The massive surge in device connectivity demands higher data rates, increased capacity with low latency and high throughput. Hence, to provide ultra-reliable, low-latency communication with ubiquitous connectivity for Internet-of-Things (IoT) devices, next-generation wireless communication leverages the incorporation of machine learning tools. However, standard data-driven models often need large datasets and lack interpretability. To overcome this, model-driven deep learning (DL) approaches combine domain knowledge with learning to improve accuracy and efficiency. Deep unfolding is a model-driven method that turns iterative algorithms into deep neural network (DNN) layers. It keeps the structure of traditional algorithms while allowing end-to-end learning. This makes deep unfolding both interpretable and effective for solving complex signal processing problems in wireless systems. We first present a brief overview of the general architecture of deep unfolding to provide a solid foundation. We also provide an example to outline the steps involved in unfolding a conventional iterative algorithm. We then explore the application of deep unfolding in key areas, including signal detection, channel estimation, beamforming design, decoding for error-correcting codes, integrated sensing and communication, power allocation, and physical layer security. Each section focuses on a specific task, highlighting its significance in emerging 6G technologies and reviewing recent advancements in deep unfolding-based solutions. Finally, we discuss the challenges associated with developing deep unfolding techniques and propose potential improvements to enhance their applicability across diverse wireless communication scenarios.

This paper proposes, for the first time, a hybrid multiple access framework that integrates the principles of rate-splitting (RS) and sparse code multiple access (SCMA) in an SISO downlink scenario. The proposed scheme, termed RS-SCMA, unifies the powerful interference management capability of rate-splitting multiple access (RSMA) with the near-optimal multiuser detection of SCMA. A key feature of RS-SCMA is a tunable splitting factor α, which governs the allocation between the genericM-ary modulated common messages and SCMA-encoded private messages. This enables dynamic control over the fundamental trade-off between system sum-rate, bit error rate (BER), and the overloading factor. We develop novel transmitter and receiver architectures based on soft successive interference cancellation (SIC), incorporating message passing algorithm (MPA) detection and soft-symbol reconstruction. Furthermore, a unified analytical expression for the achievable sum-rate is derived as a function of the splitting factor α. The performance of the proposed RS-SCMA system is evaluated in terms of both BER and sum-rate. Simulation results confirm the superiority of RS-SCMA over conventional SCMA and multi-carrier RSMA, demonstrating its scalability and robustness even in the presence of channel estimation errors.

Intelligent reflecting surfaces (IRS) have emerged as a transformative technology for enhancing wireless communication by dynamically reconfiguring the propagation environment. Additionally, orthogonal time frequency space (OTFS) has been recognized as a promising waveform for high-mobility scenarios. In this work, we investigate the joint optimization of beamforming at the base station (BS) and phase shifts at the IRS in an IRS-aided multiple-input single-output (MISO) OTFS system. The objective is to maximize the achievable sum rate under practical constraints, resulting in a highly non-convex optimization problem involving beamforming and phase shifts. To address this challenge, we propose a Bayesian neural network (BNN) based approach that models uncertainties in channel state information (CSI) and optimally learns both the beam-forming vector and IRS phase shift matrix. The BNN employs a probabilistic weight distribution, enabling robust optimization through stochastic gradient-based updates. Furthermore, we leverage Monte Carlo sampling to account for uncertainties in the learned parameters, ensuring improved performance and robustness under dynamic channel conditions. Simulation results demonstrate that the proposed BNN-based optimization outperforms conventional methods, such as alternate optimization (AO), by achieving higher capacity. Additionally, its probabilistic framework enables robustness to variations in CSI, making it well-suited for dynamic channel conditions.

Orthogonal time-frequency space (OTFS) modulation is a promising technique for next-generation wireless communication, particularly in high-mobility scenarios. However, the performance of the OTFS modulation is degraded by hardware impairments (HIs), particularly at high frequencies and mobility. Thus, efficient signal detection is essential for reliable OTFS-based communication. To overcome this, various detectors, including convolutional neural networks (CNN), minimum mean square error (MMSE), and message passing (MP), have been explored in the literature, with CNN-based detectors demonstrating superior performance over MMSE and MP. In this work, we propose a residual channel attention network (RCAN) to robustly detect OTFS signal in the presence of HIs. Using deep residual learning and channel attention mechanisms to improve detection accuracy under varying channel conditions. Extensive simulations, considering different OTFS frame sizes, user mobility levels, and HI parameters, confirm that the proposed RCAN consistently outperforms conventional detectors, including CNN, in all scenarios evaluated. This makes RCAN a highly effective solution for OTFS-based high-mobility wireless communication systems, both with and without HI.

