Kyuho Hwang

Joint Mode Selection and Power Allocation Scheme for Power-Efficient Device-to-Device (D2D) Communication

This paper proposes a power-efficient mode selection and power allocation scheme in device-to-device (D2D) communication system as an underlay coexistence with cellular networks. The proposed scheme is performed based on the exhaustive search of all possible mode combinations of the devices which consist of the mode indices for all devices in the system. Specifically, the proposed scheme consists of two steps. First, we calculate the optimal power with respect to the maximum power-efficiency for all possible modes of each device. Since the power-efficiency is not a concave function for the transmission power, we obtain the suboptimal solution by using the concavity of the lower and upper bound for the power-efficiency. The powerefficiencies for all possible modes of each device are obtained by the suboptimal power allocation in the first step. In the second step, we select a mode sequence which has the maximal power-efficiency among all possible mode combinations of the devices based on the obtained power-efficiencies in the first step. Then we can jointly obtain the suboptimal transmission power and the mode maximizing the power-efficiency. The proposed suboptimal scheme for the power allocation and mode selection performs close to the upper bound with respect to the power-efficiency. The simulation results also show that the proposed scheme outperforms the conventional schemes with respect to the power-efficiency and system capacity.

Interference Minimization Approach to Precoding Scheme in MIMO-Based Cognitive Radio Networks

A precoding scheme based on the interference minimization is proposed for an interference-limited communication system such as multiple-input multiple-output (MIMO)-based cognitive (CR) networks. The proposed precoder is to maximize the sum capacity through the minimization of interference power. By selecting a channel with the minimum singular value of the interference channel, the transmitter with the proposed precoder sends the information signals through the channel which produces the minimum interference in the system. Simulation results show that the proposed precoding scheme has the best performance in terms of the sum capacity in a MIMO-based CR network.

Blind Equalization Method Based on Sparse Bayesian Learning

A novel adaptive blind equalization method based on sparse Bayesian learning (blind relevance vector machine (RVM) equalizer) is proposed. This letter incorporates a Godard or constant modulus algorithm (CMA)-like error function into a general Bayesian framework. This Bayesian framework can obtain sparse solutions to regression tasks utilizing models linear in the parameters. By exploiting a probabilistic Bayesian learning framework, the sparse Bayesian learning provides the accurate model for the blind equalization, which typically utilizes fewer basis functions than the equalizer based on the popular and state-of-the-art support vector machine (SVM) - blind SVM equalizer. Simulation results show that the proposed blind RVM equalizer provides improved performances in terms of complexity, stability and intersymbol interference (ISI) and bit error rate (BER) in a linear channel and a similar BER performance in a nonlinear channel compared to the blind SVM equalizer.

Multi-mode precoding scheme based on interference channel in MIMO-based cognitive radio networks

The interference minimization approach to pre-coding is an appropriate scheme to mitigate the interference efficiently while it may cause the capacity loss of the desired channel. On the other hand, the precoding scheme for the maximal capacity of the desired channel improves the capacity of the desired users while it increases the interference power and finally causes the capacity loss of the interfered users. Therefore, we propose a precoding scheme which satisfies these two conflicting goals and manages the interference signal in an interference-limited environment such as a multiple-input multiple-output (MIMO)-based cognitive radio (CR) network. The proposed scheme consists of two steps. The first step nulls out the largest singular value of the interference channel to mitigate the dominant interference signal based on the interference minimization approach. The second step calculates the total system capacity of each mode and selects a mode to maximize the total system capacity. In the second step, each mode consists of the right singular vectors corresponding to the singular values except the largest singular value eliminated in the first step. Simulation results show that the proposed precoding scheme not only efficiently mitigate the interference signal, but also has the best performance in terms of the total system capacity in a MIMO-based CR network.

Blind beamformer for constant modulus signals based on relevance vector machine

The blind beamforming method for constant modulus (CM) signals based on relevance vector machine (RVM) is proposed. The proposed beamforming method is obtained by incorporating the constant modulus algorithm (CMA)-like error function into the conventional RVM framework. The RVM framework formulates the parameters of beamfomer by exploiting a probabilistic Bayesian learning procedure with assumption of Gaussian prior for parameters. The simulation results show that the proposed blind beamforming method can restore the desired signals with crowded interference signals.

Approximate Minimum BER Power Allocation of MIMO Spatial Multiplexing Relay Systems

In this paper, a multiple-input and multiple-output (MIMO) spatial multiplexing (SM) relay system is studied in a bit error rate (BER) sense, where every node is deployed with multiple antennas. In order to efficiently use the limited power resource, it is essential to optimally allocate the power to nodes and antennas. In this context, the power allocation (PA) algorithm based on minimum BER (MBER) for a MIMO SM relay system is proposed, which is derived by direct minimization of the average BER, and divided into inter-node and inter-antenna PA algorithm. The proposed scheme outperforms the conventional equal power allocation (EPA) algorithm without extra power consumption.