This paper proposes the assignment of resource blocks (RBs) to reduce the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) in a multi-user OFDM system. This system ranks the users according to the channel state information (CSI) for RB assignment. In our proposed technique, an RB is assigned to either the first- or second-ranked mobile station (MS) to minimize the PAPR of the OFDM signal. While this process reduces the PAPR, the throughput is also reduced because of the user diversity gain loss. A PAPR-throughput tradeoff is then established. Theoretical analyses and computer simulations confirm that when the number of MSs becomes large, the PAPR-throughput tradeoff is eased because of the minimal effect of the diversity gain loss. Therefore, significant PAPR reduction is achieved with only a slight degradation in the throughput.

In OFDM-based cognitive radio systems, due to the out-of-band leakage from the secondary transmission, the interference to primary users must be considered in order to guarantee the quality of service of the primary transmission. For multiuser cognitive radio systems, there exist two crucial issues in resource allocation: fairness and efficiency, in order to balance the two issues, we proposed a new utility-based cross-layer resource allocation algorithm, which can not only control the interference to primary users caused by secondary users, but also balance the spectral efficiency and fairness among cognitive users. Further, the optimal NP-hard resource allocation problem in multiuser OFDM-based systems is reduced to the sub-optimal solution by dividing the original problem into the subcarrier allocation problem and the power allocation problem. It is shown that the proposed algorithm can obtain the best performance in terms of the average rate or the utility among existing algorithms, and at the same time, all the users obtain fair resource allocation.

The capacity of multiuser OFDM systems can be maximized by allocating resources (subcarrier and power) to the user with the highest instantaneous channel gain. This assumes complete channel state information (CSI) at the transmitter, which is achieved by every user reporting its CSI for all subcarriers to the transmitter via feedback channel. In practice, due to the limited capacity of the feedback channel, the completeness of CSI may be severely restricted especially with a large number of users transmitting a large amount of feedback information. In order to reduce the amount of feedback information while preserving the maximal capacity, quality based CSI feedback (QCF) is proposed in this letter. The system capacity is derived with QCF and compared with that of full CSI feedback. The results show that QCF successfully reduces the amount of feedback information with little capacity loss.

Although each application has its own quality of service (QoS) requirements, the resource allocation for multiclass services has not been studied adequately in multiuser orthogonal frequency division multiplexing (OFDM) systems. In this paper, a total transmit power minimization problem for downlink transmission is examined while satisfying multiclass services consisting of different data rates and target bit-error rates (BER). Lagrangian relaxation is used to find an optimal subcarrier allocation criterion in the context of subcarrier time-sharing by all users. We suggest an iterative algorithm using this criterion to find the upper and lower bounds of optimal power consumption. We also propose a prioritized subcarrier allocation (PSA) algorithm that provides low computation cost and performance very close to that of the iterative algorithm. The PSA algorithm employs subcarrier selection order (SSO) in order to decide which user takes its best subcarrier first over other users. The SSO is determined by the data rates, channel gain, and target BER of each user. The proposed algorithms are simulated in various QoS parameters and the fading channel model. Furthermore, resource allocation is performed not only subcarrier by subcarrier but also frequency block by frequency block (comprises several subcarriers). These extensive simulation environments provide a more complete assessment of the proposed algorithms. Simulation results show that the proposed algorithms significantly outperform existing algorithms in terms of total transmit power consumption.