Abstracts:In this letter, a cascaded reconfigurable intelligent surfaces (RIS)-assisted two-way communication system is proposed, and its performance is explored under $\kappa -\mu $ fading channels, supporting simultaneous information exchange between the transmitter and receiver. To more accurately reflect practical communication scenarios, the impacts of transceiver hardware impairments (HWI), phase errors, path loss, and loop interference (LI) are taken into account. Approximate closed-form expressions for the outage probability (OP) and ergodic capacity (EC) are derived, and the accuracy of the theoretical formulas is validated through Monte Carlo simulations. The results demonstrate that the effects of HWI, phase errors, path loss, and LI degrade the communication performance of the system. In addition, the EC of the proposed system is improved by approximately twice the one-way system, highlighting the significant advantage of two-way communication in enhancing spectral efficiency.

Abstracts:The index coding problem aims to optimise broadcast communication by exploring side information available at the receivers. In this work, we investigate the use of Construction $\pi _{A}$ lattices over the ring of integers $\mathbb {Z}$ for index coding and establish algebraic conditions on the code generators to guarantee uniform side information gain. The effectiveness of the proposed approach is demonstrated through theoretical analysis and explicit code design examples.

Abstracts:Many optimization frameworks and mathematical models have been proposed for standalone optical and radio frequency (RF) wireless networks, as well as their integration. These models typically discretize the time horizon into fixed intervals, inherently introducing spatial discretization when network nodes are mobile. Spatial discretization is also widely applied in reinforcement learning approaches. While temporal and spatial discretizations reduce computational complexity, they may introduce inaccuracies, especially in highly dynamic systems. This letter presents analytical upper bounds for the relative deviation in signal-to-noise ratio (SNR) in both optical wireless communication (OWC) and RF, focusing on how grid granularity affects SNR accuracy through theoretical analysis. The results show that, under identical grid conditions and ideal node orientation, OWC experiences up to 45% higher relative SNR deviation than RF. Furthermore, an upper bound is derived as a function of node velocity and time interval, indicating that OWC requires 31% shorter time intervals than RF to achieve comparable SNR accuracy. Simulations validate the model, confirming that the theoretical upper bounds closely align with empirical results.

Abstracts:Accurate channel estimation is essential for enabling reliable communication in millimeter-wave (mmWave) multiple input multiple output (MIMO) systems. This letter presents a novel approach that combines pseudo-inverse-based hard thresholding (PIHT) with particle swarm optimization (PSO) to enhance the accuracy of channel estimation. The proposed method capitalizes on the faster ability of PIHT to recover sparse channel coefficients with low computational complexity as the initial stage for coarse channel estimation, followed by refinement of the channel estimates using the PSO technique. Through both computational complexity analysis and extensive simulations, the effectiveness of the combined approach is evaluated, demonstrating its potential to improve the accuracy of mmWave massive MIMO channel estimation. It is observed that at lower SNR the presented method is able to achieve reliable channel estimation.

Abstracts:Decentralized Environmental Notification Message (DENM) retransmission schemes have been suggested to increase the Packet Delivery Ratio (PDR) for DENMs, they compromise communication performance due to resource collisions with Cooperative Awareness Messages (CAM) in mixed-traffic environments. To address this issue, this letter proposes the Adaptive DENM Retransmission (ADR) scheme, which adaptively adjusts DENM retransmission resources based on the Channel Busy Ratio (CBR) estimated by the transmitting user equipment. Simulation results show that the ADR scheme improves PDR for both CAMs and DENMs, with greater improvements in congested wireless channels.

Abstracts:This letter studies the low-complexity channel estimation for orthogonal time frequency space (OTFS) in the presence of hardware impairments. Firstly, to tackle the computational complexity of channel estimation, the basis expansion model (BEM) is utilized. Then, the mean square error (MSE) of the estimated channel is theoretically derived, revealing the effects of hardware impairments on channel estimation. Based on the estimated channel, the minimum mean square error (MMSE) detector is adopted to analyze the impacts of imperfect hardware on the bit error rate (BER). Finally, the numerical results validate the correctness of our theoretical analysis of the MSE for channel estimation and lower bound of the BER, and also demonstrate that even minor hardware impairments can significantly degrade the performance of the OTFS system.

Abstracts:Whilst many common phase distributions have well-defined closed-form variance expressions, the well-known Rician phase (or Blachman-Bennett) distribution does not have any similar known expression. This Letter presents an asymptotic closed-form expression for the variance of the Rician phase distribution that is a significant improvement on existing expressions and straightforward to use. This new result takes advantage of the incomplete ${}_{3}\mathcal {F}_{1}$ hypergeometric function. We also introduce a normalized full-width half maximum (FWHM) figure of merit and the information theoretic-based Bhattarcharyya distance in a complementary way, in order to compare and characterize the Normal and the Rician phase noise distribution. MSC 2020: 41A60, 33B20, 33C90; OCIS: 060.5060, 120.3180,17 120.5050.

Abstracts:This study tackles the issue of in-phase and quadrature-phase error (IQE) in full-duplex amplify-and-forward relaying (FDR) systems. This problem is analyzed within the framework of orthogonal frequency division multiplexing (OFDM) transmissions. We propose a creative approach to calculate the IQE occurring at all terminals, assuming that the modulation type is unknown. Moreover, the channel impulse responses among different nodes are incorporated into the IQE parameters, avoiding the necessity for decoupling, as done in prior research. The proposed algorithm draws on the duplication characteristics associated with FDR transmissions to obtain maximum-likelihood estimates of the pertinent parameters. Comprehensive simulations validate the efficacy of the proposed algorithm, demonstrating significant improvements in estimation performance relative to the leading algorithms.

Abstracts:In this letter, a novel bidirectional block decision feedback equalization (BI-BDFE) algorithm is proposed for hybrid carrier (HC) systems based on the weighted-type fractional Fourier transform (WFRFT). This innovative algorithm employs a bidirectional BDFE structure in the WFRFT domain, which not only highlights the inherent advantage of HC architecture in combating doubly selective fading but also effectively mitigates the error propagation issue inherent in conventional unidirectional BDFE. Numerical results demonstrate that under doubly selective channels, the proposed BI-BDFE achieves significantly superior bit error rate (BER) performance compared to existing nonlinear equalization schemes.

Abstracts:Ultra-reliable low latency communication (URLLC) systems are pivotal for applications demanding high reliability and low latency, such as autonomous vehicles. In such contexts, channel prediction becomes essential to maintaining communication quality, as it allows the system to anticipate and mitigate the effects of fast-fading channels, thereby reducing the risk of packet loss and latency spikes. This letter presents a novel framework that integrates neural networks with extreme value theory (EVT) to enhance channel prediction, focusing on predicting extreme channel events that challenge URLLC performance. We propose an EVT-based adaptive quantile loss function that integrates EVT into the loss function of the deep recurrent neural networks (DRNNs) with gated recurrent units (GRUs) to predict extreme channel conditions efficiently. The numerical results indicate that the proposed GRU model, utilizing the EVT-based adaptive quantile loss function, significantly outperforms the traditional GRU. It predicts a tail portion of 7.26%, which closely aligns with the empirical 7.49%, while the traditional GRU model only predicts 2.4%. This demonstrates the superior capability of the proposed model in capturing tail values that are critical for URLLC systems.