Toward High-Performance Implementation of 5G SCMA Algorithms

Abstract

The recent evolution of mobile communication systems toward a 5G network is associated with the search for new types of non-orthogonal modulations such as sparse code multiple access (SCMA). Such modulations are proposed in response to demands for increasing the number of connected users. SCMA is a non-orthogonal multiple access technique that offers improved bit error rate performance and higher spectral efficiency than other comparable techniques, but these improvements come at the cost of complex decoders. There are many challenges in designing near-optimum high throughput SCMA decoders. This paper explores means to enhance the performance of SCMA decoders. To achieve this goal, various improvements to the MPA algorithms are proposed. They notably aim at adapting SCMA decoding to the single instruction multiple data paradigm. Approximate modeling of noise is performed to reduce the complexity of floating-point calculations. The effects of forwarding error corrections such as polar, turbo, and LDPC codes, as well as different ways of accessing memory and improving power efficiency of modified MPAs are investigated. The results show that the throughput of an SCMA decoder can be increased by 3.1 to 21 times when compared to the original MPA on different computing platforms using the suggested improvements. INDEX TERMS 5G, BER, exponential estimations, intel advanced vector extensions (AVX), iterative multiuser detection, knights corner instruction (KNCI), log-MPA, maximum likelihood (ML), message passing algorithm (MPA), power efficiency, SCMA, single instruction multiple data (SIMD), streaming SIMD extension (SSE).

Iterative Receiver Design for Polar-Coded SCMA Systems

“…Hence, for decoding a polar-coded SCMA system supporting K users each using E = 256 half-rate polar code, the decoding complexity C SLD per outer iteration is expressed as C SLD = 1.2 × 10 4 K. Furthermore, the number of flops required for GA-MP detection within a single outer iteration is calculated as C GA-MP = IF {U N d c [15M + 18(d c + 1)] + K(16d x + 2)} [52]. Then, the total complexity per iteration per time-slot is expressed in (38).…”

“…Previous work on the JDD of polar-coded systems is summarised in Table I [16,20,[32][33][34][35][36][37][38], which allows us the boldly and explicitly contrast our new contributions to the existing state-of-the-art. While these studies designed impressive JDD algorithms, there is a paucity of contributions on the convergence analysis, on the BER performance over fading channels and on complexity reduction techniques.…”

Sparse Code Multiple Access: Potentials and Challenges

“…Sparse code multiple access (SCMA) is among the CD-NOMA techniques that are considered by the new radio (NR) study in the 3GPP long-term evolution (LTE)-advanced, since it was among the most reliable multiple access candidates for 5G [11], [12]. The SCMA encoder directly maps the incoming bits of several users/streams to complex multi-dimensional codewords selected from a specific predefined sparse codebook set.…”

Improving Log-Likelihood Ratio Estimation with Bi-Gaussian Approximation under Multiuser Interference Scenarios

“…For such complicated networks, the overhead of acquiring the CSI of the interfering channel and the modulation and coding scheme (MCS) of interfering signals may be impractically high, implying that the joint detection or successive interference cancellation may not be possible at least for some situations, especially when the interfering station belongs to a different system or operator. In these cases, the global noise is typically treated as Gaussian noise [ 37 , 38 , 39 , 40 ]. However, since interfering signals are drawn from a finite constellation as the desired signal, the real distribution is definitely non-Gaussian.…”