This work theoretically proves that TSDM achieves the desired mean and temporal variance for each flow, and conducts extensive simulations on two open joint throughput-AoI optimization problems, finding that TSDM significantly outperforms existing scheduling policies.
Abstract
Optimizing for both low Age of Information (AoI) and high throughput is critical for remote sensing applications that rely on multichannel wireless networks. However, jointly optimizing these two metrics is an analytically challenging problem, particularly in systems with heterogeneous and unreliable channels. To address this challenge, we propose TSDM, a Two-Stage Deficit Matching scheduling framework. TSDM is based on a second-order approach that characterizes the performance of each data flow by its mean and temporal variance. In the first stage, TSDM translates the high-level utility maximization objective into a concrete set of target mean and temporal variance statistics for transmissions over each node-channel pair. In the second stage, a low-complexity Weighted Matching Deficit (WMD) rule performs real-time channel assignment. We theoretically prove that TSDM achieves the desired mean and temporal variance for each flow. Furthermore, we conduct extensive simulations on two open joint throughput-AoI optimization problems. In both cases, TSDM significantly outperforms existing scheduling policies.
Coordinated beamforming in distributed 5G networks relies on the timely exchange of inter-cell scheduling information, but backhaul latency makes this information stale. Even a single transmission time interval (TTI) of delay can reduce CBF-SLNR performance below the uncoordinated baseline, because the precoder suppresses interference toward users that are no longer active. Coordination on stale information is therefore worse than no coordination at all. To address this, we propose a two-stage predictive framework in which a Spectral Temporal Graph Neural Network (StemGNN) predicts future user equipment (UE) scheduling states from delayed historical observations, and the predictions replace stale inputs to the CBF-SLNR precoder. Evaluated on a three-cell massive MIMO downlink with 60 UEs and 64 antennas per base station under Quadriga Urban Micro (UMi) channels and a proportional fair scheduler, StemGNN achieves a mean scheduling prediction accuracy of 87.57%, outperforming LSTM, GRU, Simple RNN, and Markov chain baselines at all evaluated horizons, with gains of up to 7.71% over LSTM at longer horizons where inter-UE structural dependencies dominate over temporal autocorrelation. When integrated into coordinated beamforming, the predictions recover 57-73% of the sum rate loss caused by one TTI of backhaul delay, improving sum rate by 9.58-14.35% over the no-prediction baseline and recovering up to 83% of the Lag-1 fairness loss for cell-edge users, with fairness gains persisting at higher lag values where throughput gains diminish. These results show that treating backhaul latency as a spatio-temporal forecasting problem is an effective approach for robust inter-cell coordination in delay-constrained networks.
P. Singh, Shubham Vaishnav, A. Gokceoglu et al.· 0 citations
We consider convergecast in a multi-hop sensor network, in which sensors sample physical processes of interest and transmit packets containing these samples to a single sink node over a routing tree. The sink node remotely estimates these processes based on received packets. Our objective is to design centralized multi-hop sampling and scheduling policies that minimize the average age of information (AAoI) across sensors for networks operating under a complete interference model as well as random losses in the wireless network. We propose a policy that samples sources and schedules links according to an analytically characterized priority index. Through simulations, we compare the AAoI of the proposed policy with policies from prior work and show improved performance. The improvement in performance is observed to be larger as the number of sources increases or when the reliability of links decreases demonstrating the utility of the proposed policy. An extension to $K$ -hop interference model is also discussed.
