Iterative decoding for MIMO channels via modified sphere decoding

In recent years, soft iterative decoding techniques have been shown to greatly improve the bit error rate performance of various communication systems. For multiantenna systems employing space-time codes, however, it is not clear what is the best way to obtain the soft information required of the iterative scheme with low complexity. In this paper, we propose a modification of the Fincke-Pohst (sphere decoding) algorithm to estimate the maximum a posteriori probability of the received symbol sequence. The new algorithm solves a nonlinear integer least squares problem and, over a wide range of rates and signal-to-noise ratios, has polynomial-time complexity. Performance of the algorithm, combined with convolutional, turbo, and low-density parity check codes, is demonstrated on several multiantenna channels. The results for systems that employ space-time modulation schemes seem to indicate that the best performing schemes are those that support the highest mutual information between the transmitted and received signals, rather than the best diversity gain

Analysis of Single RF Performance on MIMO-OFDM System Using Turbo Code and V-BLAST MMSE Detection

Along with the passing time and recent technology, the advancement of information technology has been increased in the wireless technology. The common methods that are used in this wireless communication are MIMO (Multiple Input Multiple Output) and OFDM (Orthogonal Frequency Division Multiplexing). MIMO is a system stands for a couple antenna on the transmitter and receiver which are working on themultipath component. While OFDM (Orthogonal Frequency Division Multiplexing) is a transmission method using multicarrier technique, dividing spectrum frequency into a couple subcarrier. The combination of MIMO and OFDM results in a high-speed transfer data system. The Single RF has reduced the USAge of RF Front-End into a bigger matrix size in the conventional MIMO system. This final project will discuss about the Single RF system of MIMO-OFDM with the V-BLAST (Vertical Bell Laboratories Space-Time) and MMSE (Minimum Mean Square Error) detectionwhich is used to remove ISI (Intersymbol Interference) combined with theTurbo Code,where theTurbo Encoder that lies on the transmitter side is also theTurbo Decoder in the receiver side. MIMO-OFDM utilizesthe Single RF (Radio Frequency) basis. The test on this final project will include a Single RF antenna on the MIMO-OFDM system, MIMO-OFDM with the V-BLAST detector and MMSE MIMO-OFDM with the Turbo Code, by using 64 QAM modulation. The expected result is the analysis performance of the Single RF on the MIMO-OFDM system using Turbo Code and V-BLAST MMSEDetection. The system will be shown on theBit Error Rate (BER) toward the Signal to Noise Ratio (SNR)

Multiuser MIMO-OFDM for Next-Generation Wireless Systems

This overview portrays the 40-year evolution of orthogonal frequency division multiplexing (OFDM) research. The amelioration of powerful multicarrier OFDM arrangements with multiple-input multiple-output (MIMO) systems has numerous benefits, which are detailed in this treatise. We continue by highlighting the limitations of conventional detection and channel estimation techniques designed for multiuser MIMO OFDM systems in the so-called rank-deficient scenarios, where the number of users supported or the number of transmit antennas employed exceeds the number of receiver antennas. This is often encountered in practice, unless we limit the number of users granted access in the base station’s or radio port’s coverage area. Following a historical perspective on the associated design problems and their state-of-the-art solutions, the second half of this treatise details a range of classic multiuser detectors (MUDs) designed for MIMO-OFDM systems and characterizes their achievable performance. A further section aims for identifying novel cutting-edge genetic algorithm (GA)-aided detector solutions, which have found numerous applications in wireless communications in recent years. In an effort to stimulate the cross pollination of ideas across the machine learning, optimization, signal processing, and wireless communications research communities, we will review the broadly applicable principles of various GA-assisted optimization techniques, which were recently proposed also for employment inmultiuser MIMO OFDM. In order to stimulate new research, we demonstrate that the family of GA-aided MUDs is capable of achieving a near-optimum performance at the cost of a significantly lower computational complexity than that imposed by their optimum maximum-likelihood (ML) MUD aided counterparts. The paper is concluded by outlining a range of future research options that may find their way into next-generation wireless systems