Ciphered BCH Codes for PAPR Reduction in the OFDM in Underwater Acoustic Channels

We propose an effective, low complexity and multifaceted scheme for peak-to-average power ratio (PAPR) reduction in the orthogonal frequency division multiplexing (OFDM) system for underwater acoustic (UWA) channels. In UWA OFDM systems, PAPR reduction is a challenging task due to low bandwidth availability along with computational and power limitations. The proposed scheme takes advantage of XOR ciphering and generates ciphered Bose–Chaudhuri–Hocquenghem (BCH) codes that have low PAPR. This scheme is based upon an algorithm that computes several keys offline, such that when the BCH codes are XOR-ciphered with these keys, it lowers the PAPR of BCH-encoded signals. The subsequent low PAPR modified BCH codes produced using the chosen keys are used in transmission. This technique is ideal for UWA systems as it does not require additional computational power at the transceiver during live transmission. The advantage of the proposed scheme is threefold. First, it reduces the PAPR; second, since it uses BCH codes, the bit error rate (BER) of the system improves; and third, a level of encryption is introduced via XOR ciphering, enabling secure communication. Simulations were performed in a realistic UWA channel, and the results demonstrated that the proposed scheme could indeed achieve all three objectives with minimum computational power.

Comparison of SPS and FA methods for Blind Carrier Frequency Offset Estimation in OFDM systems

In orthogonal frequency-division multiplexing (OFDM) systems, synchronization issues are of great importance since synchronization errors might destroy the orthogonality among all subcarriers and, therefore, introduce intercarrier interference (ICI) and intersymbol interference (ISI). Several schemes of frequency offset estimation in OFDM systems have been investigated. This paper compares performance and computational complexity of Smoothing Power Spectrum (SPS) and Frequency Analysis (FA) methods for blind carrier frequency offset (CFO) estimation in OFDM systems.

Deep Learning Based Signal Processing For Multiuser OFDM Systems

Deep learning (DL) methods have been proved effective in improving the performance of channel estimation and signal detection. In this work, we propose three DL algorithms: fully connected deep neural network (FCDNN), convolutional neural networks (CNN), and long short-term memory (LSTM) neural networks for signal processing in multiuser orthogonal frequency-division multiplexing (OFDM) communications systems. The bit error rates (BERs) of these DL methods are compared with the conventional linear minimum mean squared error (LMMSE) detector. Additionally, the relationships between the BER and signal-to-interference ratio (SIR), signal-to-noise ratio (SNR), the number of interfering users (NoI) and modulation type are investigated. Numerical results show that all DL methods outperform LMMSE under different multiuser interference conditions, and FCDNN and LSTM give the best and robust anti-multiuser performance. This work shows that FCDNN and LSTM network have strong anti-interference ability and are useful in multiuser OFDM systems.

An Efficient Two-Stage Receiver Base on AOR Iterative Algorithm and Chebyshev Acceleration for Uplink Multiuser Massive-MIMO OFDM Systems

The massive multiple-input multiple-output systems (M-MIMO) and orthogonal frequency-division multiplexing (OFDM) are considered to be some of the most promising key techniques in the emerging 5G and advanced wireless communication systems nowadays. Not only are the benefits of applying M-MIMO and OFDM for broadband communication well known, but using them for the application of the Internet of Things (IoT) requires a large amount of wireless transmission, which is a developing topic. However, its high complexity becomes a problem when there are numerous antennas. In this paper, we provide an effective two-stage multiuser detector (MUD) with the assistance of the accelerated over-relaxation (AOR) iterative algorithm and Chebyshev acceleration for the uplink of M-MIMO OFDM systems to achieve a better balance between bit error rate (BER) performance and computational complexity. The first stage of the receiver consists of an accelerated over-relaxation (AOR)-based estimator and is intended to yield a rough initial estimate of the relaxation factor ω, the acceleration parameter γ, and transmitted symbols. In the second stage, the Chebyshev acceleration method is used for detection, and a more precise signal is produced through efficient iterative estimation. Additionally, we call this proposed scheme Chebyshev-accelerated over-relaxation (CAOR) detection. Conducted simulations show that the developed receiver, with a modest computational load, can provide superior performance compared with previous works, especially in the MU M-MIMO uplink environments.

DFT-Based Channel Estimation with Channel Response Mirroring for MIMO OFDM Systems

In this paper, DFT-Based channel estimation with channel response mirroring is proposed and analyzed. In General, pilot symbols for channel estimation in MIMO(Multi-Input Multi-Output) OFDM(Orthogonal Frequency-Division Multiplexing) Systems have a diamond shape in the time-frequency plane. An interpolation technique to estimate the channel response of sub-carriers between reference symbols is needed. Various interpolation techniques such as linear interpolation, low-pass filtering interpolation, cubic interpolation and DFT interpolation are employed to estimate the non-pilot sub-carriers. In this paper, we investigate the conventional DFT-based channel estimation for noise reduction and channel response interpolation. The conventional method has performance degradation by distortion called “edge effect” or “border effect”. In order to mitigate the distortion, we propose an improved DFT-based channel estimation with channel response mirroring. This technique can efficiently mitigate the distortion caused by the DFT of channel response discontinuity. Simulation results show that the proposed method has better performance than the conventional DFT-based channel estimation in terms of MSE.