CNN-LSTM-assisted joint channel estimation andsignal detection for optical wireless communications.

Signal detection is crucial in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, yet classical detection methods often struggle with nonlinear issues in wireless channels. To handle this challenge, we propose a novel signal detection method for MIMO-OFDM system based on the fractional Fourier transform (FrFT), leveraging the robust time series processing capabilities of long short-term memory (LSTM) networks. Our innovative approach, termed IM-LSTMNet, integrates LSTM with convolutional neural networks (CNNs) and incorporates a Squeeze and Excitation Network to emphasize critical information, enhancing neural network performance. The proposed IM-LSTMNet is applied to the FrFT-based MIMO-OFDM system to improve signal detection performance. We compare the detection results of IM-LSTMNet with zero forcing (ZF), minimum mean square error (MMSE), simple LSTM neural network, and CNN–LSTM network by evaluating the bit error rate. Experimental results demonstrate that IM-LSTMNet outperforms ZF, MMSE, LSTM, and other methods, significantly enhancing system signal detection performance. This work offers a promising advancement in MIMO-OFDM signal detection, presenting a deep learning-based solution that effectively improves the system signal detection performance.

Accurate channel state information (CSI) is critical for current and next-generation multi-antenna systems. Yet conventional pilot-based estimators incur prohibitive overhead as antenna counts grow. In this paper, we address this challenge by developing a novel framework based on Gaussian process regression (GPR) that predicts full CSI from only a few observed entries, thereby reducing pilot overhead. The correlation between data points in GPR is defined by the covariance function, known as kernel. In the proposed GPR-based CSI estimation framework, we incorporate three kernels, i.e., radial basis function, Matérn, and rational quadratic, to model smooth and multi-scale spatial correlations derived from the antenna array geometry. The proposed approach is evaluated across two channel models with three distinct pilot probing schemes. Results show that the proposed GPR with 50% pilot saving achieves the lowest prediction error, the highest empirical 95% credible-interval coverage, and the best preservation of spectral efficiency relative to the benchmarks.

The problem of channel shortening equalization (CSE) for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is addressed. Relying on constrained minimization of the mean-output-energy (MOE) at the CSE output, we propose a blind channel shortening technique, which does not require a priori knowledge or estimation of the MIMO channel impulse responses to be shortened. Numerical results showing the attainable performance gain of the proposed method are reported.

The performance analysis and transceiver design of an adaptive bit-interleaved-coded multi-input multi-output orthogonal frequency division multiplexing (BIC-MIMO-OFDM) system without channel station information (CSI) in priori are investigated in this research. First, the noncoherent capacity of a MIMO-OFDM system with TV subcarriers, nt transmit antennas and nr receive antennas is analyzed. It is shown that the lower bound of noncoherent capacity in the high signal-to-noise ratio (SNR) region is asymptotically equal to n*(1 - n*L/N) log rho, where n* = min(nl,nr), L is the number of channel taps and rho is the SNR value. Then, an adaptive BIC-MIMO-OFDM system based on transmitter beamforming and variable-rate-variable-power (VRVP) QAM is proposed. The throughput of adaptive scheme can achieve the noncoherent capacity, however, the gap to the coherent capacity cannot be totally compensated due to the overhead of training. The effect of imperfect channel state information (CSI) feedback is studied. It is demonstrated by numerical simulation that the proposed BIC-MIMO-OFDM scheme has better performance than adaptive trellis coded modulation (TCM) when outdated channel estimation is used.

For practical multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems, it is important to develop transmit diversity schemes that are transparent to a receiver in that the receiver does not need to perform additional processing. In this paper, we examine one of the most widely-used receiver-transparent transmit diversity schemes - the cyclic delay diversity (CDD) scheme. It is shown that the CDD scheme effectively increases the delay spread of the channel and that the performance degrades under non-ideal channel estimation. As a remedy to the performance loss due to non-ideal channel estimation, a cluster-based transmit diversity scheme is proposed, which garners almost all the benefits of the CDD scheme without degrading the performance of a cluster- based channel estimation. The simulation results are provided in the realistic setting of IEEE 802.16e WiMAX systems.

