Resource Allocation and Node Deployment for Multi-UAV Integrated Localization and Communication with Differentiated Requirements

ZHAO Yicheng; WANG Hai; QIN Zhen; SUN Weihao

doi:10.11999/JEIT260819

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ZHAO Yicheng, WANG Hai, QIN Zhen, SUN Weihao. Resource Allocation and Node Deployment for Multi-UAV Integrated Localization and Communication with Differentiated Requirements[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260819

Citation:

ZHAO Yicheng, WANG Hai, QIN Zhen, SUN Weihao. Resource Allocation and Node Deployment for Multi-UAV Integrated Localization and Communication with Differentiated Requirements[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260819

Citation:

ZHAO Yicheng, WANG Hai, QIN Zhen, SUN Weihao. Resource Allocation and Node Deployment for Multi-UAV Integrated Localization and Communication with Differentiated Requirements[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT260819

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Resource Allocation and Node Deployment for Multi-UAV Integrated Localization and Communication with Differentiated Requirements

doi: 10.11999/JEIT260819 cstr: 32379.14.JEIT260819

1.
College of Communication Engineering, Army Engineering University of PLA, Nanjing 210007, China
2.
Noncommissioned Officer Academy of PAP, Hangzhou 311499, China
3.
PLA Unit 61516, Beijing 100071, China

Received Date: 2026-06-18
Accepted Date: 2026-08-17
Rev Recd Date: 2026-08-13

Available Online: 2026-08-25

Abstract

Abstract

Objective In areas with weak ground-network coverage or limited Global Navigation Satellite System (GNSS) availability, multiple dual-functional Unmanned Aerial Vehicles (UAVs) can provide uplink communication and cooperative localization services. Ground terminals have different requirements for data volume, minimum communication rate, localization-accuracy threshold, service priority, and communication and localization service weights. Maximizing communication rate, localization accuracy, or a weighted sum of the two cannot directly reflect these differentiated requirements. A service below its activation threshold may be unusable, whereas resources allocated after demand saturation provide little additional value. A quasi-static service period with prior terminal positions is therefore considered. A topology-dependent Value of Service (VoS) is formulated, and Physical Resource Block (PRB) scheduling and UAV positions are jointly optimized. Methods Data or Sounding Reference Signals (SRSs) are transmitted by terminals over assigned PRBs. Time Difference of Arrival (TDoA) measurements are formed by synchronized UAVs, and a probabilistic air-to-ground channel model determines communication access and valid localization anchors. Communication VoS combines a sigmoidal rate utility with data completeness. Position Error Bound (PEB), derived from the accumulated Fisher Information Matrix (FIM), is mapped to an exponential utility to quantify localization VoS. Terminal priorities and communication and localization service weights are used to aggregate the two service values. The resulting problem P0 couples binary scheduling, non-concave utilities, time-frequency resources, service relationships, localization geometry, and UAV positions. For a fixed topology, the average number of allocated PRBs and active slots are used to construct a continuous service-level resource profile. This profile approximates the original schedule but does not provide an upper bound. Bandwidth responses are obtained by deterministic one-dimensional branch-and-bound search with damped Newton refinement and shadow-price bisection. Slot responses are obtained by deterministic comparison of a finite set of service-boundary, integer-slot, and feasible-domain-boundary candidates. Adjacent-integer recovery and deterministic slot packing are then used to construct a feasible integer schedule under resource-capacity and terminal-power constraints. If packing fails, the upward-rounded component with the smallest unit VoS loss is rolled back, and the schedule is repacked. VoS is then recomputed from the recovered integer schedule. Thus, the reported schedules remain feasible for P0 without any claim of global optimality. The fixed-topology resource-allocation procedure produces a complete resource response, including the recovered feasible schedule and its VoS, which is subsequently used to drive deployment. Coverage-repair, localization-geometry-improvement, and resource-saving candidates are generated according to service deficits, service relationships, and resource consumption. Normalized proxy scores, per-UAV candidate truncation, and beam search reduce the number of complete resource-response evaluations. For each retained deployment, service relationships are rebuilt, including communication access and localization anchors, and the FIM and resource allocation are recomputed. A candidate is accepted only when the VoS gain obtained from its complete resource response exceeds the preset threshold. The resulting outer sequence is monotonic and bounded and converges to a stable point within the generated candidate set. Resource allocation and node deployment remain coupled throughout the procedure. For each retained topology, communication access, localization anchors, the FIM, and the feasible integer schedule are recomputed before VoS is evaluated. Proxy scores are used only to rank candidates, whereas final acceptance is always based on the complete resource response. The next topology is therefore not selected from distance or localization geometry alone. The accepted update reflects terminal demand, resource scarcity, and localization geometry under the same feasible scheduling constraints used in the objective. This design keeps the optimization objective consistent with the final deployment decision and avoids a geometry-only selection rule. Results and Discussions Simulations are conducted in a 700 m × 700 m area with four UAVs at a baseline deployment height of 150 m. Communication-dominant, localization-dominant, and balanced terminals are included in equal proportions. At each load, all methods share 30 independent scenarios and identical random seeds. The deployment-height experiment reports a 95% confidence interval based on 1,000 scenario-level bootstrap resamples. For fair comparisons, all resource-allocation methods use the same topology, resource pool, and random scenarios. All deployment methods use the same proposed joint resource-allocation response, are evaluated from a cold start, and are subject to the same cap on complete resource-response evaluations, with initialization and training costs included. VoS-driven allocation yields smaller communication-rate and localization-accuracy demand deviations than the communication-priority and average-utility metrics because service saturation redirects resources from overprovisioned requests to insufficiently served requests (Fig. 2). In the convergence test, three feasible initializations converge to similar system VoS values. Damping suppresses oscillations near transitions between non-concave segments, and the small-scale benchmark indicates limited empirical loss from the continuous response and integer recovery (Fig. 3). With increasing terminal load, the proposed joint allocator outperforms a Particle Swarm Optimization (PSO)-based slot-response variant, as well as non-joint, learning-based, weight-driven, and random allocation methods (Fig. 4). Communication service is more sensitive to terminal load because both communication rate and data completeness must be maintained, whereas localization service accumulates information across anchors and slots. Resource-pool tests further show that additional bandwidth provides little benefit when slots are scarce, whereas increasing SRS bandwidth more directly improves localization. The structured deployment search achieves higher VoS with shorter end-to-end deployment time than PSO, Differential Evolution (DE), and Bayesian Optimization (BO) by focusing complete resource-response evaluations on service-aware candidates (Fig. 5). VoS first increases and then decreases with UAV deployment height because improved line-of-sight probability and multi-anchor visibility compete with increased propagation distance and path loss. Horizontal deployment updates balance communication-link quality and localization geometry according to terminal requirements (Fig. 6). Conclusions The proposed framework evaluates communication and localization services according to demand satisfaction and coordinates time-frequency resource allocation with iterative UAV deployment updates through complete resource responses and structured candidate search. It improves demand matching, system VoS, deployment efficiency, and interpretability while preserving feasibility under the original scheduling constraints. Prior terminal positions and a quasi-static service period are assumed. Future work will address dynamic terminal movement and changing demands over longer service periods through dynamic resource allocation and continuous UAV trajectory optimization.
- Integration of localization and communication,
- Value of service,
- Resource allocation,
- Node deployment

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References(16)

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