Optimizing Wireless Discontinuous Reception via MAC Signaling Learning

• URL: http://arxiv.org/abs/2406.13834v1
• Date: Wed, 19 Jun 2024 20:55:12 GMT
• Title: Optimizing Wireless Discontinuous Reception via MAC Signaling Learning
• Authors: Adriano Pastore, Adrián Agustín de Dios, Álvaro Valcarce,
• Abstract summary: We present a Reinforcement Learning approach to the problem of controlling the Discontinuous Reception (DRX) policy from a Base Transceiver Station (BTS) in a cellular network. We do so by means of optimally timing the transmission of fast Layer-2 signaling messages as specified in 5G New Radio. Our simulation results show that our proposed technique strikes an improved trade-off between latency and energy savings.
• Score: 3.4082496470541312
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present a Reinforcement Learning (RL) approach to the problem of controlling the Discontinuous Reception (DRX) policy from a Base Transceiver Station (BTS) in a cellular network. We do so by means of optimally timing the transmission of fast Layer-2 signaling messages (a.k.a. Medium Access Layer (MAC) Control Elements (CEs) as specified in 5G New Radio). Unlike more conventional approaches to DRX optimization, which rely on fine-tuning the values of DRX timers, we assess the gains that can be obtained solely by means of this MAC CE signalling. For the simulation part, we concentrate on traffic types typically encountered in Extended Reality (XR) applications, where the need for battery drain minimization and overheating mitigation are particularly pressing. Both 3GPP 5G New Radio (5G NR) compliant and non-compliant ("beyond 5G") MAC CEs are considered. Our simulation results show that our proposed technique strikes an improved trade-off between latency and energy savings as compared to conventional timer-based approaches that are characteristic of most current implementations. Specifically, our RL-based policy can nearly halve the active time for a single User Equipment (UE) with respect to a na\"ive MAC CE transmission policy, and still achieve near 20% active time reduction for 9 simultaneously served UEs.

