Reconfigurable intelligent surface (RIS)-enabled integrated sensing and communication (ISAC) systems enhance spectrum efficiency and sensing accuracy. Building on this, we propose a novel self-sustainable multi-functional RIS (S-MFRIS) concept that supports multiple functionalities: reflection, refraction, amplification, energy harvesting, and target sensing. By harvesting energy from incident signals, the S-MFRIS can reflect, refract, and amplify signals without needing an external power supply, effectively overcoming double-fading attenuation. Furthermore, by deploying low-cost sensor elements, the S-MFRIS can capture echo signals from multiple targets, mitigating the signal attenuation commonly associated with multi-hop links. Then, we establish an S-MFRIS-enabled ISAC system and formulate an optimization problem to maximize the signal-to-interference-plus-noise ratio (SINR) of the sensing targets, subject to constraints on communication rate, power budget, and reflection coefficients. To solve this non-convex problem, we decompose it into three sub-problems, which are efficiently addressed using an iterative algorithm. Simulation and numerical results demonstrate the following key findings: (1) The proposed algorithm achieves better convergence and performance than the semidefinite relaxation-based and random-based algorithms. (2) The performance of the MFRIS-aided system varies under different operating protocols, with the self-sustainable MFRIS outperforming other schemes, particularly when the power budget is sufficient. (3) The proposed S-MFRIS achieves$30\%-46\%$sensing SINR gains at most for the same total power budget or element configuration. (4) The number of sensing elements improves sensing performance up to a certain point, after which further increases in the number of elements yield diminishing returns.

An extremely large-scale intelligent reflecting surface (XL-IRS) aided communication system is studied. Although XL-IRS can effectively combat the double path-loss attenuation, the large aperture size introduces significant near-field effects. The complex near-field propagation and large number of XL-IRS elements can lead to optimality and complexity challenges in beam focusing design. Focusing on a spectral efficiency (SE) maximization problem, two unsupervised learning based algorithms are conceived for the joint optimization of base station and XL-IRS beam focusing, which operate without pre-training and exhibit strong robustness. Specifically, a dense-connected dilated autoencoder meta learning (DDAML) algorithm is proposed to achieve high SE by utilizing dense connections, while reasonably designing an autoencoder to reduce computational complexity. Furthermore, considering a need for low execution time in practical applications, a convolutional dilated autoencoder meta learning (CDAML) algorithm is also proposed to further reduce computational complexity. Simulation results show that the proposed DDAML algorithm achieves the highest SE, while the proposed CDAML algorithm significantly reduces computational complexity at the cost of limited SE loss. Moreover, the two proposed algorithms also demonstrate remarkable robustness in XL-IRS-aided near-field communications.

Intelligent reflecting surface (IRS)-assisted wireless communication has demonstrated notable performance enhancements despite challenges such as double-fading attenuation and dependency on external power sources. This paper introduces a self-sustainable IRS (S-IRS) architecture that integrates energy harvesting with information transmission, unlocking its potential in simultaneous wireless information and power transfer (SWIPT) systems. A time-splitting-based S-IRS-aided SWIPT protocol is proposed to analyze the system’s energy transfer and communication efficiency. To address the trade-off between these interdependent metrics, a sum-rate optimization problem is formulated under the energy constraints of the S-IRS and wireless devices. A hierarchical optimization framework, incorporating advanced learning and optimization techniques, is developed to solve this problem. The simulation results validate the effectiveness of the proposed approach, highlighting its advantages and the impact of critical design parameters on system performance.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing and communications (ISAC) framework holds promise for wide-range coverage, sensing, and communication. However, allocating multiple resources becomes challenging due to the coupled time, frequency, and space resources. Additionally, echo interference from communication users to the sensing target degrades sensing performance. To tackle these challenges, a performance optimization problem in a STAR-RIS-aided ISAC system is formulated, and an alternating optimization scheme is investigated to balance the defined sensing mutual information and communication performance by optimizing the active transmit beams, STAR-RIS reflecting and transmit beams, and multicarrier distribution variables under the energy splitting protocol. Simulation results demonstrate the convergence and effectiveness of the proposed scheme, highlighting its advantages in improving sensing performance compared to benchmark schemes.

