This paper investigates uplink reconfigurable intelligent surface (RIS)-assisted massive random access (RA) in multiuser cellular networks with heterogeneous user distances. The goal is to enhance media access control (MAC)-layer throughput under practical signaling and channel constraints. Conventional RIS configurations using full-array probing incur excessive overhead, especially with large numbers of reflective elements (REs). To address this, we propose an overhead-aware, multi-phase uplink RA framework that integrates adaptive RIS subarray grouping with scalable multiuser scheduling for dynamic user activity. The access optimization is formulated as a sequential decision process, from which an optimal threshold-based policy with closed-form structure is derived. Building on this, an opportunistic RA algorithm is developed to jointly optimize user scheduling, subarray configuration, and access timing with low online complexity that scales with RIS resolution, subchannel count, and user density. Numerical simulations, supported by analytical results, demonstrate significant throughput gains over existing schemes—including full-array, fixed-grouping, and both orthogonal and non-orthogonal multiple access methods—across varying coherence times, RIS sizes, and user distributions. The robustness of the proposed strategy is further verified under imperfect CSI conditions, confirming its suitability for large-scale, dynamic cellular deployments. The proposed framework is further shown to remain effective under practical impairments such as spatial correlation and finite-resolution RIS phase control.

Enhanced by inter-satellite links and satellite direct-to-device capabilities, satellite networks can offer low-latency communication globally. However, limited spectrum resources and the capacity bounds of the Shannon's information theory pose fundamental challenges for supporting bandwidth-intensive multimedia services. Semantic communication (SemCom) offers a promising solution by transmitting compressed semantic representations instead of raw data, thereby alleviating bandwidth pressure. However, it also introduces SemCom-related constraints that render conventional schemes such as contact graph routing inapplicable. To overcome this challenge, we investigate SemCom-compliant path selection and formulate it as a non-NP hard mixed-integer linear programming problem. To address the problem, we develop a graph-based scheme that exploits the special structure of the solution space, the sparsity of SemCom-capable satellites, and the property of Dijkstra's algorithm, thus achieving optimal solutions with polynomial-time complexity. Simulation results on the Starlink constellation confirm that the proposed scheme facilitates SemCom with negligible computational overhead and significant bandwidth reduction. While the bandwidth reduction comes at the cost of increased delay and path hops, these effects are shown to be mitigatable through higher SemCom deployment in a satellite network or by enabling semantic processing at the user side.

Caching is crucial in hybrid satellite-terrestrial networks to reduce latency, optimize throughput, and improve data availability by storing frequently accessed content closer to users, especially in bandwidth-limited satellite systems, requiring strategic Medium Access Control (MAC) layer. This paper addresses throughput optimization in satellite-terrestrial integrated networks through opportunistic cooperative caching. We propose a joint probing and scheduling strategy to enhance content retrieval efficiency. The strategy leverages the LEO satellite to probe satellite-to-ground links and cache states of multiple cooperative terrestrial stations, enabling dynamic user scheduling for content delivery. Using an optimal stopping theoretic approach with two levels of incomplete information, we make real-time decisions on satellite-terrestrial hybrid links and caching probing. Our threshold-based strategy optimizes probing and scheduling, significantly improving average system throughput by exploiting cooperative caching, satellite-terrestrial link transmission, and time diversity from dynamic user requests. Simulation results validate the effectiveness and practicality of the proposed strategies.

In Reconfigurable Intelligent Surfaces (RIS), reflective elements (REs) are typically configured as a single array, but as RE numbers increase, this approach incurs high overhead for optimal configuration. Subarray grouping provides an effective tradeoff between performance and overhead. This paper studies RIS-aided massive random access (RA) at the Medium Access Control (MAC) layer in cellular networks to enhance throughput. We introduce an opportunistic scheduling scheme that integrates multi-round access requests, subarray grouping for efficient RIS link acquisition, and multi-user data transmission. To optimize access request timing, RIS estimation overhead and throughput, we propose a multi-user RA strategy using sequential decision optimization to maximize average system throughput. A lowcomplexity algorithm is also developed for practical implementation. Both theoretical analysis and numerical simulations demonstrate that the proposed strategy significantly outperforms the extremes of full-array grouping and element-wise grouping.

