Queue and Channel-Based Aloha Algorithm in Multichannel Wireless Networks

Malachi, Dean; Cohen, Kobi

doi:10.1109/lwc.2020.2989259

“…Another set of related work on multi-user channel allocation has approached it from the angle of game theoretic and congestion control ( [22]- [30], [35], [36] and references therein), hidden channel states [37], and graph coloring ( [38]- [41] and references therein). The game theoretic aspects of the problem have been investigated from both noncooperative (i.e., each user aims at maximizing an individual utility) [23], [24], [29], [30], [32], [42], and cooperative (i.e., each user aims at maximizing a system-wide global utility) [21], [22], [35], [43], [44] settings.…”

Section: B Related Workmentioning

confidence: 99%

Distributed Learning Over Markovian Fading Channels for Stable Spectrum Access

Gafni

¹

,

Cohen

²

2022

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We consider the problem of multi-user spectrum access in wireless networks. The bandwidth is divided into K orthogonal channels, and M users aim to access the spectrum. Each user chooses a single channel for transmission at each time slot. The state of each channel is modeled by a restless unknown Markovian process. Previous studies have analyzed a special case of this setting, in which each channel yields the same expected rate for all users. By contrast, we consider a more general and practical model, where each channel yields a different expected rate for each user. This model adds a significant challenge of how to efficiently learn a channel allocation in a distributed manner to yield a global system-wide objective. We adopt the stable matching rate as the system objective, which is known to yield strong performance in multichannel wireless networks, and develop a novel Distributed Stable Strategy Learning (DSSL) algorithm to achieve the objective. We prove theoretically that DSSL converges to the stable matching allocation, and the regret, defined as the loss in total rate with respect to the stable matching solution, has a logarithmic order with time. Simulation results demonstrate the strong performance of the DSSL algorithm. We show that DSSL has significant advantages over the state-of-the-art methods in terms of implementation complexity, convergence speed, and network churn conditions.INDEX TERMS Wireless networks, spectrum access, online learning.

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“…Another set of related work on multi-user channel allocation has approached it from the angle of game theoretic and congestion control ( [17]- [27] and references therein), hidden channel states [28], and graph coloring ( [29]- [32] and references therein). The game theoretic aspects of the problem have been investigated from both non-cooperative (i.e., each user aims at maximizing an individual utility) [18], [19], [24], [25], [33], and cooperative (i.e., each user aims at maximizing a system-wide global utility) [17], [26], [34], [35] settings.…”

Section: B Related Workmentioning

confidence: 99%

Distributed Learning over Markovian Fading Channels for Stable Spectrum Access

Gafni¹,

Cohen²

2021

Preprint

Self Cite

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We consider the problem of multi-user spectrum access in wireless networks. The bandwidth is divided into K orthogonal channels, and M users aim to access the spectrum. Each user chooses a single channel for transmission at each time slot. The state of each channel is modeled by a restless unknown Markovian process. Previous studies have analyzed a special case of this setting, in which each channel yields the same expected rate for all users. By contrast, we consider a more general and practical model, where each channel yields a different expected rate for each user. This model adds a significant challenge of how to efficiently learn a channel allocation in a distributed manner to yield a global system-wide objective. We adopt the stable matching utility as the system objective, which is known to yield strong performance in multichannel wireless networks, and develop a novel Distributed Stable Strategy Learning (DSSL) algorithm to achieve the objective. We prove theoretically that DSSL converges to the stable matching allocation, and the regret, defined as the loss in total rate with respect to the stable matching solution, has a logarithmic order with time. Finally, simulation results demonstrate the strong performance of the DSSL algorithm. I. INTRODUCTIONWe consider the spectrum access problem, where a shared bandwidth is divided into K orthogonal channels (i.e., subbands), and M users want to access the spectrum, where K ≥ M . Each channel is modeled by a Finite-State Markovian Channel (FSMC), which is independent and non-identically distributed across channels. The FSMC is a tractable model widely used to capture the time-varying behavior of a radio communication channel [2], [3]. It is often employed to model radio channel dynamics due to primary user occupancy effects in hierarchical cognitive radio networks (where the M secondary (unlicensed) users are cognitive in terms of learning and adapting good access strategies), or the external interference effects in the open sharing model among M users in the wireless network (e.g., ISM band) [4], [5]. At each time step, each user experiences a different transmission rate over each channel depending on its FSMC distribution, where the FSMC parameters (i.e., the transition probabilities that govern the Markov chain) are unknown. At each time step, each user is allowed to choose one channel to access, and observe the instantaneous channel state. If two users or more access the same channel at the same time, a collision occurs and the achievable rate is zero.We adopt the stable matching utility (see Section II for details) as the system objective, which is known to yield strong Tomer Gafni and Kobi Cohen are with the School of Electrical and Computer Engineering,

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“…DCA enhances the efficiency and flexibility of wireless networks, ensuring efficient channel utilization and mitigating the impact of interference, leading to improved overall performance and quality of service (QoS) . Recent studies on this topic, which involves learning the environment and channel allocation algorithms, have employed frameworks such as multi-armed bandits (MAB) [1]- [9], stable matching [10], [11], game theoretic optimization and congestion control [1], [8], [12]- [21], and, more recently, deep reinforcement learning (DRL) for multiuser scenarios [22]- [36]. The latter was initially explored in our earlier work (Naparstek and Cohen [22], [23]) within a multi-agent framework, following single-agent DRL research in [37], paving the way for a significant amount of subsequent research in the wireless communications community.…”

Section: Introductionmentioning

confidence: 99%

Multi-Agent Deep Reinforcement Learning for Interference-Aware Channel Allocation in Non-Terrestrial Networks

Cho

¹

,

Yang

²

,

Oh

³

et al. 2023

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We consider the problem of dynamic channel allocation (DCA) in cognitive communication networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR) measure under a specified target quality of service (QoS)-SINR for each network. The shared bandwidth is partitioned into K channels with frequency separation. In contrast to the majority of existing studies that assume perfect orthogonality or a oneto-one user-channel allocation mapping, this paper focuses on real-world systems experiencing inter-carrier interference (ICI) and channel reuse by multiple large-scale networks. This realistic scenario significantly increases the problem dimension, rendering existing algorithms inefficient. We propose a novel multi-agent reinforcement learning (RL) framework for distributed DCA, named Channel Allocation RL To Overlapped Networks (CARL-TON). The CARLTON framework is based on the Centralized Training with Decentralized Execution (CTDE) paradigm, utilizing the DeepMellow value-based RL algorithm. To ensure robust performance in the interference-laden environment we address, CARLTON employs a low-dimensional representation of observations, generating a QoS-type measure while maximizing a global SINR measure and ensuring the target QoS-SINR for each network. Our results demonstrate exceptional performance and robust generalization, showcasing superior efficiency compared to alternative state-of-the-art methods, while achieving a marginally diminished performance relative to a fully centralized approach.

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Queue and Channel-Based Aloha Algorithm in Multichannel Wireless Networks

Cited by 3 publications

References 19 publications

Distributed Learning Over Markovian Fading Channels for Stable Spectrum Access

Distributed Learning Over Markovian Fading Channels for Stable Spectrum Access

Distributed Learning over Markovian Fading Channels for Stable Spectrum Access

Multi-Agent Deep Reinforcement Learning for Interference-Aware Channel Allocation in Non-Terrestrial Networks

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