Terahertz (THz) wireless communication is a promising technique to meet the rising demand for high-bandwidth services. Propagation of the $\mathbf{T H z}$ waves through the atmosphere is influenced by factors like attenuation, water vapour, weather, turbulence, rain and beam misalignment. Furthermore, performance of a THz link is severely degraded by non-linear hardware impairments introduced by power amplifier (PA). Conventional estimators such as zero-forcing (ZF) and minimum mean squared error (MMSE) fall short of handling beam misalignment, rain attenuation, and non-linearity. To circumvent this limitation, we propose a deep neural network (DNN) based receiver for THz communication systems in the presence of PA non-linearity. Simulation results for bit error rate (BER) performance considering non-linear distortion due to power amplifier indicate that the proposed deep learning (DL) based receiver performs better in terms of BER and is more robust compared to ZF, MMSE and orthogonal matching pursuit (OMP) based receiver.

Efficient data transmission is one of the most important features that any technology should possess. However, this efficiency should be achieved within the available resources, which in this context is energy. One method to achieve it is the use of hexagonal quadrature amplitude modulation (H-QAM). The recently proposed Orthogonal Time Frequency Space (OTFS) modulation is considered for high mobility communication scenarios over Orthogonal Frequency Division Multiplexing (OFDM) due to its unique multiplexing capabilities in the delay-Doppler (DD) domain. Nevertheless, there is still room for advancement in OTFS system design. In this paper, our objective is to enhance the coding gain and shaping gain of OTFS systems, which can complement the existing advantages of OTFS technology. To achieve this, we explore square quadrature amplitude modulation (S-QAM) and H-QAM models and calculate and compare their shaping and coding gains, supported by theoretical analysis. Our simulations demonstrate that H-QAM outperforms S-QAM, primarily because H-QAM provides greater tolerance to noise by expanding the Voronoi region. We analyze the impact of number of delay and Doppler bins on the performance of the OTFS for various modulation orders. Additionally we also find the closed form of bit error rate (BER) by considering the nature of the eigenvalues of the channel matrix. A comparison of peak-to-average power ratio (PAPR) is also done.

Orthogonal time frequency space (OTFS) modulation is positioned to be a potential waveform for 6G communications, owing to its capability to multiplex data in the delay-Doppler (DD) domain, making it robust against doubly-selective wireless communications channels. OTFS leverages a 2D transformation that effectively transform a doubly-dispersive channel into one with minimal fading characteristics. In this paper, we develop a deep learning-based signal detector for a MIMO-based OTFS system (termed as DL-MOTFS) with hardware impairments (HIs). We assess the performance of the proposed DL-MOTFS for a range of system parameters, including those related to HIs, user velocity, OTFS frame size, and the number of transmit and receive antennas. Through comprehensive comparisons against the conventional MMSE (minimum mean square error) signal detector, we find that the proposed DL-MOTFS consistently outperforms the MMSE detector, exhibiting a performance gain of around 3–5 dB.

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 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.

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.

We present a novel successive cancellation list (SCL) decoding method for the polar codes based on the mutual-information values of the bit-channels. At short-to-medium blocklengths, only a certain fraction of the bit-channels get polarized. Usually the mutual-information values for the bit- channels are computed for the construction of the code. Using these available mutual-information values, we identify the information bits exhibiting full polarization and having high channel reliability. In the proposed mutual- information-based SCL (MI-SCL) decoder, only one emanating path is considered for such bits. This results in the reduction of the width of the decoding tree. Simulation results confirm the impressive performance of the MI-SCL decoder specially with concatenated cyclic redundancy check codes.

This paper presents a method to compare the detrimental effect of different trapping sets in the LDPC codes for an AWGN channel. The messages from the check nodes in the trapping set induced sub-graph are divided into two groups: one from the mis-satisfied check nodes and the other from the unsatisfied check nodes. The message densities for these two groups are computed to find out the joint probability of all the variable nodes in the trapping set being in error in the successive iterations. These joint probabilities are used to compare the detrimental effects of different trapping sets.