This paper investigates optimal distributed Medium Access Control (MAC) strategies for wireless Internet of Things (IoT) networks, incorporating both terrestrial and non-terrestrial relays, including UAVs. While prior research has primarily focused on single-relay forwarding, we address the complexities of energy-efficient multi-relay data forwarding. This involves managing the challenges of relay probing, optimized relay utilization, and balancing energy trade-offs to maximize system energy efficiency (EE). To address these challenges, we propose a novel strategy called Distributed Data Collection with Opportunistic Relaying (DDC/OR), designed to optimize MAC performance in multi-relay IoT networks. Our approach leverages a decision-theoretic framework based on optimal sequential planning, extending traditional cooperative MAC models to support multiple relays. The proposed DDC/OR strategy offers a statistically optimized solution that maximizes average EE, with a rigorous proof of optimality. Additionally, we present a low-complexity implementation of the DDC/OR algorithm, suitable for practical deployments. For scenarios involving dynamic non-terrestrial movements and varying relay inter-distances, we introduce a two-timescale, self-organized algorithm to adaptively reconfigure relay strategies. To ensure scalability for large relay networks, several optimizations are introduced to enhance the feasibility of the DDC/OR approach. We validate the effectiveness of the DDC/OR strategy through extensive simulations, demonstrating significant EE gains in both terrestrial and non-terrestrial relay configurations. Notably, it also achieves comparatively better performance in latency, throughput, and overall energy consumption.

Mega-satellite constellations have the potential to leverage inter-satellite links to deliver low-latency end-to-end communication services globally, thereby extending connectivity to underserved regions. However, harsh space environments make satellites vulnerable to failures, leading to node removals that disrupt inter-satellite networking. With the high risk of satellite node failures, understanding their impact on end-to-end services is essential. This study investigates the importance of individual nodes on inter-satellite networking and the resilience of mega satellite constellations against node failures. We represent the mega-satellite constellation as discrete temporal graphs and model node failure events accordingly. To quantify node importance for targeted services over time, we propose a service-aware temporal betweenness metric. Leveraging this metric, we develop an analytical framework to identify critical nodes and assess the impact of node failures. The framework takes node failure events as input and efficiently evaluates their impacts across current and subsequent time windows. Simulations on the Starlink constellation setting reveal that satellite networks inherently exhibit resilience to node failures, as their dynamic topology partially restore connectivity and mitigate the long-term impact. Furthermore, we find that the integration of rerouting mechanisms is crucial for unleashing the full resilience potential to ensure rapid recovery of inter-satellite networking.

This research focuses on the challenges of distributed Medium Access Control (MAC) protocols involving Reconfigurable Intelligent Surfaces (RISs), which are still in early development. The study explores optimal channel access for multiple source-destination pairs in distributed networks with the assistance of multiple RISs. Three key issues are addressed: joint scheme for channel contention, RISs’ channel state information (CSI) acquisition, and RIS-assisted channel access; tradeoff between overhead and effective data transmission; and low-complexity distributed network operation. To achieve maximum network throughput, the paper proposes an optimal distributed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategy with opportunistic RIS assistance based on statistical optimization. The proposed MAC strategy's optimality in terms of average network throughput is rigorously derived. Closed-form expressions for threshold functions of rewards for making decisions are derived, and an easy-to-implement distributed channel access algorithm is provided with online complexity$\mathcal {O}(2^{L})$, where$L$denotes the number of distributed RISs. The proposed MAC strategy is then refined, and a low-complexity distributed algorithm is developed with complexity$\mathcal {O}(L)$. Numerical simulations verify the theoretical results, demonstrating the efficiency of the proposed strategy. This work introduces innovative solutions and analytical frameworks for the distributed MAC problem with RIS assistance, significantly advancing existing research.

This paper addresses the challenges of throughput optimization in wireless cache-aided cooperative networks. We propose an opportunistic cooperative probing and scheduling strategy for efficient content delivery. The strategy involves the base station probing the relaying channels and cache states of multiple cooperative nodes, thereby enabling opportunistic user scheduling for content delivery. Leveraging the theory of Sequentially Planned Decision (SPD) optimization, we dynamically formulate decisions on cooperative probing and stopping time. Our proposed Reward Expected Thresholds (RET)-based strategy optimizes opportunistic probing and scheduling. This approach significantly enhances system throughput by exploiting gains from local caching, cooperative transmission and time diversity. Simulations confirm the effectiveness and practicality of the proposed Media Access Control (MAC) strategy.

This paper presents an optimization approach for cooperative Medium Access Control (MAC) techniques in Vehic-ular Ad Hoc Networks (VANETs) equipped with Roadside Unit (RSU) to enhance network throughput. Our method employs a distributed cooperative MAC scheme based on Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol, featuring selective RSU probing and adaptive transmission. It utilizes a dual timescale channel access framework, with a “large-scale” phase accounting for gradual changes in vehicle locations and a “small-scale” phase adapting to rapid channel fluctuations. We propose the RSU Probing and Cooperative Access (RPCA) strategy, a two-stage approach based on dynamic inter-vehicle distances from the RSU. Using optimal sequential planned decision theory, we rigorously prove its optimality in maximizing average system throughput per large-scale phase. For practical implementation in VANETs, we develop a distributed MAC algorithm with periodic location updates. It adjusts thresholds based on inter-vehicle and vehicle-RSU distances during the large-scale phase and accesses channels following the RPCA strategy with updated thresholds during the small-scale phase. Simulation results confirm the effectiveness and efficiency of our algorithm.

