PaperlayerView exact revision on arXiv

Source-linked AI summary

User-Target Pairing for NOMA-Enabled ISAC in Low-Altitude Wireless Networks

Irfan Azam, Muddasir Rahim, Soumaya Cherkaoui

arXiv:2610.09080v1eess.SP

TL;DR

LAWN NOMA-ISAC must coordinate user pairing and target assignment to balance communication and sensing. The paper proposes MUTP with APA; simulations report improved communication and sensing outcomes versus fixed-pairing and conventional ISAC baselines, while closely approaching exhaustive-search performance.

  • Problem

    In NOMA-ISAC, communication and sensing depend on how users are paired and targets assigned, but communication-oriented or sensing-only choices can compromise the other objective.

    Source: §I · pp. 1–2

  • Method

    MUTP forms strong-weak user pairs and assigns targets using a sensing-aware criterion, while APA selects candidate power profiles to balance sum rate, QoS, and sensing reliability.

    Source: A. Motivation and Contributions · p. 2, A. Matching-Based User-Target Pairing · p. 4

  • Results

    The proposed NISAC-MUTP-APA improves sum rate, worst-target detection probability, and communication and sensing outage versus GP and OISAC benchmarks, while closely approaching exhaustive-search performance with lower association complexity.

    Source: §V · pp. 5–6, §VI · p. 6

  • Takeaways & Limitations

    The simulations support a lower-association-complexity alternative to exhaustive search that retains near-exhaustive performance in the evaluated LAWN setting.

    Source: §VI · p. 6

    APA searches a finite candidate set rather than solving P globally; richer candidate profiles and finer resolutions can reduce the gap at higher complexity.

    Source: B. QoS and Sensing-Aware Adaptive… · p. 5

Abstract

from arXiv · show

This paper presents a non-orthogonal multiple access (NOMA)-enabled integrated sensing and communication (ISAC) framework for low-altitude wireless networks (LAWN), where an aerial access point simultaneously serves ground users and senses aerial targets. To improve both communication efficiency and sensing reliability, a matching-based user-target pairing (MUTP) scheme is proposed, in which strong and weak channel NOMA users are first paired into NOMA groups and then assigned to sensing targets according to a sensing-aware pairing criterion. Furthermore, an adaptive power allocation (APA) strategy is developed to balance communication quality-of-service requirements and target detection performance. The proposed MUTP-APA scheme is compared with fixed greedy pairing, exhaustive search pairing, and conventional ISAC benchmark schemes under both fixed power allocation and APA. Simulation results show that our proposed scheme achieves higher sum rate, improved worst-target detection probability, and lower communication and sensing outage probabilities compared with conventional ISAC and fixed pairing baselines in NOMA-ISAC LAWN. Moreover, the proposed method closely approaches the exhaustive search benchmark while avoiding the high computational complexity.

I. INTRODUCTION

LAWN NOMA-ISAC requires user pairing and target assignment that jointly account for communication and sensing. The paper proposes MUTP and APA to address this coupling.

I. INTRODUCTION

  • Communication-oriented pairing can produce poorly illuminated targets, while sensing-only assignments may reduce rates or violate QoS; LAWN NOMA-ISAC therefore needs joint CU pairing and ST assignment.Prior work on NOMA-ISAC pairing has focused mainly on terrestrial or cell-free settings, leaving LAWN-specific mobility, target-distance effects, and scalable pairing underexplored.Source: §I · pp. 1–2

A. Motivation and Contributions

  • MUTP forms strong-weak NOMA pairs using QoS-aware utilities, then assigns STs to groups with a sensing-aware max-min criterion.Source: A. Motivation and Contributions · p. 2
  • APA evaluates candidate power profiles and selects among them using sum rate, QoS satisfaction, and sensing reliability.Source: A. Motivation and Contributions · p. 2
  • Simulations report improved sum rate, worst-target detection probability, and communication and sensing outage versus GP and conventional ISAC, while approaching exhaustive-search performance with lower complexity.Source: A. Motivation and Contributions · p. 2

II. SYSTEM MODEL

The system model places an ISAC access point in a low-altitude network, serving ground users while sensing aerial targets. Its geometry and channel assumptions reflect altitude, direction, and limited pointing mismatch.

II. SYSTEM MODEL

  • A single ISAC access point with a transmit ULA serves ground CUs and senses aerial STs, forming the system model shown in Fig. 1.The model uses strong-weak NOMA groups and focuses on one-to-one group-target assignment with M = G; other group-target counts are handled by subset assignment or left partly unassigned.Source: Figure 1 · p. 2, §II · p. 2

A. LAWN Geometry and Channel Model

  • The AP-CU and AP-ST links use 3D distances, so AP altitude affects both communication channel strength and sensing reliability.Source: A. LAWN Geometry and Channel Model · p. 2
  • The model uses LoS-dominant, distance-dependent array channels with an effective angular direction and bounded pointing mismatch; Doppler and fast trajectory changes are negligible or compensated within a frame.UPA azimuth-elevation beamforming and Doppler-aware modeling are left for future work.Source: A. LAWN Geometry and Channel Model · pp. 2–3

B. NOMA Communication Model

Within each NOMA group, strong and weak users share a group beam and bandwidth, with power splitting and decoding rules governing their rates and QoS.

