Abstract
A number of study results demonstrated that the performance of the coded MIMO scheme can be highly enhanced by incorporating iterative decoding and detection scheme by exchanging soft information between the symbol detector and decoder. One of the critical problems of these iterative schemes is an exponential order of the complexity with increase of number of bits in a symbol and the number of antennas. In this paper, we present an efficient iterative detection and decoding scheme for MIMO systems with phase shift keying (PSK) modulation schemes and low density parity check (LDPC) codes. In order to reduce the complexity by the number of antennas, we adopt minimum mean square error (MMSE) based linear detection scheme with parallel interference cancellation. In addition, soft bit estimation is made only with a single distance calculation per bit, with approximating performance to the maximum likelihood detection1.
Highlights
There have been a number of research studies on the development of detection schemes for multi-input multi-output (MIMO) systems, in order to achieve a capacity approaching performance
We present an efficient iterative detection and decoding scheme for MIMO systems with phase shift keying (PSK) modulation schemes and low density parity check (LDPC) codes
The parallel interference cancellation with minimum mean square error (MMSE) (PIC-MMSE) MIMO detection schemes were proposed in order to enhance the performance as well as the computational efficiency compared to the conventional MMSE-based scheme [4] [5] [6]
Summary
There have been a number of research studies on the development of detection schemes for multi-input multi-output (MIMO) systems, in order to achieve a capacity approaching performance. The parallel interference cancellation with MMSE (PIC-MMSE) MIMO detection schemes were proposed in order to enhance the performance as well as the computational efficiency compared to the conventional MMSE-based scheme [4] [5] [6]. We first normalize the detected symbol from the PIC-MMSE filtering process, and map it to a specific region, so that SBI estimation can be made with a single distance calculation [7] By this way, overall complexity is in a linear order, and it can be applied to a massive MIMO system, i.e., even the number of antennas are greater than a few tens.
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