The integrated sensing and communication (ISAC) attracts growing interest in 6G due to the extension of radar capability. This paper devotes to the resource allocation problem in ISAC system design. Both static and dynamic resource allocation schemes are comprehensively investigated, with the primary distinction lying in the allocation of resources to either communication or sensing services, or their shared utilization. We construct two-class multi-server queues of each scheme, respectively, where communication and sensing services are modeled as Poisson processes with distinct arrival and service rates. To derive the steady-state probabilities of the two models, we present proofs of reversibility. Additionally, key matrices are derived for performance measurement purposes. Numerical results demonstrate the superiority of the dynamic allocation scheme while also providing insights into resource investment and division.

High reliability is crucial for stable and accurate control in wireless Internet of Things (IoT) systems. Although average outage probability is a widely adopted metric for evaluating the reliability of wireless IoT systems, it does not account for the sparsity of outage occurrences, which can be interpreted as the frequency of outage occurrence within a certain time. Compared to an isolated single outage, clustered outages can significantly impact stability. To address this problem, we introduce the concepts of virtual queue and consecutively effective throughput. The virtual queue treats outage packets as arrivals, with its service rate determined by the desired frequency of the single outage. In contrast to the traditional throughput, consecutively effective throughput measures the effectiveness of consecutive success of transmissions. Based on these concepts, we then consider maximizing the consecutively effective throughput under virtual queue constraints. Theoretical analysis is conducted to derive the optimal rate for this problem. Specifically, we explore two scenarios: 1) outages caused by packet decoding errors and 2) outages due to packet decoding errors and latency violations. Considering the nonasymptotic property of the virtual queue, queueing theory is utilized to provide closed-form expressions for virtual queue constraints. Based on the theoretical analysis, efficient and effective algorithms are proposed to obtain the optimal rate based on the above analysis. Numerical comparisons between grid searches and our algorithms validate the correctness of our theoretical analysis. This study underscores the importance of specific scheduling designs to control clustered outages, rather than merely enhancing throughput in wireless IoT systems.

A pioneering secure transmission scheme is proposed, which harnesses movable antennas (MAs) to optimize antenna positions for augmenting the physical layer security. Particularly, an MA-enabled secure wireless system is considered, where a multi-antenna transmitter communicates with a single-antenna receiver in the presence of an eavesdropper. The beamformer and antenna positions at the transmitter are jointly optimized under two criteria: power consumption minimization and secrecy rate maximization. For each scenario, a novel suboptimal algorithm was proposed to tackle the resulting nonconvex optimization problem, capitalizing on the approaches of alternating optimization and gradient descent. Numerical results demonstrate that the proposed MA systems significantly improve physical layer security compared to various benchmark schemes relying on conventional fixed-position antennas (FPAs).

For millimeter wave, accurate beamforming can provide massive multiple-input multiple-output (MIMO) systems throughput gain. However, the time complexity of searching for the best transmitting and receiving beam pairs increases with the numbers of transmitting and receiving beams. In this work, we propose a deep learning (DL)-based spatial domain beam prediction method. Different from existing DL-based beam prediction network, we consider multiple kinds of configurations of beam pair subset for the input of the network, which can make one network support the prediction tasks of different antenna configurations with smaller input size. By considering the side information, we propose a multimodal structure to improve the prediction accuracy of DL-based beam prediction network. Compared to existing beam prediction methods, the proposed DL-based beam prediction network has better generalization and can improve training efficiency of the neural network with an outperformed beam prediction result.

This paper introduces a novel satellite handover scheme considering the link outage probability (SHLOP). It aims to improve the resource utilization of satellite handover processes in satellite- UAV integrated networks and address the limitations that the state-of-the-art failed to consider the impact of intermittent satellite-ground and inter-satellite links in UAV collaborative penetration scenarios. SHLOP dynamically adjusts the ratio of allocated channel resources for handover according to the link outage probability and the number of UAVs, which helps to reach a compromise between handover success probability and resource efficiency excluding wasted resources. Besides, a performance evaluation model is developed to analyze the relationship between resource utilization, the ratio of reserved resources, the number of users, and the link outage probability. The formulated model identifies a closed-form solution for the optimal reserved resource ratio, which optimizes the SHLOP performance. Numerical evaluation results show that SHLOP outperforms the PDRA and DCR-DQN by at least 11% in terms of resource utilization with intermittent links.

Integrated sensing and communication (ISAC) has been treated as a key technology for providing high-quality performance on both communication and sensing with higher spectrum efficiency and lower hardware cost. Among the research on ISAC, joint Radar and communication (JRC) is one of the typical scenarios. In this paper, we focus on designing the optimal JRC scheduling policy and characterizing the optimal tradeoff between the performance of communication and detection in a time-sharing JRC system. To this end, an optimization problem of minimizing the average delay with the constraints on the average power and detection probability is proposed. With the help of the constrained Markov Decision Process (CMDP) and linear programming (LP), the optimal cross-layer JRC scheduling policy is presented, which also reveals the optimal tradeoff between the performance of the communication and sensing in this JRC system. Moreover, we demonstrate the condition for detection scheduling not occupying the time resources ought to be allocated to send data in the communication-centric system. When this condition holds, we call this JRC system achieves ‘sensing for free’. For the JRC system with this property, the complexity of scheduling design can be reduced while the time resources can be utilized more efficiently.

