Efficient Power Allocation for Multiuser Cognitive Radio Networks

Cognitive radio has been recently considered as a flexible spectrum usage model to improve the spectrum efficiency. In this paper, we develop a new power allocation approach by geometric programming (GP) to realize the coexistence of a primary user (PU) and multiple cognitive users (CUs) in the same frequency band. The objective is to minimize the total power consumption of CUs while guaranteeing the not only protection of PU but also the quality of service (QoS) of multiple CUs, which is different from previous works. In order to facilitate the centralized computation, a distributed algorithm is developed to adaptively adjust the transmit power of each CU. Numerical results are provided to show the effectiveness of the proposed algorithm in power saving.     

Cognitive radio intelligent-MAC (CR-i-MAC): channel-diverse contention free approach for spectrum management

Cognitive radio was introduced to fill up the imbalance between spectrum scarcity and spectrum underutilization. So to make such an ideology work, a network which can utilize all the available channel in the best efficient manner, without causing any harmful interface to primary user (PU) and maintaining the Quality of Service (QoS) for cognitive user (CU) is required. In both Mesh as well as ad-hoc networks, effective utilization of the white-spaces by the CUs maintaining the QoS for both primary and CU is a challenging task due to the frequent and instant change in their channel status. In this paper an intelligent-MAC (i-MAC) for cognitive radio (CR) using two transceivers based on hybrid approach of combination of cooperative decision and contention-free approach is proposed. Cooperative decision, to overcome hidden node or the case when there is no common channel between the CU’s and contention-free approach, to solve the issues in contention mechanism, where same channel is selected simultaneously by multiple CU’s. Proposed CR-i-MAC permits an effective dynamic spectrum access to CUs without effecting the QoS for PU’s. The simulative performance analysis of proposed CR-i-MAC is tested in various critical cases like multi-channel single-radio and multi-channel multi-radio over different on demand routing protocols like dynamic source routing, ad-hoc on demand distance vector and weighted cumulative expected transmission time using network simulator (NS-2). The performance of the network is measured on the basis of parameters like throughput, delay and interference. The analysis of the simulation results shows that the proposed CR-i-MAC outperforms various other CR MAC’s in terms of both increased throughput and reduced delays thereby making the system stable and efficient.     

Cooperative primary–secondary dynamic spectrum leasing game via decentralized bargaining

Dynamic spectrum leasing (DSL) has been proposed as a solution for better spectrum utilization. Most of the work focused on non-cooperative game to model primary/secondary users interactions in DSL approach. Some others introduced cooperative game just for secondary users (SUs). In this paper, both primary users (PUs) and SUs incentives and level of satisfactions are considered. Nash bargaining is developed with both PUs and SUs as bargainers. A simple pricing approach is introduced which makes the proposed method practically feasible. On one hand, SUs adjust their power regarding to price and tolerable interference which are announced by PU. On the other hand, PU adjusts its tolerable interference to maximize its profit. Simulation results verify the viability of proposed method.     

Primary user supported routing protocol for cognitive radio ad hoc networks in search of higher throughput

A routing protocol for cognitive radio ad hoc networks (CRAHNs), namely, primary user supported routing (PSR) is demonstrated in this research. Unlike existing routing protocols for CRAHN, where routing of cognitive users (CUs) is accomplished within CUs, in this proposed protocol, some of the primary users (PUs) support CUs to communicate, by forwarding CU packets. This service provided by PU is of voluntary nature. However, such assistance shall be provided by the PUs, only when they are idle, thereby, preserving the principles of cognitive radio networks (CRNs), which indicates that the operation of PU should not be disturbed by the CU activities. The proposed work is compared with cognitive ad hoc on-demand distance vector (CAODV) protocol. The performance parameters considered are routing overhead, rate of loss of packets, and e2e packet delay. PSR outperforms CAODV in all these performance parameters. There has been on average 26.25% improvement in routing overhead, 34.12% decrease in loss, and 27.01% improvement in e2e delivery in the proposed PSR.     

