Dynamic wireless spectrum access using GNU Radio and software‐defined radios

This paper presents the design, implementation, and results from a dynamic wireless spectrum access system built using GNU Radio and software‐defined radios (SDRs) as part of an undergraduate senior design project. The project involved designing and implementing a dynamic wireless spectrum access system in which the secondary user (SU) learns the unknown transmission behavior (channel occupancy and time slots) of the primary user (PU) and then opportunistically transmits during time slots and using channels when they are not being used by the PUs. The main design objective was to maximize the throughput of the SU while minimizing the interference to the PU. A transmitted signal energy detection algorithm with an adaptive threshold was employed to set the channel states as occupied or not occupied. Channel state information was used to determine the PU behavior in a deterministic manner such that the unused time slots and channels could be exploited. A channel allocation scheme for the SU is proposed using the PU channel occupancy information to calculate the channel(s) and time slots available to the SU at any given time. Simulation and physical testing of the system validate the proposed algorithms. Students' feedback affirms GNU Radio and SDRs to be an effective platform for introducing abstract mathematical communications theory concepts, such as cognitive radios and dynamic spectrum allocation, in a hands‐on manner.     

Fast Primary User Detection during Ongoing Opportunistic Transmission in OFDM-based Cognitive Radio

Cognitive radio requires that a secondary user (SU) stops its transmission as soon as possible upon the arrival of a primary user (PU). In this paper, we propose a novel approach to quickly detect PUs while an SU transmission is in progress. The proposed method requires an SU transmitter to use OFDM to insert periodic zero-energy intervals in a selected subcarrier and to detect energy during each interval for PU detection. The length and periodicity of these intervals determine the performance of PU detection. We also discuss how to determine the energy detection threshold.     

A game approach for cooperative spectrum sharing in cognitive radio networks

We consider the problem of cooperative spectrum sharing among primary users (PUs) and secondary users (SUs) in cognitive radio networks. In our system, each PU selects a proper set of SUs to serve as the cooperative relays for its transmission and in return, leases portion of channel access time to the selected SUs for their own transmission. PU decides how to select SUs and how much time it would lease to SUs, and the cooperative SUs decide their respective power levels in helping PU's transmission, which are proportional to their access times. We assume that both PUs and SUs are rational and selfish. In single‐PU scenario, we formulate the problem as a noncooperative game and prove that it converges to a unique Stackelberg equilibrium. We also propose an iterative algorithm to achieve the unique equilibrium point. We then extend the proposed cooperative mechanism to a multiple‐PU scenario and develop a heuristic algorithm to assign proper SUs to each PU considering both performance and fairness. The simulation results show that when the competition among SUs is fierce, the performance gap between our heuristic algorithm and the optimal one is smaller than 3%. Copyright © 2013 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.     

Joint Beamforming and Power Allocation for Cognitive MIMO Systems Under Imperfect CSI Based on Game Theory

The problem of joint beamforming and power allocation for cognitive multi-input multi-output systems is studied via game theory. The objective is to maximize the sum utility of secondary users (SUs) subject to the primary user (PU) interference constraint, the transmission power constraint of SUs, and the signal-to-interference-plus-noise ratio (SINR) constraint of each SU. In our earlier work, the problem was formulated as a non-cooperative game under the assumption of perfect channel state information (CSI). Nash equilibrium (NE) is considered as the solution of this game. A distributed algorithm is proposed which can converge to the NE. Due to the limited cooperation between the secondary base station (SBS) and the PU, imperfect CSI between the SBS and the PU is further considered in this work. The problem is formulated as a robust game. As it is difficult to solve the optimization problem in this case, existence of the NE cannot be analyzed. Therefore, convergence property of the sum utility of SUs will be illustrated numerically. Simulation results show that under perfect CSI the proposed algorithm can converge to a locally optimal pair of transmission power vector and beamforming vector, while under imperfect CSI the sum utility of SUs converges with the increase of the transmission power constraint of SUs.     

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.     

基于稳定匹配的认知无线网络协作物理层安全机制

在Underlay认知无线网络中,次用户被允许在主用户进行数据发送时接入主用户的频谱.此时,主用户将对次用户和窃听者造成干扰.利用协作干扰技术,主用户产生的干扰可以被用来改善次用户的物理层安全.基于此,本文针对包含多个主次用户的Underlay认知无线网络,提出了一种新的协作物理层安全机制.为了在保证主用户通信质量的前提下,最大化网络中次用户的总的安全容量,该机制将对次用户进行合理的频谱接入选择和功率控制.另外,考虑到个体理性和自私性对于频谱接入稳定性的影响,该机制利用稳定匹配理论将频谱接入选择问题建模为一对一的双边匹配问题,通过构建主次用户之间的稳定匹配来保证频谱接入的稳定性.仿真结果表明,使用本文所提安全机制,可以在保证主用户通信质量的前提下,稳定而又有效地改善网络中次用户获得的总的安全容量.     