This paper focuses on achieving optimal multi-user channel access in distributed networks using a reconfigurable intelligent surface (RIS). The network includes wireless channels with direct links between users and RIS links connecting users to the RIS. To maximize average system throughput, an optimal channel access strategy is proposed, considering the trade-off between exploiting spatial diversity gain with RIS assistance and the overhead of channel probing. The paper proposes an optimal distributed Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) strategy with opportunistic RIS assistance, based on statistics theory of optimal sequential observation planned decision. Each source-destination pair makes decisions regarding the use of direct links and/or probing source-RIS-destination links. Channel access occurs in a distributed manner after successful channel contention. The optimality of the strategy is rigorously derived using multiple-level pure thresholds. A distributed algorithm, which achieves significantly lower online complexity at$O(1)$, is developed to implement the proposed strategy. Numerical simulations verify the theoretical results and demonstrate the superior performance compared to existing approaches.

Emerging smart grid applications analyze large amounts of data collected from millions of meters and systems to facilitate distributed monitoring and real-time control tasks. However, current parallel data processing systems are designed for common applications, unaware of the massive volume of the collected data, causing long data transfer delay during the computation and slow response time of smart grid systems. A promising direction to reduce delay is to jointly schedule computation tasks and data transfers. We identify that the smart grid data analytic jobs require the intermediate data among different computation stages to be transmitted orderly to avoid network congestion. This new feature prevents current scheduling algorithms from being efficient. In this work, an integrated computing and communication task scheduling scheme is proposed. The mathematical formulation of smart grid data analytic jobs scheduling problem is given, which is unsolvable by existing optimization methods due to the strongly coupled constraints. Several techniques are combined to linearize it for adapting the Branch and Cut method. Based on the topological information in the job graph, the Topology Aware Branch and Cut method is further proposed to speed up searching for optimal solutions. Numerical results demonstrate the effectiveness of the proposed method.

In this work, we investigate resource allocation strategy for real time communication (RTC) over satellite networks with virtual network functions. Enhanced by inter-satellite links (ISLs), in-orbit computing and network virtualization technologies, large-scale satellite networks promise global coverage at low-latency and high-bandwidth for RTC applications with diversified functions. However, realizing RTC with specific function requirements using intermittent ISLs, requires efficient routing methods with fast response times. We identify that such a routing problem over time-varying graph can be formulated as an integer linear programming problem. The branch and bound method incurs$\mathcal{O}(\vert \mathcal{L}^{\tau}\vert \cdot(3\vert \mathcal{V}^{\tau}\vert+\vert \mathcal{L}^{\tau}\vert )^{\vert \mathcal{L}^{\tau}\vert })$time complexity, where$\vert \mathcal{V}^{\tau}\vert$is the number of nodes, and$\vert \mathcal{L}^{\tau}\vert$is the number of links during time interval$\tau$. By adopting a k-shortest path-based algorithm, the theoretical worst case complexity becomes$O(\vert \mathcal{V}^{\tau}\vert !\vert \mathcal{V}^{\tau}\vert ^{3})$. Although it runs fast in most cases, its solution can be sub-optimal and may not be found, resulting in compromised acceptance ratio in practice. To overcome this, we further design a graph-based algorithm by exploiting the special structure of the solution space, which can obtain the optimal solution in polynomial time with a computational complexity of$\mathrm{O}(3\vert \mathcal{L}^{\tau}\vert +(2\log\vert \mathcal{V}^{\tau}\vert +1)\vert \mathcal{V}^{T}\vert )$. Simulations conducted on starlink constellation with thousands of satellites corroborate the effectiveness of the proposed algorithm.

We investigate the problem of network selection for the Internet of Things (IoT) to maximize the Quality of Experience (QoE) in a heterogeneous wireless environment. Different from the traditional network access approaches with the assumption that the network state information (NSI) is static and known a priori, a scenario where the NSI of networks is unknown and dynamic to IoT devices is considered. Due to hardware limitations in IoT, the device has a limited resource budget and consumes resources, e.g., the energy, during the process of network access. To maximize the cumulative QoE before resource exhausts, the device should learn and estimate the NSI of networks, and make appropriate decisions to balance the network selection and resource consumption. To address this issue, we formulate the problem as a combination of multiarmed bandit and optimization problems and propose two algorithms NCSA and network selection algorithm (NSA). Moreover, we consider the fact that the device has various traffic types and propose an algorithm MT-NSA. Theoretical analysis shows that the regret of all algorithms has a sublinear relationship with the resource budget. The effectiveness is validated by simulations.

