of D.M. Mandelbaum's (1972) codes are improved with shorter lengths. Because they are quasi-cyclic and majority-logic decodable, they are of practical interest.>

Disjoint difference sets (DDS), difference triangle sets (DTS), and related codes are discussed and a recursive construction for DDS is given. With this construction and the relationship between DDS and DTS, many new upper bounds for DTS and some better orthogonal codes are obtained.>

A new binary (22,13,5) quasi-perfect code is presented. The weight distributions of the coset codes are given. The code obtained is not equivalent to T.J. Wagner's code (1966). Four lower bounds for constant weight codes can be derived from it, which are better than the codes with the same parameters in R.L. Graham and N.J. Sloane (1980) and A.E. Brouwer et al. (1980).>

A lower bound on the length of binary self-orthogonal unequal error protection (UEP) codes is derived, and two design procedures for constructing optimal self-orthogonal UEP codes are proposed. With this lower bound, known self-orthogonal UEP codes can be evaluated. It is pointed out that, for given values of minimum distance and code rate, the self-orthogonal codes must be relatively long, so optimal self-orthogonal codes are not optimal in general. But self-orthogonal codes can be implemented simply, and they have error-correcting capabilities beyond those guaranteed by their minimum distance. These properties can be viewed as a partial compensation for using self-orthogonal codes.>