Adaptive dual‐radio spectrum‐sensing scheme in cognitive radio networks

In this paper, a novel spectrum‐sensing scheme, called adaptive dual‐radio spectrum‐sensing scheme (ADRSS), is proposed for cognitive radio networks. In ADRSS, each secondary user (SU) is equipped with a dual radio. During the data transmission, with the received signal‐to‐noise ratio of primary user (PU) signal, the SU transmitter (SUT) and the SU receiver (SUR) are selected adaptively to sense one channel by one radio while communicating with each other by the other one. The sensing results of the SUR are sent to the SUT through feedback channels (e.g., ACK). After that, with the sensing results from the SUT or the SUR, the SUT can decide whether the channel switching should be carried out. The theoretical analysis and simulation results indicate that the normalized channel efficiency, defined as the expected ratio of time duration without interference to PUs in data transmission to the whole frame length, can be improved while satisfying the interference constraint to PUs. After that, an enhanced ADRSS is designed by integrating ADRSS with cooperative spectrum sensing, and the performance of ADRSS under imperfect feedback channel is also discussed. Copyright © 2011 John Wiley & Sons, Ltd.     

感知频谱共享下的多天线功率优化分配算法研究

本文构建了一种在感知结果下具有多天线次用户的频谱共享模型,该模型由单入单出主用户对和多入单出认知用户对构成。当认知用户感知到主用户占用信道时,主用户向认知用户发送Message信息,使得认知用户发射端能够得知主用户对干扰总功率的限制要求,通过自适应地调整认知用户发射机的发射功率,以保证其对主用户不造成有害干扰;如果主用户未占用信道,认知用户立刻以最大发射功率占用信道。并分别在主用户存在和不存在两种情况下,优化认知用户发射机各天线的发射功率来最大化系统总的数据传输率。最后,通过数值仿真来验证推导出的功率分配策略,并对其进行分析和讨论。仿真结果表明:相比于机会频谱接入（Opportunistic Spectrum Access, OSA）和基于感知的频谱共享（Sensing-based spectrum sensing）模式,推导的功率分配策略在提出的模型中可以获得更高的信息传输率。      

Power allocation policies with full and partial inter-system channel state information for cognitive radio networks

This paper investigates several power allocation policies in orthogonal frequency division multiplexing -based cognitive radio networks under the different availability of inter-system channel state information (CSI) and the different capability of licensed primary users (PUs). Specifically, we deal with two types of PUs having different capabilities: a dumb (peak interference-power tolerable) PU and a more sophisticated (average interference-power tolerable) PU. For such PU models, we first formulate two optimization problems that maximize the capacity of unlicensed secondary user (SU) while maintaining the quality of service of PU under the assumption that both intra- and inter-system CSI are fully available. However, due to loose cooperation between SU and PU, it may be difficult or even infeasible for SU to obtain the full inter-system CSI. Thus, under the partial inter-system CSI setting, we also formulate another two optimization problems by introducing interference-power outage constraints. We propose optimal and efficient suboptimal power allocation policies for these four problems. Extensive numerical results demonstrate that the spectral efficiency achieved by SU with partial inter-system CSI is less than half of what is achieved with full inter-system CSI within a reasonable range of outage probability (e.g., less than 10 %). Further, it is shown that the average interference-power tolerable PU can help to increase the saturated spectral efficiency of SU by about 20 and 50 % in both cases of full and partial inter-system CSI, respectively.     

Resource allocation in orthogonal frequency division multiple access‐based cognitive radio systems with minimum rate constraints

In this paper, resource allocation problem in orthogonal frequency division multiple access‐based cognitive radio (CR) systems to maintain minimum transmission rate constraints of CR users (CRUs) with the specified interference thresholds is investigated. Firstly, a single primary user (PU) CR system is considered, and a suboptimal resource allocation algorithm to maximize the sum transmission rate of all CRUs is proposed. Secondly, the single‐PU scenario is extended to multiple‐PU case, and an asymptotically optimal resource allocation algorithm is proposed using dual methods subject to constraints on both interference thresholds of PUs and total transmit power of all CRUs. Analysis and numerical results show that, in contrast to classical resource allocation algorithms, the proposed algorithm can achieve higher transmission rate and guarantee each CRU's minimum transmission rate in both scenarios. Copyright © 2012 John Wiley & Sons, Ltd.     

