An Efficient Water-Filling Algorithm for Power Allocation in OFDM-Based Cognitive Radio Systems

In this paper, we present a new water-filling algorithm for power allocation in Orthogonal Frequency Division Multiplexing (OFDM) – based cognitive radio systems. The conventional water-filling algorithm cannot be directly employed for power allocation in a cognitive radio system, because there are more power constraints in the cognitive radio power allocation problem than in the classic OFDM system. In this paper, a novel algorithm based on iterative water-filling is presented to overcome such limitations. However, the computational complexity in iterative water-filling is very high. Thus, we explore features of the water-filling algorithm and propose a low-complexity algorithm using power-increment or power-decrement water-filling processes. Simulation results show that our proposed algorithms can achieve the optimal power allocation performance in less time than the iterative water-filling algorithms

An Efficient Water-Filling Algorithm for Power Allocation in OFDM-Based Cognitive Radio Systems

In this paper, we present a new water-filling algorithm for power allocation in Orthogonal Frequency Division Multiplexing (OFDM) – based cognitive radio systems. The conventional water-filling algorithm cannot be directly employed for power allocation in a cognitive radio system, because there are more power constraints in the cognitive radio power allocation problem than in the classic OFDM system. In this paper, a novel algorithm based on iterative water-filling is presented to overcome such limitations. However, the computational complexity in iterative water-filling is very high. Thus, we explore features of the water-filling algorithm and propose a low-complexity algorithm using power-increment or power-decrement water-filling processes. Simulation results show that our proposed algorithms can achieve the optimal power allocation performance in less time than the iterative water-filling algorithms

Power Allocation for Adaptive OFDM Index Modulation in Cooperative Networks

In this paper, we propose a power allocation strategy for the adaptive orthogonal frequency-division multiplexing (OFDM) index modulation (IM) in cooperative networks. The allocation strategy is based on the Karush-Kuhn-Tucker (KKT) conditions, and aims at maximizing the average network capacity according to the instantaneous channel state information (CSI). As the transmit power at source and relay is constrained separately, we can thus formulate an optimization problem by allocating power to active subcarriers. Compared to the conventional uniform power allocation strategy, the proposed dynamic strategy can lead to a higher average network capacity, especially in the low signal-to-noise ratio (SNR) region. The analysis is also verified by numerical results produced by Monte Carlo simulations. By applying the proposed power allocation strategy, the efficiency of adaptive OFDM IM can be enhanced in practice, which paves the way for its implementation in the future, especially for cell-edge communications

Power allocation algorithm in OFDM-based cognitive radio systems

In orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) systems, the optimized algorithms for sub-carrier power allocation face the problems of complex iterative calculation and difficult realization. In this paper, we propose an exponential power distribution function and derive a sub-optimal power allocation algorithm. This algorithm aims to allocate power of in-band subcarriers of cognitive users according to the numerical characteristics of the power distribution function by using a convex optimization numerical method under linear constraints. This algorithm has the advantages of fast calculation speed and easy realization, and reduces the interference to the authorized users, which is caused by the power leakage of the in-band subcarriers of cognitive users to the out-of-band subcarriers. Simulation results show that the proposed algorithm maximizes the inband channel capacity of the cognitive users under certain interference thresholds of the authorized users, thus increasing their transmission rate

Hybrid Relaying Protocol for Joint Power and Subcar-rier Allocation for OFDM Based Cognitive Radio Networks

Abstract This paper aims to avoid the interference imposed by the secondary user on a primary user in Cognitive Radio Network (CRN). In CRN, the interference from secondary user enforced on primary user mainly depends on spectral interval between primary and secondary systems. Moreover, it also depends on the power allocated to the secondary user. In order to avoid interference imposed by secondary user on primary user, a Hybrid Relaying Protocol for Joint Power and Subcarrier Allocation for Orthogonal Frequency Division Multiplexing (OFDM) based Cognitive Radio Networks is proposed. In hybrid relaying protocol, a secondary user uses amplify and forward (AF) protocol and decode and forward (DF) protocol based on the requirement to maximize network throughput. A greedy algorithm is proposed for the selection of relay to get the optimal solution. Moreover, an efficient hybrid power and subcarrier algorithm is used by considering interference constraint imposed by cognitive network to the primary user

Optimal power allocation for MIMO-OFDM based Cognitive Radio systems with arbitrary input distributions

In Cognitive Radio (CR) systems, the data rate of the Secondary User (SU) can be maximized by optimizing the transmit power, given a threshold for the interference caused to the Primary User (PU). In conventional power optimization algorithms, the Gaussian input distribution is assumed, which is unrealistic, whereas the Finite Symbol Alphabet (FSA) input distribution, (i.e., M-QAM) is more applicable to practical systems. In this paper, we consider the power optimization problem in multiple input multiple output orthogonal frequency division multiplexing based CR systems given FSA inputs, and derive an optimal power allocation scheme by capitalizing on the relationship between mutual information and minimum mean square error. The proposed scheme is shown to save transmit power compared to its conventional counterpart. Furthermore, our proposed scheme achieves higher data rate compared to the Gaussian optimized power due to fewer number of subcarriers being nulled. The proposed optimal power algorithm is evaluated and compared with the conventional power allocation algorithms using Monte Carlo simulations. Numerical results reveal that, for distances between the SU transmitter and the PU receiver ranging between 50m to 85m, the transmit power saving with the proposed algorithm is in the range 13-90%, whereas the rate gain is in the range 5-31% depending on the modulation scheme (i.e., BPSK, QPSK and 16-QAM) used