Ultra Massive MIMO is one of the potential key technologies in 6G, which attracts widespread attention from academia and industry recently. Generally, the system-level simulation is essential to evaluate the performance of Ultra Massive MIMO. However, due to ultra-large-scale antennas and extremely high concurrency, the computation complexity is pretty high, which makes the simulation workload unbearable. To deal with this issue, we propose a two-level parallel optimization via OpenMP and Eigen to drastically promote the simulation efficiency in this paper. Moreover, we provide a comprehensive insight on 6G system-level evaluation platform with modular architecture and parallel computing. It is validated that our simulation platform has the capability of evaluating 6G Ultra Massive MIMO technology and the simulation efficiency can be improved by more than 20 times than that of without parallelization. Based on the simulation results, it is shown that the UE experienced data rate can be increased up to 10 times than that of 5G, which can meet the requirement of 6G.

Wireless communication enabled integrated sensing and communication (ISAC) attracts growing interest recently. This paper investigates the sensing ability of OFDM and OTFS signals. We derive the expression of their ambiguity functions, and show that the ambiguity surface can be shaped by the information carried. To this end, we then develop a Zak-transform based ambiguity function synthesis method and deduce the corresponding information sequence in the l2-norm case. Finally, we present the ambiguity surface of it, and compare it with the Zaddoff-Chu and gold sequences that are widely used in LTE and NR systems. Simulations validate the dependency of sensing performance on the designed sequence and the size of the time-frequency resource.

Integrated sensing and communication (ISAC) has been treated as a key technology for providing high-quality performance on both communication and sensing with higher spectrum efficiency and lower hardware cost. Among the research on ISAC, joint radar and communication (JRC) is one of the typical scenarios. Due to remaining challenges on the design for full-duplex hardware and interference depression, time-division JRC is believed as the first step toward ISAC by switching communication and sensing functions in the time domain. In this article, we focus on designing the optimal cross-layer JRC scheduling policy and characterizing the optimal tradeoff between the performance of communication and sensing in a time-division JRC system with a buffer. For both active and passive radar detection scenarios. To this end, optimization problems of minimizing the average delay with different constraints on the average power and detection performance are formulated. With the help of constrained Markov decision process (CMDP) and linear programming (LP), optimal JRC scheduling policies are presented, which also reveal the optimal tradeoff between the performance of the communication and sensing. Moreover, we summarize the condition for sensing scheduling not occupying the time resources which ought to be allocated for communication in a communication-centric system. When this condition holds, we say this JRC system achievessensing for free. Based on the analysis ofsensing for freeand the investigation of numerical results, a heuristic policy is proposed for JRC systems with passive radar detection. Some insights are obtained into the scheduling design of time-division JRC systems.

Space-Air-Ground Integrated Network is the trend for 6G network development. It could integrate many advantages from terrestrial network, satellites and high altitude platform stations, improving the coverage and service quality significantly. Meanwhile, breaking the barriers of heterogeneous networks and realizing the cooperation between them pose new challenges for network operation. This paper firstly analyzes the current situation and expounds technical researches and commercial achievements from the perspectives of standardization and industry. Secondly, we focus on network architecture, network sharing and private network, mainly investigating the operation and mutual enabling key technologies in Space-Air-Ground Integrated Network. Finally, we point out the challenges from “time-frequency-space” dimensions.

The orthogonal time frequency space (OTFS) modulation as a promising signal representation attracts growing attention for integrated sensing and communication. This paper devotes to MIMO-OTFS based channel parameter estimation. Inspired by the insight that superimposed signals can be composed iteratively and processed separately, we develop the Space-Alternating Generalized Expectation-Maximization (SAGE) algorithm to support the resolution of multiple paths with modest complexity. Besides, we derive the Cramer-Rao lower bound (CRLB) of the variance of channel parameters as the fundamental limit of sensing in the OTFS modulation scheme as well as the baseline of the performance evaluation. Simulations in various cases demonstrate the feasibility.

Massive multiple-input multiple-output (MIMO) system, in which base stations are equipped with a large number of antennas in a two-dimensional (2D) antenna array, is one of the most promising technologies to improve the spectral efficiency of cellular networks. Since there is no channel reciprocity, the frequency-division duplexing (FDD) massive MIMO system has to obtain channel state information (CSI) with the help of uplink feedback. However, in practice, there are several challenges for the implementation of FDD massive MIMO system, such as feedback overhead and delay. In this paper, we begin with the virtual sectorization using CSI- Reference Signal (RS) beamforming scheme to illustrate the implementation of FDD massive MIMO in the standard transparent manner, and then we describe the potential performance degradation caused by feedback delay. To cope with this issue, we propose a historical precoding matrix indicator (PMI) based multi-user scheduling algorithm to enhance the system performance. By utilizing system-level simulator, we further present the effectiveness of the proposed algorithms.