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An Integrated Communication and Computing Scheme for Wi-Fi Networks based on Generative AI and Reinforcement Learning

Xinyang Du, Xuming Fang

arXiv:2404.13598v1cs.NIeess.SP

TL;DR

Edge networks face sparse samples, and traditional DRL may require extensive environmental interaction, increasing training cost and time. The paper combines a diffusion policy with TD3 for offloading and uses the Hungarian algorithm for resource allocation in 802.11ax Wi-Fi; simulations report lower system latency and energy consumption, enhanced QoS, and better convergence than traditional RL methods.

  • Problem

    Sparse samples in edge computing can make DRL training costly and time-consuming because models require extensive interaction with the environment.

    Source: §I · p. 1

  • Method

    DTD3 uses a diffusion model as TD3's policy network for multi-user edge-network offloading, alongside a Hungarian-algorithm resource-allocation scheme for 802.11ax Wi-Fi.

    Source: §I · p. 2, B. DTD3 Offloading Decision Algorithm · p. 4

  • Results

    The proposed approach reduces overall system energy consumption and latency, enhances QoS, ensures communication success rate, and converges better than traditional RL methods.

    Source: §V · p. 6

  • Takeaways & Limitations

    The proposed framework combines offloading decisions and Wi-Fi-oriented resource allocation in multi-STA, single-AP 802.11ax scenarios.

    Source: §V · pp. 5–6

Abstract

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The continuous evolution of future mobile communication systems is heading towards the integration of communication and computing, with Mobile Edge Computing (MEC) emerging as a crucial means of implementing Artificial Intelligence (AI) computation. MEC could enhance the computational performance of wireless edge networks by offloading computing-intensive tasks to MEC servers. However, in edge computing scenarios, the sparse sample problem may lead to high costs of time-consuming model training. This paper proposes an MEC offloading decision and resource allocation solution that combines generative AI and deep reinforcement learning (DRL) for the communication-computing integration scenario in the 802.11ax Wi-Fi network. Initially, the optimal offloading policy is determined by the joint use of the Generative Diffusion Model (GDM) and the Twin Delayed DDPG (TD3) algorithm. Subsequently, resource allocation is accomplished by using the Hungarian algorithm. Simulation results demonstrate that the introduction of Generative AI significantly reduces model training costs, and the proposed solution exhibits significant reductions in system task processing latency and total energy consumption costs.

I. INTRODUCTION

Latency-sensitive IoT applications drive demand for fast, low-latency, low-energy computing, while traditional cloud architectures cannot provide large-scale real-time processing for massive front-end devices. The paper addresses Wi-Fi resource-allocation mismatches and costly DRL training with a generative-AI and DRL offloading and resource-allocation solution.

Motivation

  • Latency-sensitive IoT applications increase demand for high-speed, low-latency, low-energy computing, while traditional cloud architectures cannot provide large-scale real-time processing for massive front-end devices.The paper presents edge computing as a response to this demand.Source: §I · p. 1

Research gap

  • Earlier joint optimization studies assume communication resources can be arbitrarily partitioned, which the authors say does not apply to existing Wi-Fi networks.Time-varying wireless channels and low DRL sample efficiency are also identified as challenges; extensive environmental interaction raises training cost and time.Source: §I · p. 1

Contributions

  • The paper proposes a multi-user MEC offloading and resource-allocation solution for communication-computing integration in 802.11ax Wi-Fi.Its stated contributions include a Hungarian-algorithm resource-allocation scheme based on Wi-Fi resource-allocation characteristics.Source: §I · p. 2
  • DTD3 uses a Generative Diffusion Model as TD3's policy network to address sparse samples and reduce convergence time while minimizing weighted latency and power consumption.The paper describes this as a task-offloading decision solution based on generative AI and DRL.Source: §I · p. 2

II. SYSTEM MODEL

The system model places MEC servers at a single AP serving multiple STAs, divided into computing and communication users. Its 802.11ax wireless model allocates discrete resource units and derives uplink rates using channel and modulation mappings.

A. Network Model

  • The model has one AP with AP-side MEC servers and multiple STAs; computing STAs may offload intensive tasks, while communication STAs need uplink bandwidth but no AP computing resources.The system-model figure depicts this architecture.Source: A. Network Model · p. 2, Figure 1 · p. 2
  • Each time slot generates one task per STA, and when AP resources cannot meet all demands, communication STAs receive priority over computing tasks.Computing tasks are described by data size, CPU cycles, and a latency constraint; communication tasks by uplink data size and latency constraint.Source: A. Network Model · p. 2

B. Wireless Transmisson Model

  • 802.11ax OFDMA resources are allocated in discrete RUs, represented as integer multiples of 26-tone units: 1, 2, 4, 9, 18, or 36.For the stated 80 MHz bandwidth, the corresponding RU sizes include 26-, 52-, 106-, 242-, 484-, and 996-tone units.Source: B. Wireless Transmisson Model · p. 2
  • The maximum achievable uplink transmission rate is derived from SNR, allocated RU size, and mappings between RU and rate and between SNR and selectable MCS.The model relates receiver RSSI to MCS using the protocol mapping.Source: B. Wireless Transmisson Model · p. 3

C. Computing Model

The computing model compares local execution with whole-task offloading and accounts for delay and energy in computing and communication. It treats the smaller returned result data as negligible in offloaded-computation delay and energy.

