Research On Resource Allocation In Cognitive Radio Systems

The contradiction between limited spectrum resource and increasing demand forwireless spectrum has become intensified. How to improve spectrum utilization efficiencywithin limited spectrum resource becomes a challenge. Cognitive radio as one of anintelligent wireless communication system can use spectrum in an efficient way. That is apromising technology to deal with the spectrum under-utilization problem.Cognitive radio can improve the spectrum utilization greatly by opportunisticspectrum access or spectrum sharing with primary user. Opportunistic spectrum accessneeds to detect and make use of spectrum holes. Once the primary user arrived, cognitiveuser must exit to find other spectrum holes immediately. Spectrum sharing mode allows theco-existence of cognitive user and primary user (PU) as long as the interference does notdegrade the PU’s performance. In this paper, spectrum sharing technique as a platform incognitive radio networks will be used.The power control problem of cognitive radio networks has been studied widely inrecent years. Cognitive radio can sense, learn, optimize, and reconfigure operatingparameters corresponding to the environment. Cognitive users usually trade off betweenmaximize its own data capacity and minimize interference which produces at primaryreceiver. This paper studies optimal power allocation strategies in different perspective.System model and the corresponding solutions are given. The main research results asfollows:1）Most traditional resource allocation algorithms are based on the hypothesis ofperfect channel state information in cognitive radio networks. However, cognitive radio isin a highly dynamic environment, channel parameters are disturbance in a certain degree.Robust optimization can deal with the uncertainty of channel parameters. A robust powerallocation algorithm in cognitive radio networks is proposed. An ellipsoidal uncertainty isused to set to describe the fluctuation of the channel interference gain. Besides interferencepower constraint which protects primary user, transmit power constraint of the cognitiveuser transmitter is considered. This power allocation problem is an optimization problemwhich has infinite number of constraints. Through robust optimization it can betransformed into another optimization problem with finite number of constraints on theworst case conditions. This algorithm can guarantee the stability and robustness to thecognitive system and provide seamless communication to each user even the channel isdisturbed.2）Target capacity tracking is a new key issue in cognitive radio system. In order to track the target capacity, a time-delay state space model is used to describe the error ofdynamic capacity. This power allocation problem of cognitive system is transformed intostate feedback control problem. The output of state feedback controller always tracks thetarget capacity of the real system. Linear matrix inequality (LMI) is obtained by H-infinitecontrol theory. Through LMI, the gain of the feedback controller and tracking capacity isobtained. The power allocation at each subcarrier for every cognitive user is got. Thepower allocation algorithm based on control theory from a new perspective in cognitiveradio networks is provided. This scheme not only guarantees the interference power forprimary user but also provides QoS guarantee to cognitive user.3）In order to get more communication capacity, power control problem is animportant factor in cognitive systems. The power allocation problem is formulated tomaximize the data capacity of each cognitive user and guarantee the QoS of both primaryuser and cognitive user. This optimization problem becomes a mixed integer nonlinearprogramming problem equivalent to variational inequalities problem in convex polyhedron.The variational inequalities problem is transformed into a complementary problem bymathematical tools. Finally, modified projection method is utilized to solve thiscomplementary problem. The optimal power allocation scheme for cognitive user isderived. This strategy makes the cognitive system converge to the Nash equilibriumquickly, and achieves maximum capacity for cognitive users.4）Cooperative communication is an important strategy to improve power efficiencyand increase system capacity as well as augment the coverage. An adaptive relay selectionand power allocation scheme are proposed. The scheme has fast convergence speed andbetter stability. The cognitive user chooses its best relay to cooperative communication ifcognitive user gets SINR below its requirement in non-cooperative communication. Thispower control scheme is a mixed integer nonlinear programming problem. KKT method isused to get the optimal solution. The goal of the scheme is to guarantee the SINR for thecognitive user; meanwhile maximize the system capacity. This scheme not only improvesthe data capacity greatly but also increases the coverage speed.5）Most of resource allocation schemes are based on the interference power constraintof primary user in cognitive radio systems. This paper presents a new criterion calledallowable signal to interference plus noise ratio (SINR) loss constraint in cognitivetransmission to protect primary users. The allowable SINR loss refers to the maximumSINR loss which primary user can tolerate. The power allocation algorithm based oninterference power constraint has been instead of by this criterion. Compared withtraditional power control method based on interference constraint, this method can obtainmore channel capacity and achieve a stable equilibrium. This method is not only easy to imple ment but also have better performance.