Resource allocation for heterogeneous services in multiuser cognitive radio networks

Xu, Ding; Zhang, Feng; Zhang, Ping

doi:10.1002/dac.2462

Cited by 14 publications

(8 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The computational complexity of Algorithm 1 is analysed briefly as follows: for fixed and , calculating and p requires O.G 2 N/ and O.G/ operations, respectively. It has been shown in [13,14] that the subgradient method converges in a small number 4 of iterations as long as appropriate step sizes are chosen, which we will also show in our simulation results in Section 5. Thus, the total complexity of Algorithm 1 is O.G 2 N4/.…”

Section: Group Outage Probability Minimisationsupporting

confidence: 58%

See 1 more Smart Citation

Resource allocation for outage probability minimisation in cognitive radio multicast networks

2014

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

In this paper, we consider a downlink secondary cognitive radio multicast network that consists of a cognitive base station and a set of secondary multicast groups sharing spectrum with a primary network. To protect the transmissions of the primary network, an interference power constraint is applied to restrict the transmit power of the cognitive base station. The problem of joint channel and power allocation for the cognitive radio multicast network is studied. The objective is to minimise the weighted aggregate outage probability for a given target rate for the cognitive radio multicast network. Specifically, two types of outage probabilities, namely, group outage probability and individual outage probability, are considered. For each type of outage probability, the optimal joint channel and power allocation algorithm is proposed based on Lagrange dual optimisation. Simulation results are illustrated to verify the effectiveness of the proposed algorithms. It is shown that the proposed algorithms significantly improve the performance of the cognitive radio multicast network in terms of outage probability. Copyright © 2014 John Wiley & Sons, Ltd.

show abstract

Section: Group Outage Probability Minimisationsupporting

confidence: 58%

“…where D 0 g . , / is given by (14). To solve (15), the exhaustive search method can be employed to determine the optimal .…”

Section: Group Outage Probability Minimisationmentioning

confidence: 99%

Resource allocation for outage probability minimisation in cognitive radio multicast networks

2014

Trans Emerging Tel Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…Optimal RA solutions for heterogeneous CRN were investigated in [6,7] and the works were further strengthened in [8] to include QoS provisioning. The concept of cooperative diversity was introduced in [9] to address the problem of interference mitigation, thereby achieving even better results.…”

Section: Related Literaturementioning

confidence: 99%

Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

Awoyemi

Maharaj

Alfa

2017

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Resources available for operation in cognitive radio networks (CRN) are generally limited, making it imperative for efficient resource allocation (RA) models to be designed for them. However, in most RA designs, a significant limiting factor to the RA's productivity has hitherto been mostly ignored, the fact that different users or user categories do have different delay tolerance profiles. To address this, in this paper, an appropriate RA model for heterogeneous CRN with delay considerations is developed and analysed. In the model, the demands of users are first categorised and then, based on the distances of users from the controlling secondary user base station and with the assumption that the users are mobile, the user demands are placed in different queues having different service capacities and the resulting network is analysed using queueing theory. Furthermore, to achieve optimality in the RA process, an important concept is introduced whereby some demands from one queue are moved to another queue where they have a better chance of enhanced service, thereby giving rise to the possibility of an improvement in the overall performance of the network. The performance results obtained from the analysis, particularly the blocking probability and network throughput, show that the queueing model incorporated into the RA process can help in achieving optimality for the heterogeneous CRN with buffered data.

show abstract

“…In overlay CR networks, the SUs acquire transmission opportunities by improving the PUs' transmission. This paper focuses on the underlay CR networks, in which various problems such as resource allocation [4][5][6][7] have been studied widely in existing literature.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation in underlay cognitive radio networks with full‐duplex cognitive base station

2017

Int J Communication

Self Cite

View full text Add to dashboard Cite

This paper considers an underlay cognitive radio network with a full-duplex cognitive base station and sets of half-duplex downlink and uplink secondary users, sharing multiple channels with the primary user. The resource allocation problem to maximize the sum rate of all the secondary users is investigated subject to the transmit power constraints and the interference power constraint. The optimization problem is highly nonconvex, and we jointly use the dual optimization method and the successive convex approximation method to derive resource allocation algorithms to solve the problem. Extensive simulations are shown to verify the performance of the resource allocation algorithms. It is shown that the proposed algorithms achieve much higher sum rate than that of the optimal half-duplex algorithms and the reference full-duplex algorithms. KEYWORDScognitive radio, full-duplex, resource allocation, successive convex approximation Int J Commun Syst. 2017;30:e3321.wileyonlinelibrary.com/journal/dac

show abstract

Resource allocation for heterogeneous services in multiuser cognitive radio networks

Cited by 14 publications

References 24 publications

Resource allocation for outage probability minimisation in cognitive radio multicast networks

Resource allocation for outage probability minimisation in cognitive radio multicast networks

Resource Allocation in Heterogeneous Buffered Cognitive Radio Networks

Resource allocation in underlay cognitive radio networks with full‐duplex cognitive base station

Contact Info

Product

Resources

About