Information Theory (cs.IT)

Abstract: With the growing interest in satellite networks, satellite-terrestrial integrated networks (STINs) have gained significant attention because of their potential benefits. However, due to the lack of a tractable network model for the STIN architecture, analytical studies allowing one to investigate the performance of such networks are not yet available. In this work, we propose a unified network model that jointly captures satellite and terrestrial networks into one analytical framework. Our key idea is based on Poisson point processes distributed on concentric spheres, assigning a random height to each point as a mark. This allows one to consider each point as a source of desired signal or a source of interference while ensuring visibility to the typical user. Thanks to this model, we derive the probability of coverage of STINs as a function of major system parameters, chiefly path-loss exponent, satellites and terrestrial base stations' height distributions and density, transmit power and biasing factors. Leveraging the analysis, we concretely explore two benefits that STINs provide: i) coverage extension in remote rural areas and ii) data offloading in dense urban areas.

Abstract: In-band full-duplex relay (FDR) has attracted much attention as an effective solution to improve the coverage and spectral efficiency in wireless communication networks. The basic problem for FDR transmission is how to eliminate the inherent self-interference and re-use the residual self-interference (RSI) at the relay to improve the end-to-end performance. Considering the RSI at the FDR, the overall equivalent channel can be modeled as an infinite impulse response (IIR) channel. For this IIR channel, a joint design for precoding, power gain control and equalization of cooperative OFDM relay systems is presented. Compared with the traditional OFDM systems, the length of the guard interval for the proposed design can be distinctly reduced, thereby improving the spectral efficiency. By analyzing the noise sources, this paper evaluates the signal to noise ratio (SNR) of the proposed scheme and presents a power gain control algorithm at the FDR. Compared with the existing schemes, the proposed scheme shows a superior bit error rate (BER) performance.

Abstract: Semantic communication has gained significant attention from researchers as a promising technique to replace conventional communication in the next generation of communication systems, primarily due to its ability to reduce communication costs. However, little literature has studied its effectiveness in multi-user scenarios, particularly when there are variations in the model architectures used by users and their computing capacities. To address this issue, we explore a semantic communication system that caters to multiple users with different model architectures by using a multi-purpose transmitter at the base station (BS). Specifically, the BS in the proposed framework employs semantic and channel encoders to encode the image for transmission, while the receiver utilizes its local channel and semantic decoder to reconstruct the original image. Our joint source-channel encoder at the BS can effectively extract and compress semantic features for specific users by considering the signal-to-noise ratio (SNR) and computing capacity of the user. Based on the network status, the joint source-channel encoder at the BS can adaptively adjust the length of the transmitted signal. A longer signal ensures more information for high-quality image reconstruction for the user, while a shorter signal helps avoid network congestion. In addition, we propose a hybrid loss function for training, which enhances the perceptual details of reconstructed images. Finally, we conduct a series of extensive evaluations and ablation studies to validate the effectiveness of the proposed system.

Abstract: Doubly-selective channel estimation represents a key element in ensuring communication reliability in wireless systems. Due to the impact of multi-path propagation and Doppler interference in dynamic environments, doubly-selective channel estimation becomes challenging. Conventional symbol-by-symbol (SBS) and frame-by-frame (FBF) channel estimation schemes encounter performance degradation in high mobility scenarios due to the usage of limited training pilots. Recently, deep learning (DL) has been utilized for doubly-selective channel estimation, where long short-term memory (LSTM) and convolutional neural network (CNN) networks are employed in the SBS and FBF, respectively. However, their usage is not optimal, since LSTM suffers from long-term memory problem, whereas, CNN-based estimators require high complexity. For this purpose, we overcome these issues by proposing an optimized recurrent neural network (RNN)-based channel estimation schemes, where gated recurrent unit (GRU) and Bi-GRU units are used in SBS and FBF channel estimation, respectively. The proposed estimators are based on the average correlation of the channel in different mobility scenarios, where several performance-complexity trade-offs are provided. Moreover, the performance of several RNN networks is analyzed. The performance superiority of the proposed estimators against the recently proposed DL-based SBS and FBF estimators is demonstrated for different scenarios while recording a significant reduction in complexity.

Abstract: The blind phase search (BPS) algorithm for carrier phase estimation is known to have sub-optimal performance for probabilistically shaped constellations. We present a belief propagation based approximate maximum a posteriori carrier phase estimator and compare its performance with the standard and an improved BPS algorithm.

Abstract: We formulate antenna impedance estimation in a classical estimation framework under correlated Raleigh fading channels. Based on training sequences of multiple packets, we derive the ML estimators for antenna impedance and channel variance, treating the fading path gains as nuisance parameters. These ML estimators can be found via scalar optimization. We explore the efficiency of these estimators against Cramer-Rao lower bounds by numerical examples. The impact of channel correlation on impedance estimation accuracy is investigated.

Abstract: High data rates are one of the most prevalent requirements in current mobile communications. To cover this and other high standards regarding performance, increasing coverage, capacity, and reliability, numerous works have proposed the development of systems employing the combination of several techniques such as Multiple Input Multiple Output (MIMO) wireless technologies with Orthogonal Frequency Division Multiplexing (OFDM) in the evolving 4G wireless communications. Our proposed system is based on the 2x2 MIMO antenna technique, which is defined to enhance the performance of radio communication systems in terms of capacity and spectral efficiency, and the OFDM technique, which can be implemented using two types of sub-carrier mapping modes: Space-Time Block Coding and Space Frequency Block Code. SFBC has been considered in our developed model. The main advantage of SFBC over STBC is that SFBC encodes two modulated symbols over two subcarriers of the same OFDM symbol, whereas STBC encodes two modulated symbols over two subcarriers of the same OFDM symbol; thus, the coding is performed in the frequency domain. Our solution aims to demonstrate the performance analysis of the Space Frequency Block Codes scheme, increasing the Signal Noise Ratio (SNR) at the receiver and decreasing the Bit Error Rate (BER) through the use of 4 QAM, 16 QAM and 64QAM modulation over a 2x2 MIMO channel for an LTE downlink transmission, in different channel radio environments. In this work, an analytical tool to evaluate the performance of SFBC - Orthogonal Frequency Division Multiplexing, using two transmit antennas and two receive antennas has been implemented, and the analysis using the average SNR has been considered as a sufficient statistic to describe the performance of SFBC in the 3GPP Long Term Evolution system over Multiple Input Multiple Output channels.