Discrete Fourier Transform Spread-Orthogonal Frequency Division Multiplexing (DFT-s-OFDM) is a promising waveform for both access and backhaul communication in modern wireless systems, due to its low peak-to-average power ratio. However, its performance is impacted by hardware impairments, particularly oscillator phase noise (PN). This paper presents tailored solutions for mitigating PN in DFT-s-OFDM systems, with distinct approaches for the access layer and backhaul communication. For the access layer, we use CoDiPhy, a deep learning (DL)-based receiver that jointly performs channel estimation, equalization, and PN compensation via a conditional denoising diffusion model. As CoDiPhy’s complexity increases with larger FFT and constellation sizes typical in backhaul scenarios, we propose a DL-aided (DLA) PN compensation approach to address the PN issue while reducing computational burden. The DLA PN method outperforms traditional linear interpolation (LI) by exploiting information from all received signals within a pilot section. Simulation results show that CoDiPhy achieves near-optimal performance in the access layer, with coded BERs within 0.2 dB (at a 10−6BER) of the ideal LMMSE solution. In the backhaul scenario, the DLA PN scheme significantly outperforms LI, enabling 1024-QAM with less than 0.5% pilot overhead.

The utilization of Orbital Angular Momentum (OAM) mode multiplexing offers a promising solution to alleviate the signal processing overhead in line-of-sight communication systems. However, concerns regarding the divergence of OAM beams have raised doubts about its practicality over long transmission distances. In this paper, we propose a novel beam steering strategy aimed at mitigating this issue by ensuring the successful reception of all OAM modes. To accomplish this objective, we present an unsupervised learning-based beam steering approach designed to individually steer each mode, thereby facilitating their reception at the receiver. We refer to this approach as Steered OAM (S-OAM). To mitigate inter-mode interference (IMI) from the steering process, we incorporate an equalizer into both the receiver and the training process. This ensures that our proposed S-OAM system is IMI-aware, improving its performance across steered OAM modes. Using S-OAM provides the flexibility of choosing the number of transmitted modes to accommodate low-capacity scenarios in low signal-to-noise ratio or long transmission distances, where a large number of modes lead to weak streams. Furthermore, the proposed approach leverages codebook-based phase shifters at the transmitter, eliminating the need for real-time calculation of phase shifter values and thereby maintaining a low level of signal processing burden at the transmit side, akin to traditional OAM transmission. Simulation results validate the efficacy of S-OAM, demonstrating its capability to generate multiple equally robust streams at the receiver with a total spectral efficiency reaching up to $0.5 \mathrm{bits} / \mathrm{sec} / \mathrm{Hz}$ from the theoretical upper bound.

This paper introduces a novel Model-Driven Deep Learning Assisted (MDLA) approach for phase noise (PN) estimation in wideband wireless backhaul scenarios. MDLA estimation combines a model-driven foundation for problem formulation with the computational power of Convolutional Neural Networks (CNN) to tackle the derived problem effectively. Exploiting inherent inter-correlations among PN-affected signals, MDLA method incorporates preceding and subsequent received signals in the PN estimation process. Moreover, a dedicated neural network is crafted to capitalize on input dependencies. This hybrid methodology demonstrates superior performance across diverse wideband scenarios, including constellation sizes, pilot overhead ratios, PN levels, and signal-to-noise ratios. The proposed MDLA scheme exhibits robustness in the face of reduced pilot overhead and achieves nearly optimal results in mitigating PN effects. In a 2 GHz bandwidth system with strong PN, MDLA remains within 0.2 dB and 0.5 dB of the PN -free system in 64-QAM and 256-QAM scenarios, respectively, at a coded bit-error-rate of $10^{-6}$.

This study presents an End-to-End Learning (ETEL) architecture designed for wireless backhaul links, tackling hardware-induced impairments such as Power Amplifier Non-Linearity (PA NL) and phase noise. Leveraging geometric constellation shaping, a fully connected neural network (NN) at the transmitter, and a residual-network-based convolutional neural network at the receiver, our solution effectively handles challenges posed by higher-order modulation and diverse NLs. Through joint transceiver NN training with a weighted loss function accounting for throughput, PA linearization, and spectrum mask requirements, our architecture outperforms conventional schemes in strong NL scenarios, supporting constellation sizes up to 256. This positions the proposed ETEL solution as a promising candidate for future backhaul links operating at mmW or sub-THz bands envisioned for 6G networks.

