A novel jitter-robust integrated sensing and communication (ISAC)-enabled beam tracking framework is proposed for unmanned aerial vehicle (UAV)-to-UAV (U2U) communication systems. In particular, a tethered UAV base station (UAV-B) serves a maneuvering UAV user (UAV-U), where jitter disrupts beam alignment and degrades communication rates. To enable beam alignment, a UAV-U angle tracking method is first proposed to estimate the relative angles between UAV-B and UAV-U. Specifically, the tethered UAV-B leverages sensing echo signals to obtain motion information of UAV-U, and integrates them with UAV-U’s prior motion models to enhance the accuracy of angle prediction. Meanwhile, through the communication link, UAV-U can receive the direction of UAV-B. Building on the obtained angle information, a jitter compensation algorithm is further developed to maximize the achievable rate of the U2U communication systems. In particular, UAV-B employs adaptive beamwidth control (ABC) to adjust its beamwidth and thus ensures robust coverage for UAV-U. Meanwhile, UAV-U employs beam steering refinement (BSR) to optimize the direction based on predicted angle information, enhancing the received signal-to-noise ratio (SNR) and reducing beam misalignment caused by jitter. Finally, our numerical results unveil that: 1) higher angle prediction accuracy is achieved by the proposed UAV-U angle tracking scheme compared to conventional Kalman filter-based angle tracking methods; and 2) a higher achievable rate in the U2U communication systems is achieved by the proposed jitter compensation scheme compared with traditional beamwidth control-based methods.

Clock synchronization is a critical technology in wireless sensor networks (WSNs), providing the foundation support for various operations among network nodes. However, traditional timestamp-based synchronization methods suffer from insufficient synchronization accuracy and high communication overhead, making it difficult to meet the requirements of high-precision applications. Meanwhile, frequent information exchange significantly increases network energy consumption and limits the network lifespan. To address these issues, we propose an implicit synchronization estimation method based on pulse correlation detection. This method operates at the physical layer, leveraging the excellent correlation properties of pulse signals to avoid the frequent information exchanges required in traditional timestamp-based synchronization, thereby reducing communication overhead. Based on this, the phase offsets of active nodes are estimated and quantized, and then transmitted to the listening nodes by using pulse position modulation (PPM) technology. Finally, high - precision synchronization between the listening node and the reference node is achieved. The simulation results confirm the validity of our analysis.

As one of the key enabling technologies for the sixth-generation (6G) mobile communication system, near-field communication can significantly enhance the spectral efficiency of the communication system through the beam focusing effect. In order to effectively extend the near-field coverage so that more users can benefit from the near-field effect, the concept of modular array architecture is introduced. However, the traditional uniform modular array (UMA) has a significant grating lobe effect, leading to severe inter-user interference (IUI) problems. To effectively suppress the grating lobe interference and optimize the sum-rate of multi-user communication, a joint optimization algorithm for module position and beamforming based on the movable modular array is proposed in this paper. By establishing a joint optimization model for the module position and beamforming, a modified minimum mean square error beamforming algorithm and a dynamic particle swarm position optimization algorithm (MMSE-DPSO) are proposed. Simulation results show that the proposed algorithm can effectively suppress the grating lobes while improving the sum-rate of multi-user communication.

Integrated sensing and communication (ISAC), which can exploit the wireless spectrum for concurrent sensing and communication functions, is regarded as a promising technology for the future sixth generation (6G) wireless communication networks. This paper proposes a robust beam tracking method for maneuverable unmanned aerial vehicles (UAVs) with jittering effect within the ISAC framework. By utilizing reflected echoes, the kinematic parameters are measured and the interacting multiple model with extended Kalman filter (IMM-EKF) is designed for robust beam tracking of maneuverable UAVs. Furthermore, due to the jittering effect, the UAV may not point to the optimal alignment direction. To this end, we propose the coordinate descent particle swarm optimization (CDPSO) algorithm to balance the jittering effect by maximizing the received signal-to-noise ratio (SNR) of the UAV. The effectiveness of the proposed scheme is verified via simulation results.

