🤖 AI Summary
This work addresses the inherent unfairness in uplink NOMA-ISAC systems, where conventional resource allocation strategies favor strong users at the expense of weak users, resulting in persistently low throughput and poor fairness for the latter. To overcome this limitation, the authors propose a novel Proportional Fairness-based Joint User Grouping and Power Allocation (PF-JUGPA) method, which uniquely incorporates users’ historical service rates into both scheduling and resource allocation. By jointly optimizing user grouping and power allocation under a proportional fairness criterion, the proposed approach achieves an effective trade-off between system throughput and user fairness while preserving sensing performance. Experimental results demonstrate that PF-JUGPA significantly improves the Jain fairness index and the average rate of weak users, with only a marginal reduction in total system throughput.
📝 Abstract
This letter addresses long-term fairness in uplink non-orthogonal multiple access integrated sensing and communication (NOMA-ISAC) systems. Existing resource allocation schemes that maximize instantaneous sum rate often favor strong users, leaving historically underserved users with poor long-term throughput. We propose PF-JUGPA, a proportional-fair scheduling based joint user grouping and power allocation method. PF-JUGPA first pre-selects users via a PF metric combining instantaneous rate proxies and historical averages, then performs fairness-aware grouping and power allocation by maximizing a weighted sum rate whose weights are inversely proportional to historical service rates. Simulation results show that PF-JUGPA significantly improves the Jain fairness index and weak-user average rates with only a modest sum-rate loss compared to sum-rate-oriented and round-robin baselines. The findings confirm that embedding long-term service history into both scheduling and resource allocation yields an effective throughput--fairness--sensing tradeoff in uplink NOMA-ISAC.