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Orthogonal time frequency space (OTFS) has emerged as the most sought-after modulation technique in a high mobility scenario. Sparse code multiple access (SCMA) is an attractive code-domain non-orthogonal multiple access (NOMA) technique. Recently a code-domain NOMA approach for OTFS, named OTFS-SCMA, is proposed. OTFS-SCMA is a promising framework that meets the demands of high mobility and massive connectivity. This paper presents a channel estimation technique based on the convolutional sparse coding (CSC) approach for OTFS-SCMA in the uplink. The channel estimation task is formulated as a CSC problem following a careful rearrangement of the OTFS input-output relation. We use an embedded pilot-aided sparse-pilot structure that enjoys the features of both OTFS and SCMA. The existing channel estimation techniques for OTFS in multi-user scenarios for uplink demand extremely high overhead for pilot and guard symbols, proportional to the number of users. The proposed method maintains a minimal overhead equivalent to a single user without compromising on the estimation error. The results show that the proposed channel estimation algorithm is very efficient in bit error rate (BER), normalized mean square error (NMSE), and spectral efficiency (SE). [ABSTRACT FROM AUTHOR]

Summary: Recently, intelligent reflecting surface (IRS)‐based communication systems are analyzed for 6G and beyond wireless networks. In this paper, we propose an IRS‐assisted downlink nonorthogonal multiple access (NOMA) system by considering separate IRS for near users (NUs) and far users (FUs). The optimal power allocation strategy among NUs and FUs is formulated for the proposed downlink IRS‐NOMA system. The symbol error probability and the sum‐rate performance of the IRS‐NOMA system are analyzed. Further, the impact of the modulation order, the number of reflecting surfaces, the number of phase shifts of reflected signals on symbol error, and power allocation between NU and FU is studied. [ABSTRACT FROM AUTHOR]

Orthogonal time frequency space (OTFS) modulation is a two-dimensional (2-D) modulation technique that has the potential to overcome the challenges faced by orthogonal frequency division multiplexing (OFDM) in high Doppler environments. The performance of OTFS in a multi-user scenario with orthogonal multiple access (OMA) techniques has been impressive. Due to the requirement of massive connectivity in 5G and beyond, it is essential to devise and examine the OTFS system with the existing non-orthogonal multiple access (NOMA) techniques. This paper proposes a multi-user OTFS system based on a code-domain NOMA technique called sparse code multiple access (SCMA). This system is referred to as the OTFS-SCMA model. The framework for OTFS-SCMA is designed for both downlink and uplink. First, the sparse SCMA codewords are strategically placed on the delay-Doppler plane. The overall overloading factor of the OTFS-SCMA system is equal to that of the underlying basic SCMA system. The receiver in downlink performs the detection in two sequential phases: first, the conventional OTFS detection using the method of linear minimum mean square error (LMMSE) estimation, and then the SCMA detection. We propose a single-phase detector based on a message-passing algorithm (MPA) to detect multiple users’ symbols for the uplink. The expressions for the asymptotic diversity orders of the proposed OTFS-SCMA system are derived for downlink and uplink. OTFS-SCMA provides a significant diversity gain over other multiple access systems for OTFS. Based on the diversity analysis, an algorithm is proposed to devise an optimal codeword allocation scheme. The performance of the proposed OTFS-SCMA system is validated through extensive simulations both in downlink and uplink. We consider delay-Doppler planes of different parameters and various SCMA systems of overloading factor up to 200%. The performance of OTFS-SCMA is compared with those of the existing OTFS-OMA, OFDM-SCMA and OTFS-power-domain (PD)-NOMA techniques. The analysis of OTFS-SCMA with channel estimation is also presented along with the BER performance. [ABSTRACT FROM AUTHOR]

This paper presents a sparse code multiple access (SCMA) system with massive antennas at the base station. This system is referred to as M‐SCMA system. A spectrally‐efficient and massive access next‐generation wireless network is realized through massive antennas and non‐orthogonal SCMA techniques. Two detection algorithms, namely, modified message passing algorithm (MMPA) and extended message passing algorithm (EMPA) are proposed to detect multiple users' symbols in M‐SCMA. A deep learning (DL)‐based detection scheme is also proposed for M‐SCMA so as to avoid channel estimation and to lower the detection complexity. Numerical results show that the DL‐based detection has similar performance as MMPA even when the channel information is not estimated explicitly. Furthermore, authors also establish the sum rate trade‐off between SCMA and orthogonal multiple access in a massive antenna system. The impact of various M‐SCMA parameters such as the number of antennas and the overloading factor, on the proposed DL, MMPA, and EMPA‐based detection are also investigated. [ABSTRACT FROM AUTHOR]

Summary: Paper has considered multiple reconfigurable intelligent surfaces (RISs) assisted downlink non‐orthogonal multiple accesses (NOMA) system, referred to as MR‐NOMA. Multiple RISs create a line‐of‐sight (LOS) channel between BS and users to improve network coverage, especially in high frequencies and severe path attenuation scenarios. In MR‐NOMA, all RISs are in series and activated simultaneously; therefore, multiplicative beamforming gain from the inter‐RIS channels is achieved. Bit error probability (BEP) under the Rician fading channel is derived and compared with simulation results. It is observed that BEP performance depends on the number of passive reflecting elements, Rician factor, and positions of RISs panels in the MR‐NOMA. [ABSTRACT FROM AUTHOR]