This article introduces a novel concept of near-field integrated sensing and communication (ISAC) within a cognitive radio (CR) framework. The secondary ISAC transmitter, equipped with an extremely large-scale array, aims to minimize transmit power while meeting the requirements for both communication and sensing, as well as satisfying an interference constraint for the primary receiver. We initially solve the formulated nonconvex problem using a semidefinite relaxation approach to achieve a globally optimal solution. To reduce computational complexity, we propose two suboptimal beamforming strategies: 1) zero-forcing (ZF)-based and 2) maximum ratio transmission (MRT)-based beamforming designs. These approaches provide a practical tradeoff between performance and complexity by fixing beam directions according to the principles of ZF and MRT. Additionally, we develop a robust beamforming design under imperfect channel state information in the communication and interference channels, ensuring reliable performance across all possible channel realizations within the uncertainty bounds. Simulation results confirm the effectiveness of the proposed methods, demonstrating power-efficient joint communication and sensing capabilities within the CR scenario.

Orthogonal time frequency space (OTFS) has attracted a lot of attention as a feasible waveform applied in joint communication and radar (JCR) systems in contrast to orthogonal frequency division multiplexing (OFDM) waveform. To explore the advantages of OTFS waveform, first, a unified matrix (UM) expression is summarized by utilizing discrete fractional Fourier transform (DFrFT), and then a novel OTFS waveform based on UM expression is investigated in this article. The fractional order parameters of the proposed UM-OTFS waveform is set to the same values during preprocessing and Heisenberg transformation stages, and the UM-OTFS waveform can be converted into other waveform forms by undergoing different fractional order parameters. In addition, a three-stage sensing parameter estimation algorithm is developed for target velocity and range estimation through grid partitioning, coarse and fine estimation. Meanwhile, a low-complexity fractional zero force (ZF) or minimum mean square error (MMSE) equalizer based on lower-upper (LU) decomposition (LU-ZF/MMSE) is presented, which results in a log-linear order of complexity without any performance degradation of bite error ratio (BER) by analyzing sparsity and quasi-banded structure of the equivalent matrix. The simulation results indicate the superiority of the proposed UM-OTFS waveform in terms of sensing parameter estimation and BER performance compared with several advanced waveforms.

Sparse code multiple access (SCMA) and mobile edge computing (MEC) are two promising technologies for future Internet of Things (IoT) networks. SCMA enables large-scale connections, while MEC brings computing resources closer to user devices, resulting in faster response time and improved user experiences through task offloading. In this article, we investigate a large-scale grant-free (GF) SCMA enhanced MEC network. First, we propose the offloading protocol for the GF-SCMA enhanced MEC framework and describe the task offloading process using GF-SCMA in detail. Then, we model and analyze the performance of this network, deriving closed-form solutions for the offloading probability and SCMA ergodic rate using stochastic geometry. Additionally, we apply queueing theory to examine the impact of GF-SCMA on task latency and energy consumption in the MEC network. The accuracy of the theoretical expressions is confirmed by simulation results, demonstrating that SCMA outperforms orthogonal multiple access (OMA) in terms of increasing offloading probability and ergodic rate, as well as reducing task delay and energy consumption. Furthermore, this advantage becomes more pronounced with higher user density and task generation rate. Through parameter comparison, it is seen that increasing the pilot and codebook number of GF-SCMA can improve the performance of the proposed scheme in practical implementations.

Integrating sparse code multiple access (SCMA) and mobile edge computing (MEC) into the Internet of Things (IoT) networks can enable efficient connectivity and timely computation for resource-limited IoT users. This paper studies the computation rate maximization problem under task deadline constraints in dynamic SCMA-MEC networks. Specifically, we propose a predictive deep Q-network for SCMA resource allocation and computation offloading (PQ-RACO) algorithm for single-cell scenarios, where IoT devices use long short-term memory (LSTM) networks to predict the states and actions of other agents. However, the PQ-RACO algorithm is not scalable for increasing numbers of IoT devices. To address this issue, an improved multi-agent deep Q-network for SCMA resource allocation and computation offloading algorithm (MQ-RACO) is proposed for multi-cell scenarios. The algorithm is a centralized training and decentralized execution (CTDE) multi-agent reinforcement learning (MARL) algorithm with explicit rewards, which is tailored to the special structure of joint rewards. Simulation results demonstrate that the proposed algorithm outperforms several state-of-the-art MARL algorithms and other benchmark schemes in terms of convergence speed and computation rate.

Abstract Clock synchronization is indispensable for numerous applications of wireless sensor networks (WSNs). When no common reference clock is available, the nodes must employ distributed synchronization techniques. This paper proposes, a distributed pulse‐based clock synchronisation approach, wherein the propagation delay is eliminated through signal ping‐pongs between neighbouring nodes. Such an approach can jointly estimate the clock skew and offset without requiring any reference clock. The whole synchronization process is completed at the physical (PHY) layer, effectively avoiding the random delay caused by packet queuing and retransmission. Simulation results show that the proposed approach can achieve higher synchronization accuracy compared with other existing methods.