Adaptive power control and beam-forming joint optimization in cognitive radio networks

A novel adaptive power control and beam-forming joint optimization algorithm is proposed in cognitive radio(CR) underlay networks,where cognitive network share spectrum with primary network which spectrum is licensed.In this paper,both primary base station(PBS) and cognitive base station(CBS) are all equipped with multi antennas,while each primary user(PU) and cognitive user(CU) has only one antenna.Different from traditional algorithms,an adaptive weight factor generating solution is supplied to different access users(both PUs and CUs) in this paper,and the different priority of users is also considered,because PUs have higher priority,the weight factor of PUs is fixed as constant and signal-to-interference and noise ratio(SINR) threshold is unchanged,while for CUs,it is set adaptively and SINR threshold is also changed accordingly.Using this algorithm,the transmit power is decreased,which relax the strict requirements for power amplifier in communication systems.And moreover,owing to PUS has fixed SINR threshold,the calculated SINR at receiver is nearly unchanged,but for CUs,the SINR is changing with the adaptive weight factor.Under the assurance of quality of service(QoS) of PUs,the solution in this paper can enable CRs access to the CR network according to adaptive SINR threshold,therefore which supplies higher spectrum utilization efficiency.     

认知无线电网络子空间映射频谱共享

针对集中式多用户多天线认知无线电网络,分析了子空间映射能够为认知系统提供的通信机会以及理想信道条件下的空间子信道分配方案。在此基础上,提出了一种基于子空间映射的频谱共享策略。根据认知系统的感知结果,计算可以利用的空间子信道数,通过认知用户接入控制和子空间映射避免或抑制系统间干扰,从而在保证授权用户通信质量的前提下,为认知用户提供通信机会。仿真结果表明,与已有的子空间映射频谱共享方法相比,该策略不仅具有更高的认知系统可达和速率,而且能够为认知系统提供更多通信机会。     

Cooperative Spectrum Prediction in Multi-PU Multi-SU Cognitive Radio Networks

Spectrum sensing is considered as the cornerstone of cognitive radio networks (CRNs). However, sensing the wide-band spectrum results in delays and resource wasting. Spectrum prediction, also known as channel status prediction, has been proposed as a promising approach to overcome these shortcomings. Prediction of the channel occupancy, when feasible, provides adequate means for an SU to determine, with a high probability, when to evacuate a channel it currently occupies in anticipation of the PU’s return. Spectrum prediction has great potential to reduce interference with PU activities and significantly enhance spectral efficiency. In this paper, we propose a novel, coalitional game theory based approach to investigate cooperative spectrum prediction in multi-PU multi-SU CRNs. In this approach, cooperative groups, also referred to as coalitions, are formed through a proposed coalition formation algorithm. The novelty of this work, in comparison to existing cooperative sensing approaches, stems from its focus on the more challenging case of multi-PU CRNs and the use of an efficient coalition formation algorithm, centered on the concept of core, to ensure stability. Theoretical analysis is conducted on the upper bound of the coalition size and the stability of the formed coalition structure. A through simulation study is performed to assess the effectiveness of the proposed approach. The simulation results indicate that cooperative spectrum prediction leads to more accurate prediction decisions, in comparison with local spectrum prediction individually performed by SUs. To the best of our knowledge, this work is the first to use coalitional game theory to study cooperative spectrum prediction in CRNs, involving multiple PUs.     

Spectrum leasing based on Nash Bargaining Solution in cognitive radio networks

Cognitive radio is becoming an emerging technology that has the potential of dealing with the stringent requirement and scarcity of the radio spectrum resource. In this paper, we focus on the dynamic spectrum access of cognitive radio networks, in which the primary user (PU) and secondary users (SUs) coexist. In property-rights model, the PU has property of the bandwidth and may decide to lease it to secondary network for a fraction of time in exchange for appropriate remuneration. We propose a cooperative communication-aware spectrum leasing framework, in which, PU selects SUs as cooperative relays to help transmit information, while the selected SUs have the right to decide their payment made for PU in order to obtain a proportional access time to the spectrum. Then, the spectrum leasing scheme is cast into a Nash Bargaining Problem, and the Nash Bargaining Solution (NBS) can be used to fairly and efficiently address the resource allocation between PU and secondary network, enhancing both the utility of PU and secondary network. Numerical results show that spectrum leasing based on NBS is an effective method to improve performance for cognitive radio networks.     

