An Information Theoretic Charachterization of Channel Shortening Receivers

Optimal data detection of data transmitted over a linear channel can always be implemented through the Viterbi algorithm (VA). However, in many cases of interest the memory of the channel prohibits application of the VA. A popular and conceptually simple method in this case, studied since the early 70s, is to first filter the received signal in order to shorten the memory of the channel, and then to apply a VA that operates with the shorter memory. We shall refer to this as a channel shortening (CS) receiver. Although studied for almost four decades, an information theoretic understanding of what such a simple receiver solution is actually doing is not available. In this paper we will show that an optimized CS receiver is implementing the chain rule of mutual information, but only up to the shortened memory that the receiver is operating with. Further, we will show that the tools for analyzing the ensuing achievable rates from an optimized CS receiver are precisely the same as those used for analyzing the achievable rates of a minimum mean square error (MMSE) receiver

Large-Scale MIMO Detection for 3GPP LTE: Algorithms and FPGA Implementations

Large-scale (or massive) multiple-input multiple-output (MIMO) is expected to be one of the key technologies in next-generation multi-user cellular systems, based on the upcoming 3GPP LTE Release 12 standard, for example. In this work, we propose - to the best of our knowledge - the first VLSI design enabling high-throughput data detection in single-carrier frequency-division multiple access (SC-FDMA)-based large-scale MIMO systems. We propose a new approximate matrix inversion algorithm relying on a Neumann series expansion, which substantially reduces the complexity of linear data detection. We analyze the associated error, and we compare its performance and complexity to those of an exact linear detector. We present corresponding VLSI architectures, which perform exact and approximate soft-output detection for large-scale MIMO systems with various antenna/user configurations. Reference implementation results for a Xilinx Virtex-7 XC7VX980T FPGA show that our designs are able to achieve more than 600 Mb/s for a 128 antenna, 8 user 3GPP LTE-based large-scale MIMO system. We finally provide a performance/complexity trade-off comparison using the presented FPGA designs, which reveals that the detector circuit of choice is determined by the ratio between BS antennas and users, as well as the desired error-rate performance.Comment: To appear in the IEEE Journal of Selected Topics in Signal Processin

Efficient detection algorithms for Multiple-Input Multiple-Output (MIMO) systems

[EN] In the last ten years, one of the most significant technological developments that will lead to the new broadband wireless generation is the communication via Multiple-Input Multiple-Output (MIMO) systems. MIMO systems are known to provide an increase of the maximum rate, reliability and coverage of current wireless communications. Maximum-Likelihood (detection over Gaussian MIMO channels is shown to get the lowest Bit Error Rate for a given scenario. However, it has a prohibitive complexity which grows exponentially with the number of transmit antennas and the size of the constellation. Motivated by this, there is a continuous search for computationally efficient optimal or suboptimal detectors. In this work, we carry out an state of the art review of detection algorithms and propose the combination of a suboptimal MIMO detector called K-Best Sphere Decoder with a channel matrix condition number estimator to obtain a versatile combined detector with predictable performance and suitable for hardware implementation. The effect of the channel matrix condition number in data detection is exploited in order to achieve a decoding complexity lower than the one of already proposed algorithms with similar performance. Some practical algorithms for finding the 2-norm condition number of a given channel matrix and for performing the threshold selection are also presented and their computational costs and accuracy are discussed[ES] Uno de los desarrollos tecnol'ogicos m'as significativos de la ' ultima d'ecada que llevar'an a la nueva generaci'on de banda ancha en movilidad es la comunicaci'on mediante sistemas de m' ultiples entradas y m' ultiples salidas (MIMO). Los sistemas MIMO proporcionan un notable incremento en la capacidad, fiabilidad y cobertura de las comunicaciones inal'ambricas actuales. Se puede demostrar que la detecci'on 'optima o dem'axima verosimilitud (ML) en canales MIMO Gaussianos proporciona la m'¿nima tasa de error de bit (BER) para un escenario dado pero tiene el inconveniente de que su complejidad crece exponencialmente con el n'umero de antenas y el tama¿no de la constelaci'on utilizada. Por este motivo, hay una cont'¿nua b' usqueda de detectores 'optimos o sub'optimos que sean m'as eficientes computacionalmente. En este trabajo, se ha llevado a cabo una revisi 'on del estado del arte de los principales algoritmos de detecci'on para sistemas MIMO y se ha propuesto la combinaci'on de un detector MIMO sub'optimo conocido como K-Best Sphere Decoder con un estimador del n'umero de condici'on de la matriz de canal, para conseguir un detector combinado basado en umbral con complejidad predecible y adecuado para implementaci'on en hardware. Se ha explotado el efecto del n'umero de condici'on en la detecci'on de datos para disminuir la complejidad de los algoritmos de detecci 'on existentes sin apenas alterar sus prestaciones. Por ' ultimo tambi'en se presentan distintos algoritmos pr'acticos para encontrar el dos n'umero de condici'on as'¿ como para realizar la selecci 'on del umbral.Roger Varea, S. (2008). Efficient detection algorithms for Multiple-Input Multiple-Output (MIMO) systems. http://hdl.handle.net/10251/12200Archivo delegad

Efficient Detectors for MIMO-OFDM Systems under Spatial Correlation Antenna Arrays

This work analyzes the performance of the implementable detectors for multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) technique under specific and realistic operation system condi- tions, including antenna correlation and array configuration. Time-domain channel model has been used to evaluate the system performance under realistic communication channel and system scenarios, including different channel correlation, modulation order and antenna arrays configurations. A bunch of MIMO-OFDM detectors were analyzed for the purpose of achieve high performance combined with high capacity systems and manageable computational complexity. Numerical Monte-Carlo simulations (MCS) demonstrate the channel selectivity effect, while the impact of the number of antennas, adoption of linear against heuristic-based detection schemes, and the spatial correlation effect under linear and planar antenna arrays are analyzed in the MIMO-OFDM context.Comment: 26 pgs, 16 figures and 5 table

Limiting Performance of Conventional and Widely Linear DFT-precoded-OFDM Receivers in Wideband Frequency Selective Channels

This paper describes the limiting behavior of linear and decision feedback equalizers (DFEs) in single/multiple antenna systems employing real/complex-valued modulation alphabets. The wideband frequency selective channel is modeled using a Rayleigh fading channel model with infinite number of time domain channel taps. Using this model, we show that the considered equalizers offer a fixed post signal-to-noise-ratio (post-SNR) at the equalizer output that is close to the matched filter bound (MFB). General expressions for the post-SNR are obtained for zero-forcing (ZF) based conventional receivers as well as for the case of receivers employing widely linear (WL) processing. Simulation is used to study the bit error rate (BER) performance of both MMSE and ZF based receivers. Results show that the considered receivers advantageously exploit the rich frequency selective channel to mitigate both fading and inter-symbol-interference (ISI) while offering a performance comparable to the MFB