Resource Allocation with Load Balancing for Cognitive Radio Networks

Wang, Huahui; Ren, Jian; Li, Tongtong

doi:10.1109/glocom.2010.5683966

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…Authors in [26], for instance, also point out the positive impact of location awareness in CRNs. Furthermore, as also pointed out by [27], [28], SUs can estimate channel gains between SUs and PUs (h SU,PU t in our case) by employing sensors near all receiving points and make them available at the central controller. Values for h SU,CBS t can also be estimated in a similar way.…”

Section: Problem Formulations and Proposed Solutionsmentioning

confidence: 81%

A Fair Scheduling Model for Centralized Cognitive Radio Networks

Gözüpek¹,

Alagöz²

2013

Preprint

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We formulate throughput maximizing, max-min fair, weighted max-min fair, and proportionally fair scheduling problems for cognitive radio networks managed by a centralized cognitive base station. We propose a very general scheduling model accomplishing goals such as making frequency, time slot, and data rate allocation to secondary users with possibly multiple antennas, in a heterogenous multi-channel and multi-user scenario. Moreover, our schedulers ensure that reliable communication between the cognitive base station and secondary users are maintained, no collisions occur among secondary users, and primary users in the service area of the cognitive base station are not disturbed. Two distinctive features of our fair schedulers are that they provide joint temporal and throughput fairness, and take throughput values experienced by secondary users in the recent past, referred to as window size, into account and use this information in the current scheduling decision. We also propose a heuristic algorithm for our fair schedulers and demonstrate through simulations that our proposed heuristic yields very close solutions to the values obtained from the optimization softwares. Furthermore, we make extensive simulations to evaluate our schedulers' performance in terms of both total throughput and fairness for varying number of secondary users, frequencies, antennas, and window size.

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Section: Problem Formulations and Proposed Solutionsmentioning

confidence: 81%

A Fair Scheduling Model for Centralized Cognitive Radio Networks

Gözüpek¹,

Alagöz²

2013

Preprint

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“…It depends on the CH loss and CH error. Error and loss are mainly caused by signal to interference and noise ratio (SINR) of the CH [24,25]. A dependability value in the range of 0 to 1 is assumed.…”

Section: Dependabilitymentioning

confidence: 99%

Intelligent dynamic spectrum allocation with bandwidth flexibility in cognitive radio network

Veeramakali¹,

Jayashri²,

Prabu

2017

Cluster Comput

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Cognitive radio (CR) is a popular wireless technology for efficient utilization of spectrum. CR network senses the unused frequency bands of the primary users (PUs) and assign them to the unlicensed secondary users (SUs). One of the major challenges in cognitive network is the allocation of available spectrum which indirectly contributes to the efficient spectrum utilization. A novel intelligent dynamic spectrum allocation with bandwidth flexibility is presented in this paper. Fuzzy inference system has been used to evaluate and rate the channel (CH) quality and the SU. The quality based spectrum allocation has been carried out. As the primary CH bandwidth differ from the SU and in the case, the SU bandwidth is lower to PU, accommodation of more than one secondary CH into primary is possible. To utilize this facility, bandwidth flexibility is incorporated in the intelligent spectrum allocation. The intelligent spectrum allocation techniques are compared with the sequence based method of spectrum allocation and priority based allocation. The quality measures utilized for comparison are service rate, average packet loss and average delay. On these measures intelligent dynamic bandwidth flexible spectrum allocation method is found to be better, compared to other three methods.

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“…Spectrum allocation for multiple channels can be done by using single-channel techniques independently for each channel and then selecting one of the available channels based on some criteria (see §3.6). For example, H. Wang et al [40] picks a channel that maximizes the aggregate data rate of SUs, and X. Li et al [28] picks a channel that allows for minimum transmission power for a desired SU data rate. Other works have addressed spectrum allocation with other optimization objectives.…”

Section: Related Workmentioning

confidence: 99%

DeepAlloc: CNN-Based Approach to Efficient Spectrum Allocation in Shared Spectrum Systems

Ghaderibaneh¹,

Zhan²,

Gupta³

2022

Preprint

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Shared spectrum systems facilitate spectrum allocation to unlicensed users without harming the licensed users; they offer great promise in optimizing spectrum utility, but their management (in particular, efficient spectrum allocation to unlicensed users) is challenging. A significant shortcoming of current allocation methods is that they are either done very conservatively to ensure correctness, or are based on imperfect propagation models and/or spectrum sensing with poor spatial granularity. This leads to poor spectrum utilization, the fundamental objective of shared spectrum systems.To allocate spectrum near-optimally to secondary users in general scenarios, we fundamentally need to have knowledge of the signal path-loss function. In practice, however, even the best known path-loss models have unsatisfactory accuracy, and conducting extensive surveys to gather path-loss values is infeasible. To circumvent this challenge, we propose to learn the spectrum allocation function directly using supervised learning techniques. We particularly address the scenarios when the primary users' information may not be available; for such settings, we make use of a crowdsourced sensing architecture and use the spectrum sensor readings as features. We develop an efficient CNN-based approach (called DeepAlloc) and address various challenges that arise in its application to the learning the spectrum allocation function. Via extensive large-scale simulation and a small testbed, we demonstrate the effectiveness of our developed techniques; in particular, we observe that our approach improves the accuracy of standard learning techniques and prior work by up to 60%.

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Resource Allocation with Load Balancing for Cognitive Radio Networks

Cited by 15 publications

References 13 publications

A Fair Scheduling Model for Centralized Cognitive Radio Networks

A Fair Scheduling Model for Centralized Cognitive Radio Networks

Intelligent dynamic spectrum allocation with bandwidth flexibility in cognitive radio network

DeepAlloc: CNN-Based Approach to Efficient Spectrum Allocation in Shared Spectrum Systems

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