New coalition formation game for spectrum sharing in cognitive radio networks

In cognitive radio networks, Secondary Users (SUs) can access the spectrum simultaneously with the Primary Users (PUs) in underlay mode. In this case, interference caused to the licensed users has to be effectively controlled. The SUs have to make spectrum access decisions in order to enhance their quality of service, but without causing harmful interference to the coexisting PUs. In this paper, we propose a cooperative spectrum decision, which enables the SUs to share the spectrum with the PUs more efficiently. Our approach is based on a new coalitional game in which the coalition value is a function of the SUs' spectral efficiencies, the inter‐SUs interference, and the interference caused to the PUs. By applying new Enter and Leave rules, we obtain a stable coalition structure. Simulation results show that the SUs' spectral efficiencies are considerably increased and that the interference caused to the coexisting PU is reduced by about 7.5% as compared to an opportunistic spectrum access scheme. Moreover, the proposed coalitional game results in a more balanced spectrum sharing in the network. Copyright © 2014 John Wiley & Sons, Ltd.     

Pre-reservation based spectrum allocation for cognitive radio network

Studies on the current usage of the radio spectrum by several agencies have already revealed that a large fraction of the radio spectrum is inadequately utilized. This basic finding has led to numerous research initiatives. Cognitive radio technology is one of the key candidate technologies to solve the problems of spectrum scarcity and low spectrum utilization. However, random behavior of the primary user (PU) appears to be an enormous challenge. In this paper, a Pre-reservation based spectrum allocation method for cognitive radio network is proposed to apply a PU behavior aware joint spectrum band (SB) selection and allocation scheme. In the first step, the SB is observed in terms of PU usage statistics whereas in the second phase, a network operator (NO) using a spectrum allocation scheme is employed to allocate SBs among secondary users (SUs). We also introduce the concept of reservation and exchange functionality under the priority serving strategy in a time-varying framing process. Simulation results show that the proposed scheme outperforms existing schemes in terms of the spectrum utilization and network revenue. In addition, it helps NO to manage the spectrum on a planned basis with a systematical spectrum reservation management where the NO has the status of time slots. Moreover, SUs have an opportunity to reserve or instantly request a SB that maximizes the SUs satisfaction in terms of quality of experience.     

On the capacity of cognitive broadcast channels with opportunistic scheduling

In this paper, we investigate the fundamental capacity limit of the cognitive broadcast channels with opportunistic scheduling, where cognitive users (CUs) can share the same spectrum with the primary user (PU) as long as the interference introduced to the PU is kept at an acceptable level. In this context, we analyze the capacity gains offered by this opportunistic spectrum sharing in Rayleigh fading environment. Specifically, we analyze the outage and effective capacity of the selected CU, and derive closed‐form expressions for these capacity metrics. We also obtain closed‐form expressions for the asymptotic performance as the bandwidth approaches infinity. Numerical results are provided to corroborate our theoretical analysis and quantify the effects of the system parameters. Copyright © 2011 John Wiley & Sons, Ltd.     

Spectrum auction with interference constraint for cognitive radio networks with multiple primary and secondary users

