Learning Algorithms for Scheduling in Wireless Networks with Unknown Channel Statistics

Stahlbuhk, Thomas; Shrader, Brooke; Modiano, Eytan

doi:10.1145/3209582.3209586

Cited by 17 publications

(14 citation statements)

References 23 publications

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“…The authors demonstrate that exploiting additional observations significantly improve the system performance. Similarly, [36] deals with scheduling transmissions in presence of unknown channel statistics. The proposed algorithm learns the channels' transmission rates while simultaneously exploiting previous observations to obtain higher throughput.…”

Section: B Experimental Resultsmentioning

confidence: 99%

LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

Zanzi

Sciancalepore²,

García‐Saavedra³

et al. 2021

IEEE Trans. Wireless Commun.

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Network Slicing is expected to become a game changer in the upcoming 5G networks and beyond, enlarging the telecom business ecosystem through still-unexplored vertical industry profits. This implies that heterogeneous service level agreements (SLAs) must be guaranteed per slice given the multitude of predefined requirements. In this paper, we pioneer a novel radio slicing orchestration solution that simultaneously provides latency and throughput guarantees in a multi-tenancy environment. Leveraging on a solid mathematical framework, we exploit the exploration-vsexploitation paradigm by means of a multi-armed-bandit-based (MAB) orchestrator, LACO, that makes adaptive resource slicing decisions with no prior knowledge on the traffic demand or channel quality statistics. As opposed to traditional MAB methods that are blind to the underlying system, LACO relies on system structure information to expedite decisions. After a preliminary simulations campaign empirically proving the validness of our solution, we provide a robust implementation of LACO using offthe-shelf equipment to fully emulate realistic network conditions: near-optimal results within affordable computational time are measured when LACO is in place.

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Section: B Experimental Resultsmentioning

confidence: 99%

LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

Zanzi

Sciancalepore²,

García‐Saavedra³

et al. 2021

IEEE Trans. Wireless Commun.

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“…Slightly abusing the notation, we also denote the set of links that is connected to v by N (v). The primary interference model can represent Bluetooth or FH-CDMA networks as well as capture the essential feature of wireless interference [2], [19], and has been adopted in many studies on wireless scheduling, e.g., see [2]- [7] for more detailed description. Time is slotted, which can be achieved by being equipped with high accuracy GPS.…”

Section: System Modelmentioning

confidence: 99%

“…In this work, we consider the scheduling problem, where link rates and statistics are unknown a priori. This occurs when new applications try to operate efficiently under uncertainty caused by wireless fading, interference, limited feedback, measurement error, system dynamics, etc [17]- [19]. We assume that an instance link rate is revealed when it is accessed/scheduled, and it is drawn from an unknown static distribution.…”

Section: Introductionmentioning

confidence: 99%

“…Although these aforementioned learning algorithms are amenable to distributed implementation, they are limited to single-hop networks, such as wireless access networks, and to saturated traffic scenarios (i.e., links always have packets to send), and thus cannot accordingly respond to packet arrival dynamics. Recently, Stahlbuhk et al [19] has developed a joint learning and scheduling scheme that provides provable efficiency under packet arrival dynamics, by incorporating GMM scheduling algorithm and UCB-based learning algorithm. Albeit interesting, it achieves only 1/2 of the capacity region and has linear complexity, which makes it less attractive for large-size networks.…”

Section: Introductionmentioning

confidence: 99%

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A learning-based distributed algorithm for scheduling in multi-hop wireless networks

2022

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We address the joint problem of learning and scheduling in multi-hop wireless network without a prior knowledge on link rates. Previous scheduling algorithms need the link rate information, and learning algorithms often require a centralized entity and polynomial complexity. These become a major obstacle to develop an efficient learning-based distributed scheme for resource allocation in large-scale multi-hop networks. In this work, by incorporating with learning algorithm, we develop provably efficient scheduling scheme under packet arrival dynamics without a priori link rate information. We extend the results to distributed implementation and evaluation their performance through simulations.

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“…The resource allocation is to be optimized by a convolutional neural network using channel information. A similar problem has been explored in [18] that use Upper Confidence Bound learning for Greedy Maximal Matching (GMM) when the channel statistics are unknown. Since the subchannel and power allocation problem is a non-convex combinatorial problem, the optimal solution of the subchannel and power allocation problem requires an exhaustive search over all possible combinations of subchannels and power levels.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach to Achieving Energy Efficiency in Relay-Assisted LTE-A Downlink System

Hassan

Ahmed

Ahmad

et al. 2019

Sensors

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In recent years, Energy Efficiency (EE) has become a critical design metric for cellular systems. In order to achieve EE, a fine balance between throughput and fairness must also be ensured. To this end, in this paper we have presented various resource block (RB) allocation schemes in relay-assisted Long Term Evolution-Advanced (LTE-A) networks. Driven by equal power and Bisection-based Power Allocation (BOPA) algorithm, the Maximum Throughput (MT) and an alternating MT and proportional fairness (PF)-based SAMM (abbreviated with Authors’ names) RB allocation scheme is presented for a single relay. In the case of multiple relays, the dependency of RB and power allocation on relay deployment and users’ association is first addressed through a k-mean clustering approach. Secondly, to reduce the computational cost of RB and power allocation, a two-step neural network (NN) process (SAMM NN) is presented that uses SAMM-based unsupervised learning for RB allocation and BOPA-based supervised learning for power allocation. The results for all the schemes are compared in terms of EE and user throughput. For a single relay, SAMM BOPA offers the best EE, whereas SAMM equal power provides the best fairness. In the case of multiple relays, the results indicate SAMM NN achieves better EE compared to SAMM equal power and BOPA, and it also achieves better throughput fairness compared to MT equal power and MT BOPA.

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Learning Algorithms for Scheduling in Wireless Networks with Unknown Channel Statistics

Cited by 17 publications

References 23 publications

LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

LACO: A Latency-Driven Network Slicing Orchestration in Beyond-5G Networks

A learning-based distributed algorithm for scheduling in multi-hop wireless networks

A Machine Learning Approach to Achieving Energy Efficiency in Relay-Assisted LTE-A Downlink System

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