In this paper, the performance of orbital angular momentum (OAM) mode multiplexing systems is investigated in the presence of oscillator phase noise (PN). First, the OAM spectrum is calculated for an OAM carrying beam that is generated by a uniform circular array (UCA) connected to an imperfect oscillator. It is shown that the OAM mode purity is insensitive to PN. Subsequently, the system model of the UCA-based OAM multiplexing in the presence of PN is presented. Using this model, the signal-to-interference-plus-noise ratio (SINR) and the sum-rate of the system are derived as a function of PN statistics. The significant reduction in the system throughput due to the PN confirms the necessity of an effective PN mitigation scheme. Hence, the system model after applying a generic pilot-based PN mitigation is also investigated, and the SINR and the sum-rate after PN compensation are analytically derived. Simulation results, generated for various PN models, reveal the existing design trade-offs between the pilot overhead and the sum-rate in these systems.

In this paper, we present an algorithm to construct a misalignment-robust beamforming (BF) codebook for orbital angular momentum mode multiplexing (OAM-MM) systems. The BF codebook is constructed for a given set of antenna misalignment (AM) parameters, i.e., the AM type, rotation angles, and displacement values. We use the sorted QR-decomposition of the augmented misaligned channel matrix to design a BF codebook that is applied to received signals using phase shifters only. Subsequently, by employing the successive interference cancellation (SIC), the transmitted symbols corresponding to each OAM mode are estimated. The combination of codebook-based BF in the analog domain and the SIC in the digital domain offers an effective strategy to solve the AM problem without sacrificing the computational savings associated with the application of OAM-MM. Simulation results confirm that by adopting the proposed approach, the sum-rate of the misaligned OAM-MM system approaches the theoretical upper bound.

One of the key weaknesses of low-density parity-check (LDPC) codes is the error floor that they typically exhibit at high signal-to-noise ratios (SNRs). Such an error floor is usually attributed to problematic structures known as trapping sets (TSs). The overwhelming majority of existing error floor estimation schemes consider the case of binary phase shift keying (BPSK) signalling. Unfortunately, these schemes are not readily extensible to estimate the error floor of high order LDPC coded modulation systems considered herein. To provide such a scheme, in this work, we use mean-shift importance sampling (MS-IS) to develop a novel error floor estimation methodology for high-order pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) LDPC coded systems. First, a computationally efficient graphical-based approach is used to identify the TSs of a given LDPC code. Subsequently, a novel analytical approach is devised to identify the TSs that are likely to have a higher contribution in the error floor. These TSs are referred to as potentially dominant TSs (PDTSs). Finally, a new methodology for categorizing the PDTSs into equivalence classes is developed. A representative PDTS of each equivalence class is chosen and an MS-IS framework is devised to obtain the error rate corresponding to each equivalence class. To arrive at the desired MS-IS scheme, we develop an algorithm that invokes the geometry of the constellation to determine the MS value. In contrast with the conventional MS-IS method used in BPSK signalling, in the proposed MS-IS scheme, the MS value is a variable that is determined based on the TS and the transmitted codeword. The computational complexity of the three main steps of our methodology, viz. extracting the PDTSs, determining the MS values, and applying the MS-IS scheme, depends merely on the size of the constellation and the structure of the code, but not on the SNR. Numerical simulations confirm the efficacy and accuracy of the proposed technique at different SNRs.

The oscillator phase noise (PN) is one of the bottlenecks toward increasing the modulation order in the OFDM-based wireless backhaul links. This paper presents an algorithm to overcome this barrier through a novel time-domain iterative phase noise mitigation method. In the first step, by leveraging the high signal-to-noise ratio of the backhaul links, the PN estimation problem in the OFDM system is formulated similar to the PN estimation in the single carrier system, for which several solutions are proposed in the literature. Subsequently, a decision directed approach is used to solve this estimation problem, which relies on the linear interpolation of refined estimates of the PN. The proposed algorithm secures a significant improvement in the phase noise suppression of the OFDM systems in the high signal-to-noise-ratio regime. By requiring small pilot overhead while avoiding computationally complex operations in the algorithm's iterations, the proposed method is tailored specially for the OFDM-based wireless backhaul links. Numerical results verify the effectiveness of proposed PN mitigation scheme.

This paper presents a novel log-likelihood ratio (LLR) calculation for high order coded modulation schemes over an additive white Gaussian noise channel at the presence of residual phase noise (RPN). Residual phase noise is known to significantly degrade the error rate performance of such systems, particularly at lower error rates, resulting in an early error floor. To model RPN, we consider the commonly used pilot symbol assisted modulation schemes for carrier recovery. We derive the exact formula for the calculation of LLR for such systems. To simplify the implementation, we also derive an approximation of LLR which reduces the complexity significantly with almost no loss in performance. The simulation results are presented for coded modulation schemes based on quadrature amplitude modulations and low-density parity-check codes. The simulations demonstrate significant performance improvement in the error rate as a result of using the new LLR calculation instead of the conventional calculation of LLR which ignores the RPN.