Capacity Analysis and Call Admission Control in Distributed Cognitive Radio Networks

Gunawardena, Subodha; Zhuang, Weihua

doi:10.1109/twc.2011.072011.110406

Cited by 22 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hybrid CRNs Spectrum Sensing [143], [145], [148], [234]- [236] Various Fading Conditions [115], [128], [129] Resource Allocation [102], [110], [116], [124], [137], [153], [172], [237] Modulation Techniques [118], [120], [203] Relaying In CRNs [142], [158], [195] Spectrum Sensing [157], [198] Interference Constraints [155], [238] CRNs with MISO Systems [239] Channel Access Approaches [240], [241] Resource Allocation [37], [44], [58], [60], [103], [107], [177], [181], [202], [225], [242]- [245] Spectrum Sensing [15] Interference Constraints [140] QoS Provisioning In M2M Communications [92] Voice Capacity [246], [247] Resource Allocation…”

Section: Interweave Crnsmentioning

confidence: 99%

“…ported by interweave CRNs. EC concept in interweave CRNs has also been used to analyse the statistical QoS provisioning with delay constraints for different real-time, QoS-aware, and bandwidth hungry applications such as multimedia applications [92], [246], [247]. Energy-detection based spectrum sensing [15], interference constraints [140], and resource allocation scheme [199], [248], have also been analysed with achievable EC in interweave CRNs.…”

Section: Hybrid Crnsmentioning

confidence: 99%

See 1 more Smart Citation

Effective Capacity in Wireless Networks: A Comprehensive Survey

Amjad

Musavian

Rehmani

2019

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physicallayer channel models, however, do not explicitly consider qualityof-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.

show abstract

Section: Interweave Crnsmentioning

confidence: 99%

Section: Hybrid Crnsmentioning

confidence: 99%

Effective Capacity in Wireless Networks: A Comprehensive Survey

Amjad

Musavian

Rehmani

2019

IEEE Commun. Surv. Tutorials

View full text Add to dashboard Cite

show abstract

“…The probability of state transition from state i (∈ {0, 1}) to state j (∈ {0, 1}) is denoted by S i, j . This is a widely used method to model the behavior of PUs [8]- [10] due to its simplicity. Therefore, the durations of channel availability and busy (D) periods are geometrically distributed with mean values T on (=1/S 1,0 ) and T o f f (=1/S 0,1 ), respectively.…”

Section: A Channel Modelmentioning

confidence: 99%

“…Research studies on voice [8]- [10] and video [11] transmission over CRNs focus on capacity/delay analysis and channel access. So far, little attention is paid to the performance analysis of request-response type services such as web browsing Manuscript received April 15, 2012; revised September 1, 2012; accepted October 22, 2012.…”

mentioning

confidence: 99%

Service Response Time of Elastic Data Traffic in Cognitive Radio Networks

Gunawardena

Zhuang

2013

IEEE J. Select. Areas Commun.

Self Cite

View full text Add to dashboard Cite

Abstract-Quality of service (QoS) support over cognitive radio networks (CRNs) is challenging due to the random spectrum availability. Elastic data traffic is a popular service whose service response time is an important QoS parameter. We analyze the mean response time of elastic data traffic service operating over a single channel time-slotted centralized CRN under three main service disciplines, namely, shortest processing time without preemption (SPTNP), shortest processing time with preemption, and shortest remaining processing time, in comparison with the processor sharing (PS) service discipline. It is shown that the SPTNP is a better choice over the PS service discipline when the traffic load is high, and that the preemption reduces the mean response time when the data file size (service time requirement) follows a heavy tailed distribution. The response time analysis can be used for call admission control to ensure service satisfaction.Index Terms-Cognitive radio network, elastic data traffic, quality-of-service, response time, shortest processor time, shortest remaining processor time, processor sharing.I. I The spectrum underutilization [2] is becoming a major setback for the development of next generation wireless networks. Among proposed solutions, the concept of cognitive radio networks (CRNs) [3][4] has become a popular choice due to its flexibility and adaptability to use in any available frequency band. It has been well accepted within the wireless communications research community to explore the underutilized portions of the radio spectrum using new-generation smart programmable radios, without harmfully interfering with the licensed primary users (PUs). Early research studies on CRNs mainly focused on physical layer aspects such as spectrum sensing [5] and power/rate controlling [6], and link layer aspects such as channel access coordination [7]. Further, the unpredictable nature of the opportunistic channels limits the research work on CRNs to best effort services without any strict quality of service (QoS) requirements. As the demand for wireless multimedia services keeps on increasing, supporting QoS-aware services over CRNs is essential.Research over CRNs. The impact of primary user activities on traffic congestion and the economic interaction between secondary user (SU) and primary network operators are studied in [12] and [13], respectively, when the SUs are data users. This type of services does not require strict QoS as in conversational or streaming services, but has a moderate service requirement in the form of response time. The response time of a service request (file) is defined as the time elapsed from the instant it is placed by a user to the instant that the service (file transmission) is completed. A. Related WorkMost of the resource allocation/scheduling works in CRNs mainly focus on throughput optimization/fairness, and they do not deal with any specific data file length distributions or response time as a performance metric. A performance analysis of elastic traff...

show abstract

“…By using cognitive radio, CMN is capable of utilizing spectrum resource from primary spectrum such as TV-band spectrum. However, to protect the transmission of primary users, a cognitive radio shall switch among primary channels if a primary user returns to the channel it uses, leading to time-varying capacity of a cognitive radio-based network [4]. As such, quality-of-service (QoS) provisioning for such a network is a challenging issue.…”

Section: Introductionmentioning

confidence: 99%

Genetic Algorithm based Resource Management for Cognitive Mesh Networks with Real-time and Non-real-time Services

2015

KSII TIIS

View full text Add to dashboard Cite

Quality-of-service (QoS) provisioning for a cognitive mesh network (CMN) with heterogeneous services has become a challenging area of research in recent days. Considering both real-time (RT) and non-real-time (NRT) traffic in a multihop CMN, [1] studied cross-layer resource management, including joint access control, route selection, and resource allocation. Due to the complexity of the formulated resource allocation problems, which are mixed-integer non-linear programming, a low-complexity yet efficient algorithm was proposed there to approximately solve the formulated optimization problems. In contrast, in this work, we present an application of genetic algorithm (GA) to re-address the hard resource allocation problems studied in [1]. Novel initialization, selection, crossover, and mutation operations are designed such that solutions with enough randomness can be generated and converge with as less number of attempts as possible, thus improving the efficiency of the algorithm effectively. Simulation results show the effectiveness of the newly proposed GA-based algorithm. Furthermore, by comparing the performance of the newly proposed algorithm with the one proposed in [1], more insights have been obtained in terms of the tradeoff among QoS provisioning for RT traffic, throughput maximization for NRT traffic, and time complexity of an algorithm for resource allocation in a multihop network such as CMN.

show abstract

Capacity Analysis and Call Admission Control in Distributed Cognitive Radio Networks

Cited by 22 publications

References 16 publications

Effective Capacity in Wireless Networks: A Comprehensive Survey

Effective Capacity in Wireless Networks: A Comprehensive Survey

Service Response Time of Elastic Data Traffic in Cognitive Radio Networks

Genetic Algorithm based Resource Management for Cognitive Mesh Networks with Real-time and Non-real-time Services

Contact Info

Product

Resources

About