The application of sparse code multiple access (SCMA) to multi-access edge computing (MEC) networks can provide massive connections as well as timely and efficient computation services for resource-constrained Internet of Things (IoT) devices. This paper investigates the maximization of computation rate in SCMA-MEC networks under a dynamic environment. We first formulate an initial optimization problem to maximize the long-term computation rate of IoT devices under task delay constraints. Then, a joint computation offloading and SCMA resource allocation algorithm based on long short-term memory (LSTM) network and dueling deep Q network (DQN) is proposed. Specifically, each IoT device acts as an agent in the algorithm. Since each device can only observe part of the environment state, the LSTM network is used to predict the states of other devices. The computation rate of devices is taken as a reward to conduct action exploration in dueling DQN, and then the near-optimal computation offloading decision, SCMA codebook allocation, and power distribution of IoT users are obtained after training. Numerical simulation results demonstrate that the proposed algorithm can achieve higher computation rate compared with other baseline schemes.

Grant-free sparse code multiple access (GF-SCMA) is considered to be a promising multiple access candidate for future wireless networks. In this article, we focus on characterizing the performance of uplink GF-SCMA schemes in a network with ubiquitous connections, such as the Internet-of-Things (IoT) networks. To provide a tractable approach to evaluate the performance of GF-SCMA, we first develop a theoretical model taking into account the property of multiuser detection (MUD) in the SCMA system. Then, the error rate performance of GF-SCMA in the case of codebook collision is analyzed to investigate the reliability of GF-SCMA when reusing codebook in massive IoT networks. For performance evaluation, accurate approximations for both success probability and average symbol error probability (ASEP) are derived. To elaborate further, the analytical results are utilized to discuss the impact of codeword sparse degree in GF-SCMA. After that, we conduct a comparative study between SCMA and its variant, dense code multiple access (DCMA), with GF transmission to offer insights into the effectiveness of these two schemes. This facilitates the GF-SCMA system design in practical implementation. Simulation results show that denser codebooks can help to support more user equipments (UEs) and increase the reliability of data transmission in a GF-SCMA network. Moreover, a higher success probability can be achieved by GF-SCMA with denser UE deployment at low detection thresholds since SCMA can achieve overloading gain.

Sparse code multiple access (SCMA) is a kind of code-domain nonorthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multiaccess edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. In this article, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. Specifically, a system utility is first used to calculate the weighted energy consumption and task execution latency. The initial utility minimization problem is nonconvex and then can be subdivided into two tractable subproblems by fixing task offloading decisions, namely, optimal local computing via CPU frequency scheduling and optimal edge computing via the SCMA codebook assignment, subcarrier power allocation, and MEC server computing resources distribution. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed. Moreover, we come up with CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation (CRA) of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and task offloading can achieve a good compromise between time delay and energy consumption for IoT devices. It is demonstrated that the proposed strategy has a remarkable advantage compared to the previous SCMA-MEC schemes.

Sparse code multiple access (SCMA) is a kind of code-domain non-orthogonal multiple access (NOMA) scheme, which can support the increasing requirements for high spectral efficiency and massive connections. Meanwhile, multi-access edge computing (MEC) is a promising technology for providing resource-constrained users with computing resources. The integration of these two technologies can efficiently improve the computation service. In this paper, we propose a novel optimization scheme in the SCMA-based MEC network from the perspective of energy and latency for the Internet of Things (IoT) devices. To minimize the total overhead of devices, the communication and computation resources allocation, as well as computation offloading are jointly considered. Primarily, a joint SCMA codebook assignment based on the bidirectional matching principle and optimal power allocation algorithm is proposed to maximize the uplink transmit rate. Moreover, we put forward CPU frequency scheduling strategies utilizing convex optimization to optimize the computing resources allocation of local devices and the MEC server. Finally, a low-complexity task offloading policy based on simulated annealing is presented. Numerical results show that our proposed joint optimization algorithm for resource allocation and computation offloading can achieve a good compromise between time delay and energy consumption for IoT devices.

It is generally thought that space‐time block codes (STBCs) can obtain no more than full space diversity. In this study, the authors propose a new construction method of joint orthogonal STBCs based on M ‐dimensional lattice constellations for obtaining space and time diversities simultaneously. By deriving the Chernoff bound of error probability, they prove the exact diversity gain of the proposed code is M times of that in traditional STBCs. This is a valuable scheme as diversity gain is usually the primary factor to determine the ability of anti‐fading. Moreover, the maximum‐likelihood decoder for the proposed code just requires joint decoding of M real symbols, whose complexity is acceptable as M , usually, needs not to be too big. Numerical results show that the proposed code has remarkable improvement of performance compared with some typical STBCs under the comparable low decoding complexity.

