An Application of Vector Coding with IBI Cancelling Demodulator and Code Elimination to Delay Spread MIMO Channels

2009 ◽  
Vol E92-B (6) ◽  
pp. 2153-2159 ◽  
Author(s):  
Zhao LI ◽  
Hiroshi FURUKAWA
Keyword(s):  
Delay Spread ◽  
Mimo Channels ◽  
Vector Coding

scholarly journals Low-Complexity Gaussian Detection for MIMO Systems

2010 ◽  
Vol 2010 ◽  
pp. 1-12 ◽  
Author(s):  
Tianbin Wo ◽  
Peter Adam Hoeher
Keyword(s):  
Computational Complexity ◽  
Likelihood Function ◽  
Gaussian Approximation ◽  
Mimo Systems ◽  
Low Complexity ◽  
Delay Spread ◽  
Data Detection ◽  
Mimo Channels ◽  
Interference Structure ◽  
Single Carrier

For single-carrier transmission over delay-spread multi-input multi-output (MIMO) channels, the computational complexity of the receiver is often considered as a bottleneck with respect to (w.r.t.) practical implementations. Multi-antenna interference (MAI) together with intersymbol interference (ISI) provides fundamental challenges for efficient and reliable data detection. In this paper, we carry out a systematic study on the interference structure of MIMO-ISI channels, and sequentially deduce three different Gaussian approximations to simplify the calculation of the global likelihood function. Using factor graphs as a general framework and applying the Gaussian approximation, three low-complexity iterative detection algorithms are derived, and their performances are compared by means of Monte Carlo simulations. After a careful inspection of their merits and demerits, we propose a graph-based iterative Gaussian detector (GIGD) for severely delay-spread MIMO channels. The GIGD is characterized by a strictly linear computational complexity w.r.t. the effective channel memory length, the number of transmit antennas, and the number of receive antennas. When the channel has a sparse ISI structure, the complexity of the GIGD is strictly proportional to the number of nonzero channel taps. Finally, the GIGD provides a near-optimum performance in terms of the bit error rate (BER) for repetition encoded MIMO systems.

Capacity Analysis for Correlated Multi-Hop MIMO Channels under Colored Noise

2015 ◽  
Vol 1 ◽  
pp. 41
Author(s):  
Nguyen N. Tran ◽  
Ha X. Nguyen
Keyword(s):  
Computational Complexity ◽  
Mutual Information ◽  
Closed Form Solution ◽  
Optimal Solution ◽  
Form Solution ◽  
Maximization Problem ◽  
Capacity Analysis ◽  
Mimo Channels ◽  
Average Mutual Information ◽  
Channel Output

A capacity analysis for generally correlated wireless multi-hop multi-input multi-output (MIMO) channels is presented in this paper. The channel at each hop is spatially correlated, the source symbols are mutually correlated, and the additive Gaussian noises are colored. First, by invoking Karush-Kuhn-Tucker condition for the optimality of convex programming, we derive the optimal source symbol covariance for the maximum mutual information between the channel input and the channel output when having the full knowledge of channel at the transmitter. Secondly, we formulate the average mutual information maximization problem when having only the channel statistics at the transmitter. Since this problem is almost impossible to be solved analytically, the numerical interior-point-method is employed to obtain the optimal solution. Furthermore, to reduce the computational complexity, an asymptotic closed-form solution is derived by maximizing an upper bound of the objective function. Simulation results show that the average mutual information obtained by the asymptotic design is very closed to that obtained by the optimal design, while saving a huge computational complexity.

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