Signal space alignment (SSA) is a promising technique for interference management in wireless networks. However, despite the excellent work done on SSA, its robustness against jamming attacks has not been considered in the literature. In this paper, we propose two antijamming strategies for the SSA scheme applied in the multiple-input-multiple-output (MIMO) Y channel. The first scheme involves projecting the jamming signal into the null space of each source's precoding vectors, effectively eliminating it entirely. The second scheme removes interference originating from the jammer by subtracting the disturbance estimate from the incoming signal. The estimate is derived on the basis of the criterion of minimizing the received signal energy. The block error rate (BLER) performance of the proposed strategies in various channel configurations is verified by link level simulations and is presented to show the efficiency in mitigating jamming signals within the SSA-based MIMO Y channel.
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http://dx.doi.org/10.3390/s24103237 | DOI Listing |
Sensors (Basel)
January 2025
School of Artificial Intelligence, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China.
The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF).
View Article and Find Full Text PDFEntropy (Basel)
December 2024
The School of Electric Engineering and Intelligentization, Dongguan University of Technology, Dongguan 523808, China.
In this paper, we propose a random frequency division multiplexing (RFDM) method for multicarrier modulation in mobile time-varying channels. Inspired by compressed sensing (CS) technology which use a sensing matrix (with far fewer rows than columns) to sample and compress the original sparse signal simultaneously, while there are many reconstruction algorithms that can recover the original high-dimensional signal from a small number of measurements at the receiver. The approach choose the classic sensing matrix of CS-Gaussian random matrix to compress the signal.
View Article and Find Full Text PDFNat Commun
January 2025
Institute of Electromagnetic Space, Southeast University, Nanjing, China.
Holographic multiple-input multiple-output (MIMO) method leverages spatial diversity to enhance the performance of wireless communications and is expected to be a key technology enabling for high-speed data services in the forthcoming sixth generation (6G) networks. However, the antenna array commonly used in the traditional massive MIMO cannot meet the requirements of low cost, low complexity and high spatial resolution simultaneously, especially in higher frequency bands. Hence it is important to achieve a feasible hardware platform to support theoretical study of the holographic MIMO communications.
View Article and Find Full Text PDFCommonly used linear equalizers in optical transmissions may induce in-band noise enhancement in the high-frequency region, degrading signaling performance. In this Letter, we propose for the first, to our knowledge, time, to mitigate the multi-input-multi-output (MIMO) equalizer-enhanced noise (EEN) in coupled-core multicore fiber (CC-MCF) systems by utilizing the spectral shaping (SS) filter and maximum likelihood sequence detection (MLSD), which have shown effective EEN mitigation in SMF systems. However, CC-MCF systems feature multiple spatial channels, each requiring separate coefficient optimization for SS filters corresponding to each output of MIMO.
View Article and Find Full Text PDFSensors (Basel)
November 2024
Department of Digital Industry Technologies, National and Kapodistrian University of Athens, Dirfies Messapies, 34400 Athens, Greece.
The goal of the study presented in this work is to evaluate the performance of a proposed adaptive beamforming approach when combined with non-orthogonal multiple access (NOMA) in cell-free massive multiple input multiple output (CF m-MIMO) orientations. In this context, cooperative beamforming is employed taking into consideration the geographically adjacent access points (APs) of a virtual cell, aiming to minimize co-channel interference (CCI) among mobile stations (MSs) participating in NOMA transmission. Performance is evaluated statistically via extensive Monte Carlo (MC) simulations in a two-tier wireless orientation.
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