2024/1

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    Exact BER Performance Analysis of an Elementary Coding Techniques for NOMA System on AWGN Channel
    (Společnost pro radioelektronické inženýrství, 2024-04) Abd Alaziz, W.; Abood, B.; Muttasher, R. M.; Fadhel, M. A.; Jebur, B. A.
    Ultra-Reliable Low Latency Communication (URLLC) requirements of modern wireless communication systems have heightened the need for complexity reduction in data processing along with error detection and correction techniques. Motivated by this fact, we introduce a low-complexity coding scheme for Non-Orthogonal Multiple Access (NOMA). Furthermore, this work presents a comprehensive mathematical analysis of the proposed coded NOMA communication system and evaluates its Bit Error Rate (BER) performance in various scenarios. Our study showcases a precise match between practical and theoretical results, underlining the presented mathematical analysis precision. Moreover, we conduct a comparison between the proposed NOMA system and other coded and uncoded NOMA systems. This comparison highlights the superior performance of the proposed system, providing evidence of its potential to achieve the desired complexity reduction without compromising performance. Finally, in the same work environment, it is worth noting that the proposed system demonstrated superior performance compared to typical uncoded NOMA systems. It achieved a minimum improvement of 21 dB for the 1st user and a 17 dB improvement for the 2nd and 3rd users.
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    Optimized-Goppa Codes Based on the Effective Selection of Goppa Polynomials for Coded-Cooperative Generalized Spatial Modulation Network
    (Společnost pro radioelektronické inženýrství, 2024-04) Chen, C.; Yang, F. F.; Waweru, D. K.
    This paper proposes a novel optimized-Goppa-coded cooperative generalized spatial modulation (OGCC-GSM) scheme for short-to-medium information block transmission. In the proposed OGCC-GSM scheme, an efficient Goppa polynomial selection approach is designed to ensure that the selected Goppa codes applied in the source and relay nodes both have the largest minimum Hamming distance (MHD) and the optimal weight distribution. Compared to conventional coded cooperation (CC) with a single antenna, the proposed scheme employs the generalized spatial modulation (GSM) technique to achieve more diversity gains, where each node is equipped with multiple antennas and more than one transmit antenna (TA) is activated at each time-instant transmission. As a benchmark comparison, the OGCC spatial modulation (OGCC-SM) scheme is also investigated with a single TA active. Moreover, the reduced-complexity transmit antenna combination (RC-TAC) selection algorithm utilized in GSM is first developed with the aid of the channel state information (CSI) to reconcile computational complexity and system performance. In addition, joint decoding is conducted on the destination terminal to further enhance the performance of the proposed scheme. The simulated results indicate the performance of the proposed OGCC-GSM scheme is superior to that of its benchmark OGCC-SM scheme, with a substantial reduction in the number of TAs. Besides, Monte Carlo simulations demonstrate that the proposed OGCC-GSM scheme prevails over its counterparts by a margin of over 4.2 dB under identical conditions.
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    Cascaded Deep Neural Network Based Adaptive Precoding for Distributed Massive MIMO Systems
    (Společnost pro radioelektronické inženýrství, 2024-04) Ge, L. J.; Niu, S. X.; Shi, C. P.; Guo, Y. C.; Chen, G. J.
    In time-division duplex (TDD) distributed large-scale multiple input multiple output (DM-MIMO) systems, the traditional downlink channel precoding method is used to resist inter-user interference (IUI). However, when the Channel State Information (CSI) is incomplete, the performance loss is serious, not only the bit error rate is high, but also the complexity of the traditional precoding algorithm is high. In order to solve these problems, this paper proposes an adaptive precoding framework based on deep learning (DL) for joint training and split application deployment. First, we train a channel emulator deep neural network (CE-DNN) to learn and simulate the transmission process of the wireless communication channel. Then, we concatenate an untrained precoding DNN (P-DNN) with a trained CE-DNN and retrain the cascaded neural network to converge. The last step is to obtain the P-DNN, namely the adaptive precoding network, by dismantling the joint trained network. Simulation results show that, when CSI is imperfect, the proposed method is compared with Tomlinson-Harashima precoding (THP) and block diagonalization (BD) precoding. The proposed method has a lower mean square error (MSE) and higher spectrum efficiency, as well as a bit error rate (BER) performance close to the THP. The source codes and the neural network codes are available on request.
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    Split-Ring Coupled Low-Cost Antenna with Electromagnetic Bandgap (EBG) Superstrates to Produce Tri-bands and High Gains
    (Společnost pro radioelektronické inženýrství, 2024-04) Kawdungta, S.; Torrungrueng, D.; Chou, H.-T.
    In this paper, a novel tri-band low-cost antenna covering the desired frequencies is presented. The architecture is formed by a printed dipole coupled by a split-ring within an electromagnetic bandgap (EBG) structure for high radiation gains. The printed dipole is placed beneath two dielectric superstrates, and the coupling split-ring is placed on its top. The proposed antenna is excited by the printed dipole with a coaxial connector. It is placed in the middle cavity formed by two dielectric superstrates and a metal reflector as the simple EBG structure. The simulation results show three resonant frequencies at 1.42, 2.39 and 5.40 GHz respectively, with uni-directional radiation patterns and high gains enhanced by the EBG structure. Experimental measurements over an antenna prototype validate the results of reflection coefficients and radiation patterns. It is found that the gains are 8.50, 6.00 and 8.10 dBi at 1.42, 2.39 and 5.00 GHz respectively, which are sufficient for L-band and WiFi applications. In addition, simulation and measurement results are in good agreement.
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    Inverse Synthetic Aperture Radar Imaging Based on the Non-Convex Regularization Model
    (Společnost pro radioelektronické inženýrství, 2024-04) Zhao, Y.; Yang, F.; Wang, C.; Ye, F.; Zhu, F.; Liu, Y.
    Compressed Sensing (CS) has been shown to be an effective technique for improving the resolution of inverse synthetic aperture radar (ISAR) imaging and reducing the hardware requirements of radar systems. In this paper, our focus is on the l_p 0 p 1 model, which is a well-known non-convex and non-Lipschitz regularization model in the field of compressed sensing. In this study, we propose a novel algorithm, namely the Accelerated Iterative Support Shrinking with Full Linearization (AISSFL) algorithm, which aims to solve the l_p regularization model for ISAR imaging. The AISSFL algorithm draws inspiration from the Majorization-Minimization (MM) iteration algorithm and integrates the principles of support shrinkage and Nestrove's acceleration technique. The algorithm employed in this study demonstrates simplicity and efficiency. Numerical experiments demonstrate that AISSFL performs well in the field of ISAR imaging.