Wireless caching has attracted substantial attention due to the potential for reducing service delays. However, it will waste energy if the cached content item is not requested by users. In order to improve the energy efficiency of wireless caching, we present a socially motivated wireless caching strategy from the cross-domain perspective in this paper. To this end, we first study the evolutionary requesting model based on users’ preference and their social ties. Then we derive the approximated expression for the delay-outage probability with a given power allocation strategy over Rayleigh block fading channels based on the saddle point technology. Based on the derived expression, a two-layer binary search method is presented to determine a suitable caching volume with a given outage probability and a power constraint. After that, we formulate a cross-layer optimization problem to jointly allocate the storage and transmission resources. By solving this optimization problem, we can determine the caching strategy to minimize the power consumption while promising the hit ratio performance. In addition, we extend the energy-efficient caching system over additive white Gaussian noise channels. Finally, numerical results are presented to demonstrate the potential of the presented caching strategy.

Movable antenna (MA) technology provides a promising avenue for actively shaping wireless channels through dynamic antenna positioning, thereby enabling electromagnetic radiation reconstruction to enhance physical layer security (PLS). However, its practical deployment is hindered by two major challenges: the high computational complexity of real-time optimization and acritical temporal mismatch between slow mechanical movement and rapid channel variations. Although data-driven methods have been introduced to alleviate online optimization burdens, they are still constrained by suboptimal training labels derived from conventional solvers or high sample complexity in reinforcement learning. More importantly, existing learning-based approaches often overlook communication-specific domain knowledge—particularly the asymmetric roles and adversarial interactions between legitimate users and eavesdroppers, which are fundamental to PLS. To address these issues, this paper reformulates the MA positioning problem as a predictive task and introduces RoleAware-MAPP, a novel Transformer-based framework that incorporates domain knowledge through three key components: role-aware embeddings that model user-specific intentions, physics-informed semantic features that encapsulate channel propagation characteristics, and a composite loss function that strategically prioritizes secrecy performance over mere geometric accuracy. Extensive simulations under 3GPP-compliant scenarios show that RoleAware-MAPP achieves an average secrecy rate of 0.3606 bps/Hz and a Secrecy Performance Coverage Probability (SPSC) of 79.26%,outperforming the state-of-the-art predictive baseline by 35.5% and 6.73 percentage points, respectively, while maintaining robust performance across diverse user velocities and noise conditions.

The evolution of industrial intelligent manufacturing necessitates wireless communication systems capable of replacing conventional wired infrastructures, offering superior flexibility, scalability, and reduced maintenance overhead. While 5 G New Radio (NR) Ultra-Reliable Low-Latency Communication (uRLLC) standards (Release 15-17) have shown promise for mission-critical applications, current implementations remain constrained by their unidirectional optimization paradigm, unable to simultaneously satisfy the dual imperatives of sub-millisecond latency ($\lt 1$ms) and 99.9999% reliability demanded by industrial control systems. To address these challenges, we present a transformative subband full-duplex (SBFD) network architecture that ensures persistent time-domain spectral availability for concurrent uplink/downlink operations, thereby eliminating direction-switching latency. Our solution introduces three key innovations: (1) an interference-aware SBFD resource allocation framework that strategically isolates UL/DL subbands to minimize cross-link interference (CLI), (2) a dual-optimization algorithm that jointly maximizes spectral efficiency while guaranteeing channel-adaptive reliability thresholds, and (3) a practical implementation scheme compatible with existing 5G NR physical layer specifications. Extensive simulations under realistic factory channel models demonstrate 58.3% reduction in aggregate CLI and 41.2% improvement in control command decoding accuracy compared to legacy half-duplex systems. This research establishes a new paradigm for wireless industrial networks, effectively closing the performance gap between 5G URLLC specifications and the exacting demands of Industry 4.0 applications.

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.

Sensing performance is typically evaluated by classical metrics, such as Cramer-Rao bound and signal- to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the metric for sensing and communication, where researchers have proposed to utilize mutual information (MI) to measure the sensing performance with deterministic signals. However, the need to communicate in ISAC systems necessitates the use of random signals for sensing applications and the closed-form evaluation for the sensing mutual information (SMI) with random signals is not yet available in the literature. This paper investigates the SMI and precoder design for sensing applications with random signals. For that purpose, we first derive the closed-form expression for the SMI with random signals by utilizing random matrix theory. The result reveals some interesting physical insights regarding the relation between the SMI with deterministic and random signals. The derived SMI is then utilized to optimize the precoder by leveraging a manifold-based optimization approach. The accuracy of the theoretical analysis and the effectiveness of the proposed precoder design method are validated by simulation results.

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.

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.

Heterogenous network is currently under intensive discussion within 3GPP as an effective solution to improve network coverage and capacity for long term evolution (LTE)Advanced system, which consists of a mix of high-power macro base stations and a diverse set of low-power nodes, i.e., pico, femto and relay nodes. In this paper, we will particularly focus on the mixed deployment of macro and pico cells. Co-channel deployment is typically assumed for heterogenous network to make the best use of spectrum resource; therefore, a critical issue for heterogenous network is co-channel interference between macro and pico cells, which, if not properly addressed, may significantly degrade the performance of heterogenous network. Furthermore, other deployment options, e.g., the number and the positions of pi co nodes will also affect system performance greatly. In this paper, we will carry out extensive simulation campaigns to evaluate the downlink and uplink performance of co-channel macro-pico heterogeneous network. Results show that huge performance benefit can be obtained if heterogenous network is well designed and parameterized.