Optimizing data transfers is critical for improving job performance in data-parallel frameworks. In the hybrid data center with both wired and wireless links, reconfigurable wireless links can provide additional bandwidth to speed up job execution. However, it requires the scheduler and transceivers to make joint decisions under coupled constraints. In this work, we identify that the joint job scheduling and bandwidth augmentation problem is a complex mixed integer nonlinear problem, which is not solvable by existing optimization methods. To address this bottleneck, we transform it into an equivalent problem based on the coupling of its heuristic bounds, the revised data transfer representation and non-linear constraints decoupling and reformulation, such that the optimal solution can be efficiently acquired by the Branch and Bound method. Based on the proposed method, the performance of job scheduling with and without bandwidth augmentation is studied. Experiments show that the performance gain depends on multiple factors, especially the data size. Compared with existing solutions, our method can averagely reduce the job completion time by up to 10% under the setting of production scenario.

A novel approximate multiplier with tunable accuracy modes which leverages an inaccurate 4 × 4 multiplier is proposed. The inaccurate multipliers achieve a power reduction of 17.98%–34.15% over corresponding accurate designs with a maximal absolute relative error of 22.2%. A Gaussian filter is implemented for image sharpening as a sample assessment and the results show that the proposed architecture can achieve 5–22 dB increment on Signal Noise Ratio (SNR) with less power and area compared to other designs. In addition, the proposed multiplier is enhanced to correct approximate results by using an extra error detection and correction (EDC) circuit for non-error resilient applications. The proposed design has the least area compared to other existing error correcting multipliers.

In this paper, we consider roadside infrastructure-to-roadside infrastructure communications in a vehicular ad hoc network. A remote roadside unit (RSU), which does not have connection to the backbone network, needs to send its data traffic to a central RSU (which has backbone connection) by using help from vehicles passing by. Cost is assigned to information transmission energy consumption, as well as possible violation of a soft delay bound. For each passing vehicle, the remote RSU needs to decide whether or not to ask for help from the vehicle, with a target at a minimal rate of cost. We derive an optimal decision strategy of the remote RSU, which is shown to have a conditional pure-threshold structure, i.e., when a vehicle arrives at the remote RSU, if the queuing delay of the data traffic at the remote RSU is above a threshold, it is optimal for the remote RSU to ask for help from the vehicle, with a condition that the vehicle's speed satisfies a requirement. We also provide a method that can theoretically derive the threshold. The conditional pure-threshold structure makes our derived strategy very easy to implement with very low computation complexity.

In this paper, the problem of opportunistic channel sensing and access in cognitive radio networks when the sensing is imperfect and a secondary user can access up to a limited number of channels at a time is investigated. Primary users' statistical information is assumed to be unknown, and therefore, a secondary user needs to learn the information online during channel sensing and access process, which means learning loss, also referred to as regret, is inevitable. For each channel, the busy/idle state is independent from one slot to another. In this research, the case when all potential channels can be sensed simultaneously is investigated first. The channel access process is modeled as a multi-armed bandit problem with side observation. And channel access rules are derived and theoretically proved to have asymptotically finite regret. Then the case when the secondary user can sense only a limited number of channels at a time is investigated. The channel sensing and access process is modeled as a bi-level multi-armed bandit problem. It is shown that any adaptive rule has at least logarithmic regret. Then we derive channel sensing and access rules and theoretically prove that they have logarithmic regret asymptotically and with finite time. The case when the busy/idle states of a channel are correlated over slots is also investigated. And a channel sensing and access rule with logarithmic regret is derived. The effectiveness of the derived rules is validated by simulation.

In this paper, the problem of distributed opportunistic channel access in wireless relaying is investigated. A relay network with multiple source-destination pairs and multiple relays is considered. All source nodes contend through a random access procedure. A winner source may give up its transmission opportunity if its link quality is poor. In this research, we apply the optimal stopping theory to analyze when a winner source should give up its transmission opportunity. By assuming the winner source has channel state information (CSI) of links from itself to relays and from relays to its destination, the existence of an optimal stopping strategy is rigorously proved. The optimal stopping strategy has a pure-threshold structure. The case when a winner source does not have CSI of links from relays to its destination is also studied. Two stopping problems exist, one in the main layer (for channel access of sources), and the other in the sub-layer (for channel access of relays). An intuitive stopping strategy, where the main layer (for the first hop) and sub-layer (for the second hop) maximize their throughput respectively, is derived. The intuitive stopping strategy is shown to be non-optimal. An optimal stopping strategy is then derived theoretically. In either the intuitive stopping strategy or the optimal stopping strategy, the main-layer stopping rule has a pure-threshold structure, while the sub-layer stopping rule has a threshold determined by the channel realization in the preceding first-hop transmission. Our research reveals that multi-user (including multi-source and multi-relay) diversity and time diversity can be utilized in a relay network by our proposed strategies. The effectiveness of the strategies is validated by numerical and simulation results.