Saman Atapattu

This letter studies interference-aware flexible duplexing (FlexD) in multi-pair wireless networks under distributed (D-FlexD) and centralized (C-FlexD) coordination. Unlike prior FlexD studies, we explicitly incorporate contention and coordination overhead within each coherence block and analyze the resulting throughput outage and average throughput under full and partial channel state information (CSI). For D-FlexD, closed-form outage and rate expressions are derived using a geometric contention model, while for C-FlexD we obtain order-statistic outage expressions under centralized pair selection. High-SINR analysis reveals a geometry-limited outage floor. Numerical results validate the analysis and identify regimes where half-duplex (HD), FlexD, and full-duplex (FD) are preferred.

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.

Urban environments pose a fundamental challenge to millimeter-wave (mmWave) satellite communications (SatCom) by introducing spatially correlated line-of-sight (LoS) blockage, also known as shadowing, which degrades reliability, even with the assistance of reconfigurable intelligent surfaces (RIS). We develop a tractable analytical framework to evaluate the success probability of SatCom systems aided by multi-RIS deployments while considering the crucial factor of correlated LoS conditions. First, we derive an analytical closed-form expression for the exact per-realization success probability. To address the intractability of averaging over minimum-distance constrained (repulsive) RIS deployments, we propose a geometry-averaged formulation by approximating the Matérn hard-core (MHC) process with an inhomogeneous PPP (IPPP) of a known conditional intensity. We then propose a scalable annular-averaging method that replaces the radial integral with ringwise averages, enabling efficient large-scale analysis. Monte Carlo simulations confirm the model’s validity and reveal the impact of blockage intensity, correlation decay, and RIS–user separation, thus providing practical guidelines for RIS deployment to assist future non-terrestrial networks.

Backscatter communication is a promising technology to enhance the signal strength received by the receiver in straight tunnel environments. The impact of the number of tags and their phase adjustment on system performance remains a challenging issue though. Therefore, in this paper, we investigate the channel gain of backscatter-assisted communication with multiple tags in straight tunnels. In particular, we derive the probabilities that the backscatter link gain is greater than the direct link under adjustable and random phase assumptions by applying the Gaussian and Gamma approximations to derive tractable expressions. The simulation results show that phase-adjustable tags significantly improve the channel gain of the backscatter links compared to the random phase case. Moreover, the number of tags has an upper threshold for an effective tag deployment pattern. These insights provide valuable guidelines for the efficient design of backscatter communication systems in tunnel environments.

This letter investigates flexible duplexing (FlexD) for interference-aware multi-user networks employing uniform planar array (UPA)-based directional beam steering. Unlike half-duplex (HD), full-duplex (FD) systems, FlexD adaptively selects the transmission direction based on link quality to enhance interference resilience. We extend FlexD to phased-arrays with location-based beam steering and joint user selection, deriving analytical expressions for throughput outage under fading. A practical direction selection algorithm with partial channel state information (CSI) is developed to reduce overhead. Simulations validate analysis and demonstrate FlexD’s performance over HD and FD under throughput, energy efficiency, and asymmetric traffic, highlighting its potential for next-generation wireless networks.

Rydberg-atom quantum receivers (RAQRs) enable ultra-sensitive RF detection by transducing incident fields into the optical domain with minimal quantum noise. This letter addresses the unexplored problem of detecting unknown RF signal presence under noise by formulating it as a binary hypothesis test for MIMO RAQRs with nonlinear magnitude-only measurements. We derive likelihood ratio tests (LRTs) under varying channel and signal knowledge, and propose a non-coherent CFAR energy detector for fully unknown scenarios. We analyze the role of reference signals, showing that excessive reference power can impair detection. Simulations demonstrate superior performance of the proposed RAQR over RF receiver.

This article investigates optimal distributed medium access control (MAC) strategies for wireless Internet of Things (IoT) networks, incorporating both terrestrial and nonterrestrial relays, including UAVs. While prior research has primarily focused on single-relay forwarding, we address the complexities of energy-efficient multirelay data forwarding. This involves managing the challenges of relay probing, optimized relay utilization, and balancing energy tradeoffs 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 multirelay 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 nonterrestrial movements and varying relay interdistances, 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 nonterrestrial relay configurations. Notably, it also achieves comparatively better performance in latency, throughput, and overall energy consumption.

Enhanced by onboard computing resources and inter-satellite links (ISLs), large-scale satellite networks (SNs) can supplement terrestrial networks, facilitating real-time edge computing and transmission in remote areas at low latency. This capability benefits applications such as real-time remote object tracking, vehicular edge computing, and environmental monitoring. However, different from terrestrial vehicular networks, satellite mobility and the intermittent nature of ISLs often render many computing satellites inaccessible, necessitating efficient scheduling scheme for joint computing and communication resource allocation. To meet this requirement, we investigate joint computing and transmission strategy for real-time computing applications over SNs to reduce end-to-end delays. We formulate the real-time computing and transmission problem over time-varying SNs as an integer linear programming problem. The mathematical optimization method has an exponential time complexity of ${O}(2|\mathcal {L}^{\tau }| \cdot (4 |\mathcal {V}^{\tau }| + 4 |\mathcal {L}^{\tau }|)^{2|\mathcal {L}^{\tau }|})$ in the worst case, which grows rapidly with the growth of network sizes. Here, $|\mathcal {V}^{\tau }|$ and $|\mathcal {L}^{\tau }|$ denote the number of nodes and links, respectively, within the time window ${\tau }$. Resorting to the designed $k$-shortest path-based method yields a worst-case complexity of $O(|\mathcal {V}^{\tau }|! \cdot |\mathcal {V}^{\tau }|^{3})$. While efficient under common scenarios, its running time is still relatively unstable and may become tens of times longer when computing resources become scarce. To enhance efficiency, we further design a graph-based algorithm that leverages the solution space's unique structure, thereby achieving optimal solutions with polynomial time complexity ${O}(3|\mathcal {L}^{\tau }| + (2\log {|\mathcal {V}^{\tau }|}+1) |\mathcal {V}^{\tau }|)$. Extensive evaluations conducted on the world's largest Starlink system validate the efficacy of the designed methods and highlight their potential synergy.

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.