When Zero-Forcing (ZF) is adopted as a detector, decreasing the condition number of the channel matrix increases the BER performance. In this paper, we propose a new detection algorithm which reduces the condition number of channel matrix down to nearly 2 on average. Since the least singular value of the channel matrix is a major factor determining the condition number, we, first, project the received signal into a space spanned by singular vectors that are orthogonal to the one corresponding to the least singular value. Then, LR decomposition is performed to reduce further the condition number of the projected channel matrix. Computer simulations show that the performance of the proposed algorithm is comparable to that of the ML detector for both correlated and uncorrelated channels. And also the proposed algorithm provides an at least 2dB improvement compared to the conventional LR-based Ordered Successive Interference Cancellation (LR-OSIC) detector with a Bit Error Rate (BER) of 10-3 and a comparable computation load.

A compact representation of the Green function is proposed by applying the discrete wavelet concept in the k-domain, which can be used for the acceleration of scattered field calculations in integral equation methods. A mathematical expression of the Green function based on the discrete wavelet concept is derived and its characteristics are discussed.

In heterogeneous networks, network selection is an important task for reconfigurable mobile devices (MDs). In the reconfigurable MD architecture that has been standardized by the European Telecommunications Standards Institute (ETSI), the network selection functionality is handled by a software component called Mobility Policy Manager (MPM). In this paper, we present an implementation of the MPM whereby a reconfigurable MD conforming to the ETSI standard can select the most appropriate radio access network (RAN) to use. We implemented a reconfigurable MD test-bed compliant with the ETSI standard, and show that the network selection driven by the MPM enhances the throughput of the receiving MD by about 26% compared to the arbitrary network selection provided by a conventional reconfigurable MD without the functionality of MPM, verifying the functionality of the MPM.

This letter introduces a joint design method for uplink-downlink multiple-input multiple-output (MIMO) relay communication systems in which the source nodes transmit information to the destination nodes with the help of a relay. We propose a signal forwarding schceme based on the minimum mean-square error (MMSE) approach in uplink relay systems. Exploiting the duality of relay systems, we also propose a relaying scheme for downlink relay systems. Simulation results confirm that the proposed joint design method improves the performance of the relay systems compared with that of conventional relaying schemes in uplink and downlink MIMO relay systems.

This paper presents a new on-off beamforming algorithm to provide an optimal weight vector. The proposed algorithm is based on the eigen-space on-off algorithm for exploiting a diversity gain that is available in the signal environments of wide angle spread. The proposed technique utilizes the primary and secondary eigenvectors to form the optimal weight vector, which is robust to the angle spread. It is confirmed in this paper that the proposed algorithm shows excellent performance compared to some typical beamforming methods which degrade the receiving performance as the angle spread increases. It is also shown that the proposed algorithm has a reasonable computational complexity of O(14N) and fast convergent speed, where N is the number of antenna elements.

The sub-blocking algorithm has been known as a core component in implementing a turbo decoder using a Graphic Processing Unit (GPU) to use as many cores in the GPU as possible for parallel processing. However, even though the sub-blocking algorithm allows a large number of threads in a given GPU to be adopted for processing a large number of sub-blocks in parallel, each thread must access the global memory with strided addresses, which results in uncoalesced memory access. Because uncoalesced memory access causes a lot of unnecessary memory transactions, the memory bandwidth efficiency drops significantly, possibly as low as 1/8 in the case of an Long Term Evolution (LTE) turbo decoder, depending upon the compute capability of a GPU. In this paper, we present a novel method for converting uncoalesced memory access into coalesced access in a way that completely recovers the memory bandwidth efficiency to 100% without additional overhead. Our experimental tests, performed with NVIDIA's Geforce GTX 780 Ti GPU, show that the proposed method can enhance the throughput by nearly 30% compared with a conventional turbo decoder that suffers from uncoalesced memory access. Throughput provided by the proposed method has been observed to be 51.4Mbps when the number of iterations and that of sub-blocks are set to 6 and 32, respectively, in our experimental tests, which far exceeds the performance of previous works implemented the Max-Log-MAP algorithm.

In a multi-user multiple-input multiple-output (MU-MIMO) system that adopts zero-forcing (ZF) as a precoder, the best selection is the combination of users who provide the smallest trace of the inverse of the channel auto-correlation matrix. Noting that the trace of the matrix is closely related to the determinant, we search for users that yield the largest determinant of their channel auto-correlation matrix. The proposed technique utilizes the determinant row-exchange criterion (DREC) for computing the determinant-changing ratio, which is generated whenever a user is replaced by one of a group of pre-selected users. Based on the ratio computed by the DREC, the combination of users providing the largest changing ratio is selected. In order to identify the optimal combination, the DREC procedure is repeated until user replacement provides no increase in the determinant. Through computer simulations of four transmit antennas, we show that the bit error rate (BER) per signal-to-noise ratio (SNR) as well as the sum-rate performance provided by the proposed method is comparable to that of the full search method. Furthermore, using the proposed method, a partial replacement of users can be performed easily with a new user who provides the largest determinant.