N. Raj, V. Sukumaran· IEEE Wireless Communications...· 0 citations
In this paper, we investigate downlink scheduling for urban air mobility (UAM) in a cooperative space-air-ground integrated network. Multiple ground stations (GSs) employ narrow three-dimensional beams and share spectrum across multiple subbands, while a satellite provides an orthogonal-band service option. Rapidly time-varying geometry and directional interference require joint decisions on base station association, GS subband assignment, and transmit powers. We formulate a finite-horizon mixed discrete-continuous problem that maximizes sum rate while penalizing handovers and GS overload, using only UAM positions and velocities. To address the combinatorial scheduling problem, we propose GeoSetPPO, a geometry-aware set-attention proximal policy optimization (PPO) method that outputs per-UAM discrete association and subband decisions with permutation-invariant representations. Conditioned on each schedule, GS powers are computed by a per-slot successive convex approximation (SCA) module under per-GS power budgets and minimum signal-to-interference-plus-noise ratio (SINR) constraints. To reduce training cost and improve stability, we adopt a two-stage training strategy that transitions reward evaluation from uniform power to SCA-based power allocation. Simulations demonstrate stable convergence, higher returns than multi-layer perceptron (MLP)- and Transformer-based PPO under the considered training setting, and favorable reward and schedule-feasibility performance relative to algorithm-based and distance-based schedulers. In the larger evaluated network, GeoSetPPO also reduces the scheduling latency from 40.84 ms to 2.90 ms relative to the previous algorithm-based method.
Hyung-Joo Moon, Sangha Park, Chan-Byoung Chae et al.· 0 citations
In this paper, we investigate the downlink performance of multi-cell RSMA-enabled ISAC networks in which base stations (BSs), communication users, and sensing targets are spatially distributed according to independent Poisson point processes (PPPs). Each BS simultaneously serves multiple users using RSMA while exploiting the common stream as a dual-functional communication and sensing waveform. The users are equipped with FAS that selects the best antenna port to maximize the received signal quality. Closed-form analytical expressions are derived for the ergodic sum-rates by combining stochastic geometry, order statistics, and Laplace-transform-based interference analysis. Furthermore, a tractable approximation for the average radar SINR is developed by characterizing the statistical properties of the common precoder. Leveraging the derived analytical expressions, a low-complexity analytical resource allocation framework is proposed to jointly optimize the RSMA power allocation, the communication-sensing beam tradeoff, and the number of scheduled users while sat- isfying the sensing quality-of-service constraint. Compared with conventional iterative optimization approaches, the proposed analytical design significantly reduces computational complexity while achieving nearly identical communication performance. Simulation results verify the accuracy of the developed analytical expressions and demonstrate substantial improvements in both RSMA sum-rate and sensing performance over conventional transmission schemes.
Abdelhamid Salem, Hana Shamata, Salma M. Elkawafi et al.· 0 citations
Integrated Sensing and Communication (ISAC) is emerging as a key technology for next-generation wireless networks, enabling simultaneous communication and sensing functionalities. This paper focuses a RIS-assisted full-duplex (FD) ISAC system, in which a multi-antenna base station (BS) concurrently performs multi-user uplink and downlink transmission while also carrying out radar sensing. To maximize the joint uplink–downlink sum rate, an optimization problem is formulated under practical constraints, such as radar detection SINR, self-interference, BS transmit power, user power budgets, and RIS unit-modulus conditions. To address the nonconvexity of this problem, a two-stage hybrid optimization approach is developed. In the first stage, the augmented Lagrangian technique decomposes the complex problem into simpler subproblems involving beamforming, power allocation, and RIS phase optimization, leading to a feasible initial solution. The second stage employs a Multi-Agent Deep Deterministic Policy Gradient (MADDPG) framework to refine this solution adaptively, enabling the system to respond effectively to variations in the channel environment, mobility patterns, and interference levels. The proposed hybrid framework achieves optimal resource allocation while maintaining feasibility, robustness, and adaptability. Analytical results confirm its convergence behavior, and extensive simulation results confirm that the proposed scheme consistently outperforms conventional optimization and single-agent DRL baselines in sum-rate maximization, interference mitigation, and sensing accuracy, confirming its effectiveness for RIS-assisted full-duplex ISAC systems.
S. Waqas, Fenghua Huang, Fakhar Abbas et al.· IEEE Transactions on Wireles...· 0 citations
This study investigated the effectiveness of spatio-temporal scheduling in coordinated multi-transmitter WPT systems and demonstrated the potential of the proposed scheme for robust and efficient power supply, even under shadowing conditions.
Yuna Sawada, Shino Shiraki, N. Kikuma et al.· 0 citations