Research On Radio Resource Management Algorithm In Cognitive Small Cell Networks

This dissertation uses cognitive small cell networks and large-scale antennas technologies to improve system throughput, energy efficiency and the number of communication users. This dissertation summarizes the development vision and the corresponding key technologies in 5G, and analyzes the important role and challenges of small cell networks in the future wireless communication system and gives corresponding solutions, and proposes the solutions of solving problems exiting in uplink/downlink communication when the small cell networks coexist with macro networks, and discusses the performance of power gain in large-scale multi-user multi-antenna system.This dissertation mainly studies the algorithms of spectrum allocation, power control, base station selection, and user switching in CSCN (cognitive small cell networks). Firstly, the dynamic spectrum resource allocation process is modeled as a potential game from minimizing the total system interference level point of view, based on the interference temperature constraints and the new interference operator in which the spectrum resource can be overlapping. And the existence and uniqueness of Nash equilibrium are proved. Secondly, the user-centered resources allocation model is proposed for realizing the joint allocation of spectrum resources, small cell base stations and transmission power in uplink under a distributed framework. The joint selection of spectrum resources and small cell base stations is modeled as a potential game, and the power allocation process is modeled as a non-cooperative game based on maximizing capacity and energy efficiency respectively. The price factor is given to optimize the global system performance. For maximizing the energy efficiency which is non-convex optimal when solving the power allocation strategies, the dissertation derives an equivalent expression which is more easily solved, and proposes a simplified iteration algorithm. Thirdly, the downlink spectrum resource blocks and power allocation is modeled as a non-cooperative game in which the players are the small cell base stations. And two algorithms based on opening access behavior and interference temperature respectively are used to avoid damaging primary users’ normal communication. The master game is re-modeled as equivalent sub-games for solving the transmission power more easily by transmitting the non-convex optimal energy efficiency into an equivalent maximizing problem. For the convergence of the spectrum resource blocks allocation strategies, a new centralized allocation model is proposed with limited feedback information. And the sub-games with coupling limitation for maximizing their utility functions are transmitted into variational inequality problems, and the existence and uniqueness of equilibrium solutions are proved. Finally, in the large-scale multi-user multi-antenna system, the dissertation studies a zero forcing power allocation algorithm when satisfying the minimum individual rates and maximum transmission power constraints. Based on the statistical characteristics of the equivalent channel gain, the closed-form expression for minimizing the average transmission power of a single user is given, and the dissertation also derives the power gain expressions based on the perfect and non-perfect channel estimation respectively.