Vehicle-to-Everything (V2X) communication and computation encounter challenges in achieving ultrareliable, low-latency communication, and optimizing energy consumption in dynamic vehicular environments. To overcome these issues, intelligent reflecting surfaces (IRSs) are introduced to boost communication efficiency and reliability while lowering latency and energy use. This article presents an IRS-enhanced V2X system, employing multiple IRSs to improve vehicle communication and computation offloading through spectrum reuse principles. An effective utility function is developed to quantify total latency and energy consumption, facilitating precise system evaluation and optimization. The complex optimization problem is divided into four subproblems: 1) vehicle operation mode selection; 2) spectrum reuse allocation; 3) computation offloading decision; and 4) beamforming design. A swap-matching-based tabu-search method solves the mode selection subproblem, while semi-definite relaxation and penalty functions address the other issues integrated through an alternating optimization algorithm. The optimized system achieves efficient and reliable communication with reduced latency and energy consumption. Simulations reveal that effective utility function minimization significantly enhances system efficiency compared to benchmarks. Strategic IRS deployment reduces channel losses, resulting in substantial performance improvements and supporting intelligent, sustainable transportation network advancement.

The maritime joint sensing and communication system (MSCS) has recently emerged as a promising solution to address the maritime spectrum scarcity issue for high-efficiency communication and environmental sensing. To mitigate the significant path loss experienced over the complex sea surface, the intelligent reflecting surface (IRS) is integrated into the MSCS to enhance the signal quality by dynamically adjusting the phases of its reflecting elements. Building upon this foundation, we aim to improve the sensing performance by maximizing the sensing signal-to-noise ratio while maintaining normal maritime communication, which involves optimizing active and passive beamforming vectors. Under the unpredictable environmental information and high-complexity and high-overhead channel information estimation in MSCS, we propose a heuristic algorithm based on genetic evolution to tackle this problem. To ensure the algorithm convergence and the feasibility of available solutions, we introduce an individual processing approach and an elitist reservation strategy in each genetic generation. Numerical results and simulations validate the convergence and efficacy of the proposed algorithm. Additionally, we analyze the effects of critical parameters and IRS structure on the algorithm performance.

Screen Content Coding (SCC) is an indispensable tool for enabling distributed collaboration, such as video conferencing. Encoders in the latest video coding standards, particularly for SCC scenarios, employ a wider variety of partitioning tree splitting types, recursively traversing all branches, as well as a larger number of coding modes and submodes, to achieve higher coding efficiency compared to encoders in previous standards. This process leads to very high coding complexity, as each tree leaf node, called a coding unit (CU), for every partitioning size and location in the picture is repeatedly visited and evaluated multiple times during the optimal partitioning search. Additionally, each CU visit involves evaluating a vast number of coding options and their combinations to identify the best one. The complexity is further exacerbated in SCC due to the addition of many new CU coding modes and options. To significantly reduce SCC complexity without coding efficiency loss, this article proposes a new technique, Accelerated Revisit CU-coding (ARC), along with an SCC search space analysis for in-depth operation-level and run/platform-independent assessment of SCC complexity. ARC exploits the correlation between the first visit and subsequent revisits of a CU with the same location and size. By fully leveraging the correlation and information from the first visit, ARC significantly accelerates revisit CU-coding while maintaining the same high coding efficiency. ARC is implemented in HPM, the AVS3 reference software. Experiments demonstrate that ARC reduces encoding runtime by 29.74%, 47.78%, and 54.25% for 1,920 × 1,080 FHD, 4K UHD, and 8K UHD test sequences, respectively, in All Intra configuration, without coding efficiency loss. These runtime reductions align with corresponding search space reductions of 30.91%, 49.67%, and 54.41%, as obtained from the search space analysis.

The evolution of computing paradigms has been significantly influenced by the emergence of wireless-powered mobile edge computing (WP-MEC), fundamentally transforming resource-efficient processing at the network edges. Intelligent reflecting surfaces (IRSs) integrated with WP-MEC offer new opportunities by enhancing the channel quality while addressing the complex resource management challenges. To address this, a collaborative transmission and resource management scheme for communication, energy, and computation is provided in this article. In particular, a novel performance evaluation index, named energy cost utility is presented first to capture the IRS-aided coupling performances thoroughly. Subsequently, a collaborative transmission mechanism addressing communication, energy transmission, and edge computing issues is developed, leveraging adaptive IRS association. Furthermore, to enhance the system performance, a hierarchical optimization framework for the resource management with limited computation capability is proposed, which includes the upper-layer optimization-based IRS association and resource allocation, along with the lower-layer deep reinforcement learning-based active and passive beamforming, aimed at maximizing the energy cost utility. Compared to the other benchmark schemes, our proposed collaborative transmission and resource management approach demonstrates the ability to learn from the environment and improve behavior gradually and exhibits superiority in enhancing transmission quality and reducing energy consumption. Also, appropriate neural network parameters will significantly improve the performance and convergence rate of the proposed algorithm. Finally, the advantages of the IRS association regarding quantity and configuration for enhancing the energy cost utility are explored, highlighting its potential to shape the future of the Internet of Things.