HOSTA is an in-house high-order CFD software that can simulate complex flows with complex geometries. Large scale high-order CFD simulations using HOSTA require massive HPC resources, thus motivating us to port it onto modern GPU accelerated supercomputers like Tianhe-1A. To achieve a greater speedup and fully tap the potential of Tianhe-1A, we collaborate CPU and GPU for HOSTA instead of using a naive GPU-only approach. We present multiple novel techniques to balance the loads between the store-poor GPU and the store-rich CPU, and overlap the collaborative computation and communication as far as possible. Taking CPU and GPU load balance into account, we improve the maximum simulation problem size per Tianhe-1A node for HOSTA by 2.3X, meanwhile the collaborative approach can improve the performance by around 45% compared to the GPU-only approach. Scalability tests show that HOSTA can achieve a parallel efficiency of above 60% on 1024 Tianhe-1A nodes. With our method, we have successfully simulated China's large civil airplane configuration C919 containing 150M grid cells. To our best knowledge, this is the first paper that reports a CPUGPU collaborative high-order accurate aerodynamic simulation result with such a complex grid geometry.

The efficacy of widely used single instruction, multiple data architectures is often limited when handling divergent control flows and short vectors; both circumstances result in SIMD fragments that use only a subset of the available datapaths. This paper proposes a multiple SIMD, multiple data (MSMD) architecture with flexible SIMD datapaths that can be dynamically or statically repartitioned among multiple control flow paths, all executing simultaneously. The benefits are twofold: SIMD fragments resulting from divergent branches can execute in parallel, as can multiple kernels with short vectors. The resulting SIMD architecture can achieve the flexibility similar to a multiple instruction, multiple data architecture. We have both simulated the architecture and implemented a prototype. Our experiments with data-parallel benchmarks show that the architecture leads to 60% performance gains with an area overhead of only 3.06%.

In this paper, we propose a class of full diversity rate one space-time block codes (STBC) satisfying the generalized orthogonal constraint (GOC). First an explicit construction of completely decoupled STBC is proposed to obtain a rate one STBC with linear decoding complexity for any number of transmit antennas. Then we propose an adaptation strategy for the codes to achieve full diversity by utilizing partial phase information of the channel obtained via a feedback link. With a few feedback bits, the proposed rate one code has full diversity while reserving the same decoding complexity as Orthogonal STBCs. Moreover, the full diversity can be still achieved even if the simple zero-forced decoding is used at the receiver.

In this letter, we propose a lattice-based full diversity design for rate-one quasi-orthogonal space time block codes (QSTBC) to obtain an improved diversity product for eight transmit antennas where the information bits are mapped into 4-D lattice points instead of the common modulation constellations. Particularly, the diversity product of the proposed code is directly determined by the minimum Euclidean distance of the used lattice and can be improved by using the lattice packing. We show analytically and by using simulation results that the proposed code achieves a larger diversity product than the rate-one QSTBCs reported previously.

Recently, full rate and full diversity two-group (2Gp) and four-group (4Gp) decodable space-time block codes (STBC) derived from quasi-orthogonal STBC (QSTBC) and designed under diversity product maximization criterion have been proposed. In this paper, we derive an upper bound of diversity product for those STBCs and discover that the diversity product of the current 2Gp-QSTBC and 4Gp-QSTBC has the potential to approach the upper bound for 8 transmit antennas. To this end, we propose an improved design of 2Gp and 4Gp STBC with increased diversity product for 8 transmit antennas by allowing sufficient number of dimensions for constellation rotation. The diversity product of the proposed two-group decodable STBC achieves the derived upper bound.

Recently, a single-symbol decodable transmit strategy based on preprocessing at the transmitter has been introduced to decouple the quasi-orthogonal space-time block codes (QOSTBC) with reduced complexity at the receiver . Unfortunately, it does not achieve full diversity, thus suffering from significant performance loss. To tackle this problem, we propose a full diversity scheme with four transmit antennas in this letter. The proposed code is based on a class of restricted full-rank single-symbol decodable design (RFSDD) and has many similar characteristics as the coordinate interleaved orthogonal designs (CIODs), but with a lower peak-to-average ratio (PAR).