In this paper, we present an algorithm for reducing the transmit normalization factor by perturbing the transmit signal in a Multi-User Multiple Input Multiple Output (MU-MIMO) system which uses the channel inverse matrix as its precoding matrix. A base station must normalize unnormalized transmit signals due to the limitation of the constant transmit power. This paper defines the norm of the unnormalized transmit signal as the transmit normalization factor used to normalize the transmit signal. Recalling that the transmit normalization factor consists of a combination of the singular values from the channel inverse matrix, we provide a codebook that successively reduces the coefficients of these singular values. Through computer simulations, the proposed algorithm is compared to sphere encoding in terms of the Bit Error Rate (BER) and the outage probability in a MU-MIMO signal environment. Sphere encoding is known to be an optimal solution amongst the perturbation methods that reduce the transmit normalization factor [1]. This work demonstrates that the proposed algorithm is has very good performance, comparable to that of sphere encoding, while its computational load is nearly 200 times less. Since the codebook in our algorithm depends only on the given channel, the difference in the computational complexity becomes even greater when the channel state is not changed, because the codebook can be reused. Furthermore, the codebook exhibits the characteristic of robustness to the maximum Doppler shift.

This letter presents an optimal user selection algorithm that provides a maximum sum-rate in a zero-forcing based Multiuser MIMO system for downlink. The proposed technique forms a primary group of users whose channel power exceeds a predetermined threshold. Through computer simulations, we have found that the proposed method outperforms the conventional technique yielding a sum rate that is 0.33 bps/Hz higher when the transmit SNR is 10 dB and the total number of users and transmit antennas in the cell is 100 and 4, respectively.

A new beamforming technique based on the power method is proposed in this paper. We show that the new technique is quite robust to the angular spread in the received signals making it particularly useful for smart antennas in WCDMA systems. The proposed technique utilizes two primary eigenvectors of the autocovariance matrix of the received data to form the weight vector of a smart antenna. An efficient adaptive procedure combining the power method and deflation method is given to compute the first and second largest eigenvalues with a reasonable complexity and accuracy. To demonstrate the proposed technique, it has been applied to WCDMA signal environment showing its robust and improved performance.

This letter presents a novel user scheduling algorithm that provides a maximum sum-rate based on zero-forcing beamforming (ZFBF) in multiple-input multiple-output (MIMO) systems. The proposed technique determines primary user pairs in which the sum-rate exceeds a predetermined threshold. To determine the threshold, we define the maximum-sum-rate criterion (MSRC) derived from the extreme value theory (EVT). Applying the MSRC in ZFBF-based user scheduling, we find that the performance of the proposed method is comparable to that of the exhaustive searching scheme which has a greater computational load. Through computer simulations, we show that the proposed method outperforms the very well-known correlation-based method, semi-orthogonal user selection (SUS), yielding a sum rate that is about 0.57 bps/Hz higher when the transmit SNR is 10 dB with perfect CSI at BS and the numbers of users and transmit antennas in a cell are 100 and 4, respectively.

This paper proposes a new blind adaptive algorithm for computing the weight vector of an antenna array system. The new technique utilizes a Generalized On-Off algorithm to obtain the weight vector from the pilot channel of IS2000 1X system, of which the processing gain can be controlled arbitrarily. The proposed algorithm generates a suboptimal weight vector maximizing the SINR (signal to interference plus noise ratio) with a linear computational load. Based on the analysis obtained from various simulations, it is observed that the proposed algorithm is suitable for the practical IS2000 1X mobile communication environments.

To make the best use of the known characteristics of the alternating-direction-implicit finite-difference time-domain (ADI-FDTD) method such as unconditional stability and modeling accuracy, an efficient time domain solution with variable time-step size is proposed. Numerical results show that a time-step size for a given mesh size can be increased preserving a desired numerical accuracy over frequencies of interest.

The objective is to generate a suboptimal weight vector for an adaptive array system operating in a multipath fading CDMA (Code Division Multiple Access) channel. The total computational load of the proposed procedure is about including the update of autocovariance matrix as well as the weight update itself, where N is the number of antenna elements. The performance of the proposed array system is shown in terms of BER (Bit Error Rate), allowable capacity, and convergence rate in practical CDMA signal environments such as IS95 and IS2000 1X.

This letter introduces an alternative adaptive beamforming with the total computational load of about O(3N) where N denotes the number of antenna elements. The proposed technique finds a weight vector that maximizes the received signal power at the array output by searching for the suboptimal phase of each weight in a multipath fading CDMA mobile communication environment.