Computing modes

  • Computing tasks cannot be divided; local execution delay and energy depend on the device CPU capacity and the task's required CPU cycles.The model distinguishes local execution from offloaded execution.Source: C. Computing Model · p. 3

Offloaded computing

  • Offloaded computation comprises task upload, AP execution, and result return, with the return stage's delay and energy treated as negligible because result data are much smaller than task data.The model expresses offloading delay and energy using allocated AP computing resources, transmission rate, task data size, and STA transmission power.Source: C. Computing Model · p. 3

Communication tasks

  • Communication-task delay and energy are modeled separately, and total system delay and energy are then summarized.Source: C. Computing Model · p. 3

D. Problem Formulation

The formulation minimizes a weighted sum of system delay and energy consumption over offloading and AP communication and computation resources. Integer offloading and RU variables make the problem nonconvex and difficult to solve with traditional optimization algorithms.

Objective

  • The paper minimizes a weighted sum of system delay and energy consumption, with binary local/offloaded execution decisions and AP communication and computation resource allocations as decision variables, consistent with and.Source: D. Problem Formulation · p. 3

Constraints

  • The formulation constrains task delays, binary offloading decisions, AP communication and computation resources, and RU allocations to the supported discrete sizes.The AP resource maxima are Bmax and Fmax; RU sizes are 1, 2, 4, 9, 18, or 36.Source: D. Problem Formulation · p. 3

Solution framing

  • Integer offloading and RU variables make the optimization problem nonconvex and NP-hard, so the paper formulates it as an MDP and uses generative AI and DRL to seek offloading and resource-allocation values.The NP-hard characterization is attributed to prior work.Source: D. Problem Formulation · p. 3

III. OFFLOADING AND RESOURCE ALLOCATION SCHEME BASED ON DTD3

The RL formulation defines a state from task and MEC-resource information, uses binary offloading decisions as actions, and shapes rewards around latency, energy, and communication-task priorities.

A. Three Key Elements for RL

B. DTD3 Offloading Decision Algorithm

DTD3 uses a diffusion model as the TD3 policy network for multi-user edge offloading. Its reverse diffusion chain produces state-conditioned actions, while Q-values enter training of the policy objective.

B. DTD3 Offloading Decision Algorithm

C. Resource Allocation Scheme

The allocation scheme prioritizes demanding computing tasks, distributes MEC computing resources, and searches RU configurations to select a communication allocation minimizing transmission latency and energy consumption.

C. Resource Allocation Scheme

  • Tasks with poorer channels, larger data, greater CPU needs, or tighter latency constraints receive higher priority and more computing resources.Source: C. Resource Allocation Scheme · p. 4
  • The scheme determines RU-specification combinations from STA counts and available bandwidth, then uses a Hungarian algorithm to assign RUs and calculate transmission latency and energy.Source: C. Resource Allocation Scheme · pp. 4–5
  • It selects the RU configuration combination that minimizes STA transmission latency and energy consumption; when tasks exceed available RUs, lower-priority tasks are no longer offloaded.Source: C. Resource Allocation Scheme · p. 5

IV. SIMULATION RESULT

The simulations specify the network and task settings and compare DTD3 with local, full, random, and DQN offloading baselines. The supplied passages describe the evaluation setup and measures.

IV. SIMULATION RESULT

  • The simulation places STAs randomly within 20 m of a fixed AP, uses 10 GHz MEC and 1 GHz STA CPUs, and sets task latency limits to 80%–120% of local-computation latency.Source: §IV · p. 5
  • DTD3 is compared with local, full, and random offloading using even RU allocation, plus DQN offloading with Hungarian-algorithm RU allocation.Source: §IV · p. 5
  • The evaluation defines QoS as the share of computing tasks meeting latency limits and communication success rate as the share of communication tasks completed within their limits.Source: §IV · p. 5

A. Scenario with Varying Computing STAs

With three communication STAs, increasing computing STAs raises total cost and strains AP resources; DTD3 maintains stronger QoS and communication success than competing approaches.

A. Scenario with Varying Computing STAs

B. Scenario with Varying Capacity of MEC

As MEC capacity varies, DTD3 outperforms the other approaches across total cost, QoS, and communication success rate.

B. Scenario with Varying Capacity of MEC

C. Convergence Analysis

Under the stated Wi-Fi scenario, DTD3 converges around 400 episodes, and the proposed diffusion-based offloading and Hungarian-algorithm allocation scheme is reported to improve system performance. The authors identify multi-agent mobile edge systems as a possible future extension.

C. Convergence Analysis

  • DTD3 converges around 400 episodes with optimal convergence performance in the tested setting of three communication STAs, 10 computing STAs, and 10GHz MEC resources.The authors attribute reduced convergence time and training cost to the generative diffusion model working with the RL framework.Source: C. Convergence Analysis · p. 5, Figure 4 · p. 6

V. CONCLUSION

  • The scheme combines generative AI and DRL for multi-STA, single-AP 802.11ax Wi-Fi networks, with diffusion models addressing sparse samples and Hungarian-algorithm communication allocation.Source: §V · pp. 5–6
  • The proposed approach reduces overall system energy consumption and latency, enhances QoS, and ensures communication success rate.Source: §V · p. 6
  • The authors propose extending the framework to multi-agent mobile edge systems using distributed RL execution to improve scalability and robustness.Source: §V · p. 6
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