Related papers

• STAR-RIS-assisted Collaborative Beamforming for Low-altitude Wireless Networks [58.13757830013997]
  Wireless networks based on uncrewed aerial vehicles (UAVs) offer high mobility, flexibility, and coverage for urban communications.<n>They face severe signal attenuation in dense environments due to obstructions.<n>To address this critical issue, we consider introducing collaborative beam of UAVs and omni-directional re-altitude beamforming.
  arXiv  Detail & Related papers  (2025-10-25T01:28:37Z)
• Silentflow: Leveraging Trusted Execution for Resource-Limited MPC via Hardware-Algorithm Co-design [6.998260344481881]
  We introduce Silentflow, a protocol that eliminates communication in COT generation.<n>We balance end-to-end latency and resource demands, achieving up to 39.51x speedup over state-of-the-art protocols.
  arXiv  Detail & Related papers  (2025-08-18T21:00:10Z)
• Deep Active Speech Cancellation with Mamba-Masking Network [62.73250985838971]
  We present a novel deep learning network for Active Speech Cancellation (ASC)<n>The proposed Mamba-Masking architecture introduces a masking mechanism that directly interacts with the encoded reference signal.<n> Experimental results demonstrate substantial performance gains, achieving up to 7.2dB improvement in ANC scenarios and 6.2dB in ASC.
  arXiv  Detail & Related papers  (2025-02-03T09:22:26Z)
• Dynamic Spectrum Access for Ambient Backscatter Communication-assisted D2D Systems with Quantum Reinforcement Learning [68.63990729719369]
  The wireless spectrum is becoming scarce, resulting in low spectral efficiency for D2D communications. This paper aims to integrate the ambient backscatter communication technology into D2D devices to allow them to backscatter ambient RF signals. We develop a novel quantum reinforcement learning (RL) algorithm that can achieve a faster convergence rate with fewer training parameters.
  arXiv  Detail & Related papers  (2024-10-23T15:36:43Z)
• OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications [53.398544571833135]
  This work proposes a spectrally efficient irregular Sinc (irSinc) shaping technique, revisiting the traditional Sinc back to 1924. irSinc yields a signal with increased spectral efficiency without sacrificing error performance. Our signal achieves faster data transmission within the same spectral bandwidth through 5G standard signal configuration.
  arXiv  Detail & Related papers  (2024-06-07T09:20:30Z)
• Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks [52.96907334080273]
  As the network scale increases, the energy efficiency of LoRa networks decreases sharply due to severe packet collisions. We propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms.
  arXiv  Detail & Related papers  (2023-09-16T11:37:23Z)
• Communication-Efficient Orchestrations for URLLC Service via Hierarchical Reinforcement Learning [14.604814002402588]
  We propose a multi-agent Hierarchical RL (HRL) framework that enables the implementation of multi-level policies with different control loop timescales. On a use case from the prior art, with our HRL framework, we optimized the maximum number of retransmissions and transmission power of industrial devices.
  arXiv  Detail & Related papers  (2023-07-25T11:23:38Z)
• Federated Meta-Learning for Traffic Steering in O-RAN [1.400970992993106]
  We propose an algorithm for RAT allocation based on federated meta-learning (FML) We have designed a simulation environment which contains LTE and 5G NR service technologies.
  arXiv  Detail & Related papers  (2022-09-13T10:39:41Z)
• Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach [79.59560136273917]
  limited communication resources, bandwidth and energy, and data heterogeneity across devices are main bottlenecks for federated learning (FL) We first devise a novel FL framework with partial model aggregation (PMA) The proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets.
  arXiv  Detail & Related papers  (2022-04-20T19:09:52Z)
• Collaborative Intelligent Reflecting Surface Networks with Multi-Agent Reinforcement Learning [63.83425382922157]
  Intelligent reflecting surface (IRS) is envisioned to be widely applied in future wireless networks. In this paper, we investigate a multi-user communication system assisted by cooperative IRS devices with the capability of energy harvesting.
  arXiv  Detail & Related papers  (2022-03-26T20:37:14Z)
• Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications [64.1076645382049]
  Combination of energy harvesting (EH), cognitive radio (CR), and non-orthogonal multiple access (NOMA) is a promising solution to improve energy efficiency. In this paper, we study the spectrum, energy, and time resource management for deterministic-CR-NOMA IoT systems.
  arXiv  Detail & Related papers  (2021-09-17T08:55:48Z)
• End-to-End Learning of OFDM Waveforms with PAPR and ACLR Constraints [15.423422040627331]
  We propose to use a neural network (NN) at the transmitter to learn a high-dimensional modulation scheme allowing to control the PAPR and adjacent channel leakage ratio (ACLR) The two NNs operate on top of OFDM, and are jointly optimized in and end-to-end manner using a training algorithm that enforces constraints on the PAPR and ACLR.
  arXiv  Detail & Related papers  (2021-06-30T13:09:30Z)
• A Low-Delay MAC for IoT Applications: Decentralized Optimal Scheduling of Queues without Explicit State Information Sharing [8.039591168227345]
  We consider a system of several collocated nodes sharing a time slotted wireless channel. We seek a MAC (medium access control) that (i) provides low mean delay, (ii) has distributed control, and (iii) does not require explicit exchange of state information or control signals. The design of such MAC protocols must keep in mind the need for contention access at light traffic, and scheduled access in heavy traffic.
  arXiv  Detail & Related papers  (2021-05-24T11:44:08Z)
• Multi-hop RIS-Empowered Terahertz Communications: A DRL-based Hybrid Beamforming Design [39.21220050099642]
  Wireless communication in the TeraHertz band (0.1--10 THz) is envisioned as one of the key enabling technologies for the future sixth generation (6G) wireless communication systems. We propose a novel hybrid beamforming scheme for the multi-hop RIS-assisted communication networks to improve the coverage range at THz-band frequencies.
  arXiv  Detail & Related papers  (2021-01-22T14:56:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.