Searcharxiv⌕ Search

arXiv · 2610.25307

Queue-Aware ISAC Power Allocation Under Imperfect CSI: Integer-Service Analysis and Anchored Lyapunov Look-Ahead

Abstract

We study power allocation for a multiuser integrated sensing and communication (ISAC) downlink with bursty queues and imperfect channel state information (CSI). Starting from a long-run utility--backlog objective, we derive its common task reward and online score. Integer departures are represented by threshold events of indefinite Gaussian quadratic forms, accounting for estimation error in both signal and interference. Their moments give the conditional one-slot task reward and characterize the error of an efficient plug-in score. An anchored quadratic Lyapunov look-ahead corrects its reward-greedy action with a bounded nominal loss. Across five CSI-error levels, the rule reduces mean queue exceedance by 27.4--43.9\% (0.49--0.99 percentage points) relative to the plug-in maximizer, with mean reward loss below 0.01 per slot. The rule achieves higher reward and lower exceedance than DPP, SAC and PPO; its advantage over freshly retrained learning baselines extends to five configurations with 9--23 power-allocation variables using fixed candidate budgets. For the seven-variable system, a fused implementation reproduces all 25 confirmation trajectories and achieves a one-core p99 decision time of 0.274--0.311\,ms.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hanyi Sun, Yifan Zhou, Yixiao Gu. 2026-10-18. Queue-Aware ISAC Power Allocation Under Imperfect CSI: Integer-Service Analysis and Anchored Lyapunov Look-Ahead. https://arxiv.org/abs/2610.25307

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

6G: From Connectivity Infrastructure to Guaranteed Digital Services

Sixth-generation (6G) mobile networks offer an opportunity to align network architecture with operator control, verifiable service outcomes, and governed automation. This article develops four complementary contributions. The Control Compact allocates ownership and governance responsibilities through an own-federate-consume taxonomy. The Guarantee Economy proposes six outcome-priced service tiers aligned with IMT-2030 usage scenarios. The Network MCP Platform, the principal contribution, is a conceptual reference architecture that uses the Model Context Protocol (MCP) to connect agentic workflows to operational tools and telecom execution interfaces under operator-defined governance. Two real-network examples from Rakuten Mobile illustrate MCP-enabled agentic workflows: radio access network (RAN) second-line (L2) support combines diagnosis, remediation, and outcome verification, while IP Multimedia Subsystem (IMS) availability investigation combines evidence collection, adaptive investigation, and reporting. These examples support operational feasibility; evaluation of the complete platform, and its performance and security properties, remains future work. A separate TM Forum Level 4 assessment corroborates deployed closed-loop autonomy for a scoped RAN energy-optimization scenario. The fourth contribution presents Rakuten Mobile's public standardization position and a phased deployment roadmap. The analysis distinguishes operational records, external assessment evidence, standards-based requirements, and proposed architectural and commercial extensions. It identifies validation requirements for broader deployment and outcome-based service assurance.

cs.NI↗

Blackbox Performance Analysis of LLM-based Network Control

LLMs are becoming an indispensable part of network control systems due to their impressive reasoning. Before such LLM-enabled systems can be deployed, it is imperative to develop tools that can audit their performance on previously unseen inputs. However, auditing these systems is challenging due to the inherent opacity of LLMs. LENS is the first step towards black-box performance analysis of LLM-enabled network control systems. It uses novel methods to extend deep reinforcement learning to guide the search for performance gaps and inputs that make LLMs struggle. LENS finds large gaps for LLM-based traffic engineering and GPU allocation systems, and even matches existing white-box analyzers designed for hand-crafted TE heuristics.

cs.NI↗

NetGuise: Adversarial Traffic Generation for Early-Stage Classifier Evasion

Early-stage encrypted traffic classifiers make decisions from only the first few packets of a connection, enabling low-latency classification but raising robustness concerns under realistic traffic morphing. Existing adversarial traffic generation methods often assume complete flows, feature-level perturbations, or iterative runtime feedback, which do not match the constraints of early-stage execution. We present NetGuise, a target-conditioned traffic morphing framework that generates packet-level padding and non-negative delay actions while preserving packet direction, ordering, and packet count and respecting MTU, bandwidth, and latency constraints. We evaluate NetGuise for censorship evasion under two observation interfaces, i) a history-only causal interface for online packet-by-packet execution, ii) and a full-window oracle interface that provides an upper-bound characterization of early-stage morphability. On VANT, NetGuise achieves 33.2% mean targeted evasion under the causal interface versus 82.0% under the oracle interface; on UTMobileNet, the corresponding results are 5.6% and 81.8%. NetGuise outperforms the strongest causal baseline, BAP, on both datasets and, on VANT, reduces bandwidth overhead from 19.80% to 3.02% and generation latency from 37.42 ms/flow to 1.16 ms/flow. Under the oracle interface, it achieves up to 85.7% targeted evasion, retains 43.5% after four rounds of adaptive censor retraining, and maintains 48% under zero-shot cross-environment transfer. Our results show that early-stage classifiers can remain morphable despite high clean accuracy and that robustness evaluation should account for targeted morphability, observation constraints, defender adaptation, and operational network conditions.

cs.NI↗