Junil Ahn

Loop-interference suppression strategies using antenna selection in full-duplex MIMO relays

Loop-interference (LI) from relay transmission to reception reduces the channel capacity and makes the relay system unstable in full-duplex (FD) multi-input multi-output (MIMO) relay systems. conventional schemes to suppress LI still have drawbacks: 1) incur a dispensable signal-to-noise ratio (SNR) loss in low SNR regions; 2) increase the system complexity due to the requirement of weighting matrices; and 3) make insufficient practical sense due to varying signal-to-interference ratios (SIRs). In this paper, we propose a new LI suppression scheme using transmit antenna selection in order to solve the above problems. Simulation results show that the proposed scheme outperforms a conventional scheme, especially in low SNR and high SIR regions, even though the proposed scheme has a lower complexity.

Schnorr-Euchner sphere decoder with statistical pruning for MIMO systems

A near-maximum-likelihood (ML) detection algorithm for spatially multiplexed multiple-input multiple-output (MIMO) systems has been considered. The sphere decoder (SD) is one of the promising techniques to solve the ML problem. However the SD has a loose necessary condition for pruning branches, and it becomes impractical in large dimensional systems. We propose a Schnorr-Euchner SD with statistical pruning (SP-SESD) in order to further reduce complexity with small performance degradation. Squared statistical constraint radius (SCR) and expected partial path metric from unvisited levels are defined from statistics of noises, and two pruning conditions are jointly applied to search tree for detection efficiency. A flexible trade-off between bit error rate (BER) and complexity can be supported by selecting two pruning probabilities in the proposed scheme, and hence one can design various MIMO detectors according to system demands. Simulation results show the proposed SP-SESD requires lower computational complexity than any statistical pruning approaches, although performance degradation is negligible. The proposed algorithm is effective for MIMO systems with any number of antennas.

Lower Bound on Expected Complexity of Depth-First Tree Search with Multiple Radii

Depth-first tree search with multiple radii (DFTS-MR) algorithm attains significant complexity reduction over DFTS with a single radius (DFTS-SR) for solving integer least-squares (ILS) problems. Herein, we derive the lower bound on the expected complexity of DFTS-MR under i.i.d. complex Gaussian environments. Currently, the upper bound on the expected DFTS-MR complexity is known. Our analytical result shows the computational dependence on the statistics of the channel, the noise, and the transmitted symbols. It also reflects the use of multiple radii, which is one of the main characteristics of DFTS-MR. The resultant lower bound provides an efficient means to better understand the complexity behavior of DFTS-MR, along with the (known) upper bound.

A Near-ML Decoding with Improved Complexity over Wider Ranges of SNR and System Dimension in MIMO Systems

In this letter, we aim to present a near-maximum-likelihood (ML) decoding algorithm with low-complexity for wider ranges of SNR and system dimension in multiple-input-multiple-output (MIMO) systems. Based on the proposed radius design criterion, we introduce the effective radius (ER) which is determined using the statistics of path metric under correct and incorrect decoding cases. Since the constraint established by the ER maintains tightness during most search procedure, the proposed scheme further improves the complexity, and its performance loss is still negligible by properly selecting design probabilities.

Improved lattice-reduction-aided detectors over added search signals using quantization error for MIMO-OFDM systems

This paper describes approaches to the problem that lattice-reduction-aided (LRA) detectors have non-ideal decision region and detect outlying constellation points. The proposed approaches find the shortest distance from a received signal to added search signals using quantization error. Using these approaches, we can improve a bit error (BER) performance with a low complexity compared to full search maximum-likelihood (ML) detection.

Expected complexity analysis of increasing radii algorithm by considering multiple radius schedules

In this study, the authors investigate the expected complexity of increasing radii algorithm (IRA) in an independent and identified distributed Rayleigh fading multiple-input-multiple-output channel with additive Gaussian noise and then present its upper bound result. IRA employs several radii to yield significant complexity reduction over sphere decoding, whereas performing a near-maximum-likelihood detection. In contrast to the previous expected complexity presented by Gowaikar and Hassibi (2007), where the radius schedule was hypothetically fixed for analytic convenience, a new analytical result is obtained by considering the usage of multiple radius schedules. The authors analysis reflects the effect of the random variation in the radius schedule and thus provides a more reliable complexity estimation. The numerical results support their arguments, and the analytical results show good agreement with the simulation results.

Joint Selection with Multi-Streams for Multiuser MIMO Systems with Block Diagonalization

In this paper, we present a new scheme combining mode switching (SM/STBC), and transmit antenna subset selection (Tx-AnSS), i.e.,joint selection for a multiuser-MIMO (MU-MIMO) system with block diagonalization (BD) for error probability enhancement. Further, the proposed scheme also adaptively selects the number of streams by a simple selection criterion which is a square minimum Euclidean distance of the user with worst performance. Then, the proposed joint selection with multi-streams can take the advantages of spatial diversity improvement and array gain. Simulation results show that bit error rate (BER) of the proposed scheme outperforms the conventional schemes, which follows the SM-based scheme at low-SNR region and the STBC-based scheme at high-SNR region.

Adaptive interference suppression methods using transform domain approach in an on-board filter bank for satellite communications

It is necessary that the satellite transponder has ability to suppress interferences for reliable communications in real applications, specially military areas. Adaptive interference suppression methods using transform domain approach are presented for an on-board filter bank in this paper. The three kinds of algorithms to compute the threshold level are jointly evaluated with the notch filter or the clipper under different interfered environments. From simulation results, it is verified that the proposed methods are helpful to overcome the performance degradation due to interferences. Especially, Notch-Alg. 2 which uses the notch filter and the sample mean of energies of frequency bins shows the best bit error rate (BER) performance in all considered interferences.

Ordering aided Schnorr-Euchner sphere decoding with statistical pruning based on IRA for MIMO systems

A Schnorr-Euchner sphere decoder (SESD) with increasing radii algorithm (IRA) named as the IRA-SESD and two ordering preprocessing strategies are considered in this paper. Statistical constrain radii (SCRs) are obtained from probabilistic distribution of path metric in order to statistically prune branches. Ordering preprocessing schemes are jointly applied to further reduce computational complexity of the IRA-SESD. This ordering aided IRA-SESD presents near-ML performance with low complexity. The proposed scheme has been evaluated by computer simulations for uncoded multiple-input multiple-output (MIMO) systems.