Extensive research in recent years has shown the benefits of cognitive radio technologies to improve the flexibility and efficiency of spectrum utilization. This new communication paradigm, however, requires a well-designed spectrum allocation mechanism. In this paper, we propose an auction framework for cognitive radio networks to allow unlicensed secondary users (SUs) to share the available spectrum of licensed primary users (PUs) fairly and efficiently, subject to the interference temperature constraint at each PU. To study the competition among SUs, we formulate a non-cooperative multiple-PU multiple-SU auction game and study the structure of the resulting equilibrium by solving a non-continuous two-dimensional optimization problem, including the existence, uniqueness of the equilibrium and the convergence to the equilibrium in the two auctions. A distributed algorithm is developed in which each SU updates its strategy based on local information to converge to the equilibrium. We also analyze the revenue allocation among PUs and propose an algorithm to set the prices under the guideline that the revenue of each PU should be proportional to its resource. We then extend the proposed auction framework to the more challenging scenario with free spectrum bands. We develop an algorithm based on the no-regret learning to reach a correlated equilibrium of the auction game. The proposed algorithm, which can be implemented distributedly based on local observation, is especially suited in decentralized adaptive learning environments as cognitive radio networks. Finally, through numerical experiments, we demonstrate the effectiveness of the proposed auction framework in achieving high efficiency and fairness in spectrum allocation.     

认知无线电频谱分配新算法研究

构造了一个基于合作博奕的非对称纳什协商效能函数,并通过2个认知用户间协商子载波的使用,实现了基于感知贡献加权的比例公平性频谱分配的新算法。仿真结果表明提出的方案不仅实现了频谱资源的公平有效分配,而且有利于频谱感知的最大化。     

Performance analysis of cooperative spectrum monitoring in cognitive radio network

Wireless Networks - The imperfect spectrum monitoring (SM) is a major obstacle to detect the emergence of primary user (PU) quickly during the cognitive users’ (CUs’) data transmission...     

Channel assignment scheme in clustered multichannel cognitive radio networks with outdated CSI over Rayleigh fading channels

This work investigates channel assignment for cooperative spectrum sensing in multichannel cognitive radio networks, where the heterogeneity of primary user (PU) activity and the effect of varying channel condition on the received signal‐to‐noise ratio during cluster formation are considered. With the objective to minimize interference to the PU while enhancing multiple spectrum utilization of the secondary user (SU), an overlapping cluster‐based assignment is formulated into a nonlinear integer optimization problem. To obtain an efficient solution, the nonlinear integer problem is transformed into a mixed integer linear problem, based on which, this paper proposes an exact solution and then two new heuristic algorithms for suboptimal solutions, respectively. Furthermore, a comparative study of four different cluster head selection schemes with respect to their performance in cooperative spectrum sensing, under cluster's heterogeneity in terms of SUs distribution relative to PU transmitter location is presented. Based on the study, a robust cluster head selection scheme is proposed. Simulation results show that good sensing performance and increased opportunistic spectrum utilization in multichannel cognitive radio networks are two sides of a coin that depend on the ratio of the SUs to the number of PU channels. How far away the PU is from the cluster center is also seen to be key in the optimal selection of cluster heads in cooperative spectrum sensing.     

An auction-based approach for spectrum leasing in cooperative cognitive radio networks: when to lease and how much to be leased

The problem of resource allocation in a spectrum leasing scenario in cooperative cognitive radio networks is addressed. The system model consists of a number of primary user (PU) pairs and a secondary user (SU) pair. The SU pair allocates the whole its transmission power in a portion of transmission frame to relay the primary signals. In return, the PU pairs lease their unused portion of transmission frame to the SU pair. In this way, the PU pairs take advantage of their unused portion of time to gain savings in their transmission power. However, a few important questions must be answered: When to lease and how much to be leased. We determine when is beneficial for PUs to lease their unused spectrum portion to the SU and how much of PUs’ resources is optimum to be leased. An efficient auction mechanism is proposed and the existence and uniqueness of the Nash Equilibrium (NE) for the proposed auction game is proved. Since the NE is the solution of a set of fixed point problems, two iterative algorithms, synchronous and asynchronous schemes, are proposed to reach the NE in an iterative manner and their convergence to the fixed point is also proved. Finally, the proposed auction is extended to a network with multiple secondary user pairs. Simulation results acknowledge the more efficient utilization of resources as a result of implementing the proposed auction based resource allocation.     

