Spectrum efficiency of orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems can be improved by adaptive resources allocation. In resources allocation, transmission resources such as modulation level and transmission power are adaptively assigned based on channel variations. The goal of this paper is maximize the total transmission rate of secondary user (SU). Hence, we investigate adaptive power and modulation allocation to achieve this purpose. For power allocation, we investigate optimal and conventional methods and then introduce a novel suboptimal algorithm to calculate the transmission power of each subcarrier. In addition, for adaptive modulation, we consider two kinds of modulations including multi-quadrature amplitude modulation (MQAM) and multi-phase-shift keying (MPSK). Also, simulation results are indicated the performance of our algorithm.
A novel method to improve the performance of the frequency band is cognitive radio that was introduced in 1999. Due to a lot of advantages of the OFDM, adaptive OFDM method, this technique is used in cognitive radio (CR) systems, widely. In adaptive OFDM, transmission rate and power of subcarriers are allocated based on the channel variations to improve the system performance. This paper investigates adaptive resource allocation in the CR systems that are used OFDM technique to transmit data. The aim of this paper is to maximize the achievable transmission rate for the CR system by considering the interference constraint. Although secondary users can be aware form channel information between each other, but in some wireless standards, it is impossible for secondary user to be aware from channel information between itself and a primary user. Therefore, due to practical limitation, statistical interference channel is considered in this paper. This paper introduces a novel suboptimal power allocation algorithm. Also, this paper introduces a novel bit loading algorithm. In the numerical results sections, the performance of our algorithm is compared by optimal and conventional algorithms. Numerical results indicate our algorithm has better performance than conventional algorithms while its complexity is less than optimal algorithm.
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