B. NOMA Communication Model

C. Sensing Model

The sensing model assigns each NOMA group to a target and evaluates target-link sensing performance, including detection probability and the weakest target’s reliability.

C. Sensing Model

  • Each NOMA group maps to one ST, and its sensing gain depends on target distance and steering-vector direction.Source: C. Sensing Model · p. 3
  • Target detection probability follows a noncoherent energy-detection model for a nonfluctuating target, with a threshold set by the false-alarm probability.Source: C. Sensing Model · p. 3
  • The design tracks worst-target detection probability and jointly optimizes pairing, assignment, group power, NOMA power split, and beam control under QoS and sensing-reliability constraints.Source: C. Sensing Model · p. 3

III. PROBLEM FORMULATION

The formulation jointly designs strong-weak user pairing, group-target assignment, group power allocation, and beam control. Its constraints specify communication QoS, target detection, power budgets, and feasible assignment and control ranges.

  • The design jointly selects strong-weak user pairs, sensing targets for NOMA groups, group powers, and communication-sensing beam control.Source: §III · p. 3
  • The algorithm initializes group powers equally as P_g = P_T/G before forming the NOMA groups.Source: §III · p. 4
  • Algorithm 1 builds preference lists from pair utilities and applies stable matching to form the strong-weak user pairs.Source: §III · p. 4
  • Constraints require minimum rates for both users, target detection probability, a total power budget, feasible power split and beam control, and valid pairings and assignments.Source: §III · p. 4

IV. MATCHING-BASED USER-TARGET PAIRING AND ADAPTIVE POWER ALLOCATION

The paper proposes a low-complexity MUTP-APA framework for a joint design with discrete associations and continuous power and beam-control variables. It constructs communication- and sensing-aware associations before refining power allocation using QoS and sensing awareness.

  • The joint problem combines discrete user pairing and group-target assignment with continuous power allocation and beam control.Source: §IV · p. 4
  • The MUTP-APA framework first constructs communication- and sensing-aware associations, then refines power allocation with QoS and sensing awareness.Source: §IV · p. 4
  • The framework addresses the computational expense of exhaustive association search, especially as the numbers of users and targets increase.Source: §IV · p. 4

A. Matching-Based User-Target Pairing

The matching stage forms NOMA groups and assigns sensing targets to them. It uses QoS-aware utilities for strong-weak user matching and selects target assignments to improve the weakest group-target sensing gain.

A. Matching-Based User-Target Pairing

B. QoS and Sensing-Aware Adaptive Power Allocation

APA evaluates a finite set of candidate power profiles and selects a solution according to sum rate when constraints are met, or joint communication-sensing performance otherwise. The candidate-based approach is not globally optimal, and its performance gap depends on candidate-set richness and resolution.

B. QoS and Sensing-Aware Adaptive Power Allocation

V. SIMULATION RESULTS AND DISCUSSION

The simulations compare the proposed NISAC-MUTP scheme with fixed greedy pairing, exhaustive search, and OISAC benchmarks under fixed power allocation and APA. Across sum rate, worst-target detection, and outage results, the proposed scheme performs favorably against fixed-pairing and OISAC baselines and closely follows exhaustive search.

V. SIMULATION RESULTS AND DISCUSSION

  • The proposed NISAC-MUTP scheme closely approaches exhaustive search while providing lower pairing complexity, with exhaustive search achieving the highest sum rate.OISAC benchmarks achieve the lowest sum rate because orthogonal access divides group resources between users.Source: §V · p. 5
  • The proposed MUTP scheme follows exhaustive search closely for worst-target detection probability and reaches the required probability at lower SNR than fixed GP and OISAC.The comparison uses the weakest sensed target's detection probability.Source: §V · p. 5, Figure 3 · p. 5
  • NISAC-MUTP-APA reduces user-QoS constraint violations compared with fixed GP and OISAC, with a more visible COP reduction at medium to high SNR.Proper user pairing and APA become more effective in that SNR region.Source: §V · p. 5, Figure 4 · p. 6
  • Sensing outage probability decreases rapidly with SNR for NISAC-MUTP-APA, while OISAC requires higher SNR to reduce sensing outage.SOP occurs when at least one assigned target fails to meet its required detection probability; sensing-aware assignment improves the weakest group-target links, and APA supports detection reliability.Source: §V · p. 6, Figure 4 · p. 6

VI. CONCLUSION

The conclusion presents an altitude-aware NOMA-ISAC design for ground communication users and aerial sensing targets, combining user-target pairing with adaptive power allocation. Simulations show improved communication and sensing outcomes over fixed-pairing and OISAC benchmarks while approaching exhaustive-search performance at lower association complexity.

VI. CONCLUSION

  • The proposed NISAC-MUTP-APA scheme improves sum rate, worst-target detection probability, and communication and sensing outage compared with GP and OISAC benchmarks, while closely approaching exhaustive-search performance with lower association complexity.The design forms strong-weak NOMA user pairs, assigns sensing targets to NOMA groups, and uses APA to balance sum rate, user QoS, and sensing reliability.Source: §VI · p. 6
  • The framework jointly considers ground communication users and aerial sensing targets in an altitude-aware LAWN system.Source: §VI · p. 6
  • Future work will consider UPA-based 3D beamforming, Doppler-aware mobility modeling, imperfect CSI, and UAV trajectory optimization.Source: §VI · p. 6
Loading 2610.09080v1…