Iterative Detection and Decoding for Cell-Free Massive MIMO Using LDPC Codes

Abstract

This paper proposes an iterative detection and decoding (IDD) scheme for a cell free massive multiple input multiple output (CF-mMIMO) system. Users send coded data to the access points (APs), which is jointly detected at central processing unit (CPU). The symbols are exchanged iteratively in the form of log likelihood ratios (LLRs) between the detector and the lowdensity parity check codes (LPDC) decoder, increasing the coded system's performance. We propose a list-based multi-feedback diversity with successive interference cancellation (MF-SIC) to improve the performance of the CF-mMIMO. Furthermore, the proposed detector is compared with the parallel interference cancellation (PIC) and MF-PIC schemes. Finally, the bit error rate (BER) performance of CF-mMIMO is compared with the co-located mMIMO (Col-mMIMO).

Centralized and Decentralized IDD Schemes for Cell-Free Massive MIMO Systems: AP Selection and LLR Refinement

“…In the literature, few works are devoted to the bit error rate (BER) performance analysis for coded CF-mMIMO networks using message passing for various implementation architectures and cooperation levels [32]- [34]. There is still a need for low-complexity LLR processing schemes for decentralized processing in CF-mMIMO networks to facilitate BER performance close to that of the centralized schemes.…”

“…The receiver consists of an MMSE filter followed by a soft interference cancellation scheme, which may use either a successive or a list-based successive interference cancellation technique. The receiver first creates soft estimates of the transmitted symbols by computing the jth UE symbol mean sj based on the soft beliefs from the LDPC decoder [13], [32], [33] given E {s j } = sj as described by sj = s∈A sP(s j = s), (8) where A is the set of complex constellations. The a-priori probability of the extrinsic LLRs is given by [32], [33]…”

“…The receiver first creates soft estimates of the transmitted symbols by computing the jth UE symbol mean sj based on the soft beliefs from the LDPC decoder [13], [32], [33] given E {s j } = sj as described by sj = s∈A sP(s j = s), (8) where A is the set of complex constellations. The a-priori probability of the extrinsic LLRs is given by [32], [33]…”

“…where Λ i (b i ) is the extrinsic LLR of the i-th bit calculated by the LDPC decoder from the previous iteration, and s bl ∈ (+1, −1) denotes the l-th bit of symbol s. The variance of the j-th UE symbol is calculated as [32], [33]…”

“…In this section, we detail the proposed list-based detection scheme shown in Figure 3, which is similar to the one adopted in our previous papers [32], [33]. The design takes advantage of list feedback (LF) diversity by selecting a list of constellation candidates if there is the unreliability of the previously detected symbols [32], [33]. A shadow area constraint (SAC) is employed to obtain an optimal feedback candidate.…”