An adaptive handoff strategy for cognitive radio networks

Spectrum handoff plays an important role in spectrum management as it is the process of seamlessly shifting the on-going transmission of a secondary user (SU) to a free channel without degrading the quality of service. In this paper, we develop an adaptive handoff algorithm that allows an SU to detect the arrival of a primary user (via sensing) and adapt to a reactive or a proactive handoff strategy accordingly. The adaptive handoff scheme first allows an SU to decide whether to stay and wait on current channel or to perform handoff. Then, in case of handoff, an SU intelligently shifts between proactive or reactive handoff modes based on primary use (PU) arrival rate. Further, a PU prioritized Markov approach is presented in order to model the interactions between PUs and SUs for smooth channel access. Numerical results show that the proposed handoff scheme minimizes the blocking probability, number of handoffs, handoff delay and data delivery time while maintaining channel utilization and system throughput at maximal level compared to simple reactive and proactive schemes.     

Performance Analysis of the Primary User in the Secondary User Relay Assisted Spectrum Sharing Networks

In this paper, inaccurate spectrum detecting by the secondary user (SU) is taken into account. The impact of the interference caused by the SUs due to miss detection on the primary user (PU) in a spectrum sharing network is analyzed, and those SU nodes of correct detection are assumed to act as potential relays to assist the PU transmission process based on two proposed cooperative transmission schemes, referred to as, the distance based and the signal-to-noise-ratio (SNR) based schemes. We utilize stochastic geometry to analyze the impact of the secondary network parameters and cooperative transmission schemes on a typical primary source–destination (S–D) pair for the SU relay assisted spectrum sharing networks in Rayleigh fading environment. Using this approach, closed-form expressions for the primary system success probabilities with those cooperative transmission schemes as well as the PU direct re-transmission scheme are derived respectively. Simulations confirm our analytical derivations and results demonstrate that significant improvement on the PU success probability by using SU cooperative transmission schemes, and the SNR based scheme is superior to the distance based scheme.     

Cognitive Relaying with Frequency Incentive for Multiple Primary Users

This work is directed towards a symbiotic architecture called cognitive relaying with frequency incentive for multiple primary users (CRFI-M). The rationale of CRFI-M is that the primary users (PUs) of a cognitive radio (CR) network, with weak transmission links, seek cooperation from the secondary user (SU) nodes in their vicinity to achieve as much throughput as they can get from their direct links. In return they reward the SUs with incentive frequency bands for their own communication. Each PU has its own distinct bandwidth of operation; however, when relaying through the SU network it can also use the bandwidth of the other PUs to enhance its throughput. Furthermore, the incentive frequency bands may be offered as a complete band-set or interleaved bands. Cross-layer optimization problems are formulated for each of these variants of the CRFI-M paradigm in a multi-hop multi-channel SU network. The frequency incentive that should be awarded to the SUs by each PU is analyzed by means of a utility-based decision-making process, and its efficient utilization is proposed. To make the CRFI-M scheme practically realizable, a MAC scheduling protocol is described. Simulation results are furnished to demonstrate the proof of concept.     

Ergodic capacity and outage probability optimization for secondary user in cognitive radio networks under interference outage constraint

This paper considers a cognitive radio network where a secondary user (SU) coexists with a primary user (PU). The interference outage constraint is applied to protect the primary transmission. The power allocation problem to jointly maximize the ergodic capacity and minimize the outage probability of the SU, subject to the average transmit power constraint and the interference outage constraint, is studied. Suppose that the perfect knowledge of the instantaneous channel state information (CSI) of the interference link between the SU transmitter and the PU receiver is available at the SU, the optimal power allocation strategy is then proposed. Additionally, to manage more practical situations, we further assume only the interference link channel distribution is known and derive the corresponding optimal power allocation strategy. Extensive simulation results are given to verify the effectiveness of the proposed strategies. It is shown that the proposed strategies achieve high ergodic capacity and low outage probability simultaneously, whereas optimizing the ergodic capacity (or outage probability) only leads to much higher outage probability (or lower ergodic capacity). It is also shown that the SU performance is not degraded due to partial knowledge of the interference link CSI if tight transmit power constraint is applied.     

Beamforming and power allocation in the downlink of MIMO cognitive radio systems based on multiobjective optimization

In this paper, power allocation and beamforming are considered in a multiple input multiple output (MIMO) downlink cognitive radio (CR) communication system, which a base station (BS) serves one primary user (PU) and one secondary user (SU). In order to design the CR system, a constrained multiobjective optimization problem is presented. Two objectives are the signal to noise plus interference ratios (SINRs) of PU and SU. Since PU has a spectrum license for data communication, a constraint in the optimization problem is that the SINR of PU must be greater than a predefined threshold based on the PU demand requirement. Another constraint is a limitation on power in BS. By considering the mentioned model, three iterative algorithms are proposed. At each iteration of all algorithms, the receiver beamforming vectors are derived based on the maximization of PU and SU SINRs, by assuming that the allocated powers and BS beamforming vectors are known. Also, power is assigned to users such that the constraint of power limitation is satisfied. The difference between the algorithms is in the obtaining of transmitter beamforming parameters. We evaluate the performance of the proposed algorithms in terms of bit error rate (BER) in simulations. Also, the computational complexity of the proposed algorithms is obtained.     