异构认知网络自适应频谱决策方法

To improve spectrum utilization and mini-mize interference to Primary User (PU), an adaptive spectrum decision method is proposed for Seconda-ry User (SU), while taking traffic load balancing and spectrum heterogeneity into consideration. Long-term statistics and current sensing results are integrated into the proposed decision method of spectrum access. Two decision methods, namely probability based and sensing based, are presented, compared and followed by performance analysis in terms of delay. For probability based spectrum deci-sion, Short-Time-Job-First (STJF) priority queuing discipline is employed to minimize average residual time and theoretical conclusion is derived in a novel way. For sensing based decision we treat the inter-rupted service of SU as newly incoming and re-de-cision process is initialized to find available spec-trum in a First-Available-First-Access (FAFA) fash-ion. Effect of sensing error in PHY layer is also an-alyzed in terms of extended average residual time. Simulation results show that, for relatively low arri-ving rate of SU traffic, the proposed spectrum deci-sion method yields at least a delay reduction of 39. 5% compared with non-adaptive method. The pro-posed spectrum decision can significantly improve delay performance even facing sensing errors, which cause performance degeneration to both PU and SU.     

Cognitive Radio Networks: Analysis of a Paid-Sharing Approach Based on Admission Control Decisions

Cognitive radio networks have emerged in the last decade as a solution for two problems: spectrum underutilization and spectrum scarcity. The main idea is to manage the radio spectrum more efficiently, where secondary users (SUs) are allowed to exploit the spectrum holes in primary user’s (PUs) frequency bands. We consider a paid-sharing approach where SUs pay for spectrum utilization. A challenging aspect in these mechanisms is how to proceed when a PU needs certain amount of bandwidth and the free capacity is insufficient. We assume a preemptive system where PUs have strict priority over SUs; when a PU arrives to the system and there are not enough free channels to accommodate the new user, one or more SUs will be deallocated. The affected SUs will then be reimbursed, implying some cost for the PUs service provider (SP). This paper bears on the design and analysis of an optimal SU admission control policy; i.e. that maximizes the long-run profit of the SP. We model the optimal revenue problem as a Markov Decision Process and we use dynamic programming and further techniques such as sample-path analysis to characterize properties of the optimal admission control policy. We introduce different changes to one of the best known dynamic programming algorithms incorporating the knowledge of the characterization. In particular, those proposals accelerate the rate of convergence of the algorithm when is applied in the considered context. Our results are validated through numerical examples.     

Spectrum sharing in multi-channel cooperative cognitive radio networks: a coalitional game approach

In this paper, we study a coalitional game approach to resource allocation in a multi-channel cooperative cognitive radio network with multiple primary users (PUs) and secondary users (SUs). We propose to form the grand coalition by grouping all PUs and SUs in a set, where each PU can lease its spectrum to all SUs in a time-division manner while the SUs in return assist PUs’ data transmission as relays. We use the solution concept of the core to analyze the stability of the grand coalition, and the solution concept of the Shapley value to fairly divide the payoffs among the users. Due to the convexity of the proposed game, the Shapley value is shown to be in the core. We derive the optimal strategy for the SU, i.e., transmitting its own data or serving as a relay, that maximizes the sum rate of all PUs and SUs. The payoff allocations according to the core and the Shapley value are illustrated by an example, which demonstrates the benefits of forming the grand coalition as compared with non-coalition and other coalition schemes.     

移动用户目标系统中基于拍卖模型的信道资源分配

为了提高卫星通信信道的频谱利用率,从卫星通信中的认知无线电技术出发,简述了当前认知卫星研究现状。针对移动用户目标系统( Mobile User Objective System,MUOS)的特殊通信环境,提出了插空式( overlay)频谱共享方式下基于拍卖模型的认知卫星信道分配方式,并建立了收益公式。同时,引入星上频谱共享干扰公式来计算认知用户对系统造成的影响,并代入收益公式以计算认知技术给系统带来的收益大小;之后给出模拟通信环境参数,利用Matlab进行仿真计算。仿真结果表明,认知用户的加入虽会使主用户通信质量略有下降,但使系统总体收益呈上升趋势。