Non-orthogonal multiple access (NOMA) based fog radio access networks (F-RANs) offer high spectrum efficiency, ultra-low delay, and huge network throughput, and this is made possible by edge computing and communication functions of the fog access points (F-APs). Meanwhile, caching-enabled F-APs are responsible for edge caching and delivery of a large volume of multimedia files during the caching phase, which facilitates further reduction in the transmission energy and burden. The need of the prevailing situation in industry is that in NOMA-based F-RANs, energy-efficient resource allocation, which consists of cache placement (CP) and radio resource allocation (RRA), is crucial for network performance enhancement. To this end, in this paper, we first characterize an NOMA-based F-RAN in which F-APs of caching capabilities underlaid with the radio remote heads serve user equipments via the NOMA protocol. Then, we formulate a resource allocation problem for maximizing the defined performance indicator, namely network profit, which takes caching cost, revenue, and energy efficiency into consideration. The NP-hard problem is decomposed into two sub-problems, namely the CP sub-problem and RRA sub-problem. Finally, we propose an iterative method and a Stackelberg game based method to solve them, and numerical results show that the proposed solution can significantly improve network profit compared to some existing schemes in NOMA-based F-RANs.

In this paper, we examine the potential benefits of non-orthogonal multiple access (NOMA) in achieving energy-efficient mobile edge computing (MEC) in wireless networks. To this end, we consider an uplink communication system where the edge users (EUEs) adopt NOMA protocol to offload their own tasks to the edge access points in the presence of cellular users (CUEs) performing regular uplink transmissions. We first characterize the energy consumption of our considered system. Then, taking the delay constraints of the CUEs and EUEs into consideration, we show how the energy consumption of the system can be optimized by judiciously determining the task offloading allocation, the subchannel allocation, as well as the power allocation. In order to solve the non-convex problem, an iterative Stackelberg-game-based scheme is proposed, in which the EUEs perform the task and power allocation as leaders, while the CUEs perform the subchannel allocation as followers. Numerical results show that, compared to exiting solutions, our proposed NOMA-based scheme can significantly reduce the energy consumption of the system, and the performance improvement becomes more profound when the delay constraints of the CUEs and EUEs become stringent.

Resource allocation in fog radio access networks (F- RANs) is a hot topic but with challenges, especially combined with non-orthogonal multiple access (NOMA) technique which is promised to enhance spectral efficiency (SE). In NOMA-based F-RANs, radio subchannels occupied by remote radio heads (RRHs) can be reused by fog access points (F-APs) and power domain NOMA technique is applied for the link from F-APs to fog user equipments (FUEs). In this paper, an energy efficiency (EE) maximization problem consisting of subchannel reuse assignment and NOMA-based power allocation is formulated for NOMA-based F-RANs. To solve it efficiently, the problem is modeled as a Stackelberg game and two game-theoretic algorithms are developed for two sub-problems, respectively. In particular, The subchannel reuse assignment is modeled as a one-to-many matching game and the power allocation is modeled as a non-cooperative game. The low complexity of proposed algorithms make the problem tractable and fast to converge to the Nash equilibrium (NE). Simulation results show that the proposed algorithms achieve the significant performance gains on system EE, users' latency with an accurate and low complexity way compared to other baselines.

The growing popularity of Internet of Things (IoT) with the requirements of highly reliable and low latency has imposed huge challenges to current cellular networks. Using small aerial platforms like unmanned aerial vehicles (UAVs) to assist terrestrial base stations (BSs) is attractive, but it often challenged by the lack of UAV access selection and resource allocation algorithm to balance the network performance and service cost. In this paper, we study the UAV access selection and BS bandwidth allocation problems in a UAV assisted IoT communication network, where a hierarchical game framework is presented. The complicated interactions among UAVs and BSs as well as the cyclic dependency is studied by applying the Stackelberg game theory. Wherein, the access competition among groups of UAVs is formulated as a dynamic evolutionary game and solved by an evolutionary equilibrium. On the other hand, the problem of how much bandwidth should BSs allocate to the UAVs is modeled as a noncooperative game, where the existence and the uniqueness of Nash equilibrium is analyzed. Stochastic geometry tool is used to model the position distribution of network nodes and drive the payoff expressions by taking into account different network parameters. The analytical results for the proposed hierarchical game model and the corresponding solutions are evaluated via simulations, which verify both the validity of our analysis and the effectiveness of the proposed algorithms.