Research On OFDM Based Cognitive Radio Resource Allocation Problem With Multi-norms

Resource allocation is one of the key technologies to guarantee communication system to work normally as well as utilize the system resource efficiently. However, an entirely new technique, i.e. cognitive radio which is supposed to resolve the contradiction between the scarcity of wireless frequency spectrum and low utilization of the existed spectrum, has hardly satisfied the needs of this technique due to its fast dynamic feature with former resource allocation algorithms. How to reasonably and efficiently allocate resource in the cognitive radio system is becoming a new hot research spot in the research area of wireless communication. At the same time, this technique also can surely provide many opportunities and challenges for the future development of communication technologies.Recently, the existing cognitive radio resource allocation algorithms are mainly based on classical convex optimization theory, game theory, graph coloring theory, cooperative manner, as well as intelligent optimization theory etc. Although these algorithms can partially satisfy some optimal objectives in one timeslot, they are not the most suitable methods to describe system physical feature to this new problem from the point of view of cognitive radio dynamic feature. In order to fit the new characters of this technology, this paper tries to use multi-norms to discuss, analyze and study this problem from different profiles, and gives a corresponding system model and some solutions.The research significance and background, the current status on cognitive radio resource allocation research both at home and aboard are presented in the first two chapters. And the analysis of the main weakness of the existing algorithms and basic concept of cognitive radio under OFDM principle are discussed briefly. Then the merits of implementing cognitive radio system by OFDM technology are expounded. At last, the features of cognitive radio resource allocation problem are given.In order to adapt the fast changing feature of network environment and consider the fairness among users in cognitive radio system, chapter 3 proposes a fairness threshold based cognitive radio resource allocation algorithm. On the one hand, traditional communication system often demands a strict proportional fairness that may cause the whole system capacity drop. And on the other hand, the complexity of traditional algorithms may also lead to the reduction of system available transmission time. Comparing to the traditional wireless communications, cognitive radio has an opportunistic transmission feature. This makes that the cognitive user can use the licensed spectrum bands during the time when primary user does not use it. However cognitive user must release the bands immediately when primary user reoccupies them. It may easily bring the problem that the resource allocation algorithm has not been calculated and the primary users have already come back to the bands. Moreover, more complex algorithms take more precious transmission time, so that the system performance heavily degrades. Therefore, it is clear that much more transmission opportunities are more important than the extreme strict fairness among different users in cognitive radio system. By considering the trade-off between system fairness and algorithm complexity, we propose a fairness threshold concept that obtains more capacity by sacrificing partial fairness in the system. At power allocation stage, we introduce the particle optimization method to resolve this problem. It can largely promote algorithm convergence speed. Simulation results demonstrate that the proposed algorithm efficiently improves whole system performance under a certain fairness condition, meanwhile obtain a fast convergence speed.Due to cognitive radio system is often significantly affected by primary user's activity, we propose a primary user activity based and primary user transmission outage probability constrained resource allocation algorithm in chapter 4. In a cognitive radio networks, secondary user must watch the spectrum situation timely in order to find out the optimal resource allocation strategy. However, when primary user's activity is too frequent, secondary user often can not catch up this rapid change. In consideration of this problem, in order to obtain a more appropriate resource allocation algorithm for cognitive radio networks, a new description model for primary user's activity is needed. Here, we construct a model for primary user's activity on each frequency spectrum by corresponding activity probability. To obtain the real data rate of secondary user, the data rate loss of each spectrum from a statistics view is calculated. Meanwhile, an outage probability concept as one constraint of this optimization problem is introduced in order to guarantee primary user's transmission quality. In the computer simulation results, it can be seen that the proposed algorithm efficiently improves the performance both in system capacity and network adaptation compare to the former method.In a fast changing cognitive network environment, it is considered that the stable and reliable transmission is more important. In chapter 5, we introduce the portfolio selection theory in economy to formulize cognitive radio resource allocation problem into a best investment selection problem. And the stable transmission schemes in a minimum-variance sense are given. At the same time, to limit harm interference from the cognitive user, mutual interference concept is introduced as a constraint of this problem. In this algorithm we look the system total power and channel capacity on each subcarrier as total security assets and return of each security respectively. Therefore, our optimal objective is to minimize the variance of system data rate under a given expected transmission rate. From the mathematics, it is the same problem with"risk variance minimization problem"in security market. Hence, we can use portfolio selection theory to deal with resource allocation problem in a cognitive radio system. Meanwhile, communication quality for primary user by mutual interference threshold is efficiently protected. Simulation results show that the propose algorithm can provide more efficient protection to primary user compare to the former scheme at the same stable transmission rate. This improvement makes the proposed algorithm more adaptable for cognitive radio system.In order to maximize a long-term average rate during the whole transmission process in cognitive radio system, a dynamic programming method to analyze the resource allocation problem is proposed, and a mathematic iterative expression is derived in chapter 6. Since in the cognitive radio transmission the data is transmitted in each time slot, it is just correspond with the character of dynamic programming that the decisions are made in each stage. At the same time, the dynamic changing feature of cognitive radio can also be well described by transitional relationship among different states in a dynamic programming framework. Therefore, it is a valuable attempt to deeply discuss the cognitive radio resource allocation problem with dynamic programming method. This method can model primary user's occupation on each subcarrier by discrete-time Markov chain. And it gives the transition probabilities among different system states. Meanwhile, a primary rate loss model to guarantee primary user's transmission quality is introduced. Finally, a specific dynamic programming iterative expression is given. In the given computer simulations, it shows that the proposed algorithm not only maximizes the long-term average rate in the whole transmission process but also efficiently guarantee primary user's request data rate. In addition, it also provides a convenient, fast and practical method for cognitive radio resource allocation.At last, chapter 7 concludes the whole paper and forecasts the next step research work.