Resource allocation for multiuser cognitive OFDM networks with proportional rate constraints

In this paper we study the resource allocation problem for the multiuser orthogonal frequency division multiplexing (OFDM)‐based cognitive radio (CR) systems with proportional rate constraints. The mutual interference introduced by primary user (PU) and cognitive radio user (also referred to secondary user, SU) makes the optimization problem of CR systems more complex. Moreover, the interference introduced to PUs must be kept under a given threshold. In this paper, the highest achievable rate of each OFDM subchannel is calculated by jointly considering the channel gain and interference level. First, a subchannel is assigned to the SU with the highest achievable rate. The remaining subchannels are always allocated to the SU that suffers the severest unjustness. Second, an efficient bit allocation algorithm is developed to maximize the sum capacity, which is again based on the highest achievable rate of each subchannel. Finally, an adjustment procedure is designed to maintain proportional fairness. Simulation results show that the proposed algorithm maximizes the sum capacity while keeping the proportional rate constraints satisfied. The algorithm exhibits a good tradeoff between sum capacity maximization and proportional fairness. Furthermore, the proposed algorithm has lower complexity compared with other algorithms, rendering it promising for practical applications. Copyright © 2011 John Wiley & Sons, Ltd.     

Fair and Efficient Spectrum Splitting for Unlicensed Secondary Users in Cooperative Cognitive Radio Networks

In cooperative cognitive radio networks (CCRNs), a licensed primary-user (PU) is allowed to leverage several unlicensed secondary-users (SUs) to relay its traffic. In this paper, a staged dynamic spectrum allocation (DSA) scheme is proposed for CCRNs. In the first stage, the network is uncongested. A simple pricing based DSA scheme is proposed for the PUs to lease their idled frequency bands to the SUs. And, hence, the initial quality of service (QoS) demands (in terms of the minimum rate requirements) of the PUs and the SUs are both satisfied through direct transmission on the allocated frequency bands. In the second stage, the network reaches the full-loaded situation. Therefore, a cooperative relaying based DSA scheme is proposed to stimulate the PUs to split more extra frequency bands to fulfill the increased QoS demands of the SUs, on condition that the QoS of the PUs are well maintained. By applying the cooperative bargaining game theory in the proposed cooperative relaying based DSA, on the one hand, the SUs can get fairness rate-rewards from the PUs according to the level of contribution that they can make to compensate the PUs for the rate-losses. Hence, the increased QoS demands of the SUs can be accommodated in short term. On the other hand, the PUs could retain the SUs successfully to obtain the long-term revenue, on condition that their QoS constraints are still satisfied. Finally, the analysis results of the proposed bargaining game theoretic DSA scheme (in the second stage) are testified through computer simulations.     

Robust beamforming in cognitive radio

This letter considers the multi-antenna cognitive radio (CR) network, which has a single secondary user (SU) and coexists with a primary network of multiple users. Our objective is to maximize the service probability of the SU, subject to the interference constraints on the primary users (PUs) in the form of probability. Exploiting imperfect channel state information (CSI), with its error modeled by added Gaussian noise, we address the optimization for the beamforming weights at the secondary transmitter. In particular, this letter devises an iterative algorithm that can efficiently obtain the robust optimal beamforming solution. For the case with one PU, we show that a much simpler algorithm based on a closed-form solution for the antenna weights of a given power can be presented. Numerical results reveal that the optimal solution for the constructed problem provides an effective means to tradeoff the performance between the PUs and the SU, bridging the non-robust and worstcase based systems.     

Mutual interference considered power allocation in OFDM-based cognitive networks: the multiple SUs case

Power allocation for secondary users (SUs) in cognitive networks is an important issue to ensure the SUs’ quality of service. When the mutual interference between the primary users (PUs) and the SUs is taken into consideration, it is wanted to achieve the conflict-free power allocation while synchronously maximizing the capacity of the secondary network. In this paper, the optimal power allocation problem is considered in orthogonal frequency division multiplexing cognitive networks. The single SU case is primarily formulated as a constrained optimization problem. On this basis, the multiple SUs case is then studied and simulated in detail. During the analysis, the mutual interference among the PUs and the SUs is comprehensively formulated as the restrictions on the SU’s transmission power and the optimization problems are finally resolved by iterative water-filling algorithms. Consequently, the proposed power allocation scheme restrains the interference to the primary network, as well as maximizing the capacity of the secondary network. Specifying the multiple-SUs case, simulation results are exhibited in a simplified scenario to confirm the efficiency of the proposed water-filling algorithm, and the influence of the mutual interference on the power allocation and the system capacity is further illustrated.