In this paper, we propose an energy-efficient interference alignment (IA) based resource management algorithm for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) cognitive radio (CR) systems. The proposed algorithm provides the secondary users (SUs) with the opportunity for underlay sharing of the primary system spectrum. The proposed algorithm ensures the quality-of-service (QoS) of the primary system by guaranteeing the minimum transmission rate. The problem is formulated as a mixed-integer non-convex optimization problem, in which the objective is to maximize the energy efficiency, and the constraints are the per-user power budget and QoS demand of the primary system. To tackle mixed-integer and non-convexity nature of the problem, we propose a sub-optimal energy-efficient algorithm through two successive steps. The first step schedules the subcarriers among the SUs based on IA while the second step iteratively allocates the power based on Dinkelbach’s scheme. Simulations reveal that the proposed algorithm achieves significant improvement in the energy efficiency compared to the traditional spectrum-efficient algorithm.KeywordsCognitive radioInterference alignmentResource allocationEnergy efficiencyMIMOOFDM

The selective mapping (SLM) scheme is one of the most popular peak-to-average power ratio (PAPR) reduction techniques proposed for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. One of the major disadvantages of this scheme is the need for the transmission of side information (SI) bits to enable the receiver to recover the transmitted data. The authors present a small overhead SLM (s-SLM) scheme for space–time block coded (STBC) MIMO-OFDM systems. This proposed scheme improves the system bandwidth efficiency and achieves a significantly lower bit error rate (BER) than the individual SLM (i-SLM) and direct SLM (d-SLM) schemes. In addition, approximate expressions for the complementary cumulative distribution function (CCDF) of the PAPR and the average BER of the proposed s-SLM scheme are derived. The simulation results show that the proposed s-SLM scheme improves the detection probability of the SI bits and hence gives a better performance than the i-SLM and the d-SLM schemes.

The reconfigurable intelligent surfaces (RISs) have garnered a lot of interest in the development of smart radios. The acquisition of channel state information in RIS aided systems are extremely challenging as pilot overhead is considerably large. Previous works have shown an improvement in reducing pilot over head by exploiting the structured sparsity. In this contribution, we consider a sparse RIS under the environment of multiuser MIMO communication system by randomly selecting the elements of RIS in uniform planar arrays to eliminate unnecessary elements and to reduce the large pilot overhead. The random distribution models i.e Gaussian, Poisson and Geometric are adopted and analyzed for selection of sparse RIS. The numerical results show a significant improvement in NMSE performance with reduced pilot overhead.

Adaptive modulation has been shown to have significant benefits for high-speed wireless data transmission when orthogonal frequency division multiplexing (OFDM) is employed. However, accurate channel state information (CSI) is required at the transmitter to achieve the benefits. Imperfect CSI arises from noisy channel estimates, which may also be outdated due to a delay in getting the CSI to the transmitter. In this paper, we study adaptive OFDM with imperfect CSI for the uncoded variable bit rate case, where a target bit error rate is set. A loading algorithm based on the statistics of the real channel is proposed. Performance results in terms of the average spectral efficiency are provided for adaptive OFDM systems when there is noisy channel estimation or CSI delay. The use of multiple estimates is then proposed to improve the performance. It is shown that multiple estimates from different frequencies or times can enhance the performance significantly, which enables the system to tolerate larger errors in channel estimation or longer delay in CSI

For a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, time-varying multipath fading of channel destroys the orthogonality among subcarriers and leads to serious intercarrier interference (ICI). The system performance degrades more severely as normalized Doppler frequency increases. In order to mitigate the effect of time-varying fading, a low-complexity iterative receiver with joint ICI cancellation and pilot-assisted channel estimation is proposed. The initial channel state information (CSI) is estimated by performing time-domain interpolation and least-square (LS) method on the received pilot symbols. The soft outputs are obtained from the decoders after low-complexity linear minimum mean-square error (LC-LMMSE) detection. In the following stages, the soft outputs are feedback to update the CSI estimation. Furthermore, a ¿linear statistics combining¿ (LSC) technique is used to improve the performance of the proposed equalizer by combining the outputs of LC-LMMSE and parallel interference canceler (PIC) with weighting coefficients estimated by maximizing the signal to interference-plus-noise ratio (SINR) at the output of LSC. The complexity of system is significantly reduced by restricting the interference to neighboring subcarriers and employing the LC-LMMSE by limiting the frequency-domain CSI into diagonal region. The simulation results show that the proposed iterative receiver with estimated CSI approaches the ICI-free bound even at very high mobility scenarios.

Pilot assisted channel tracking (PACT) has been very popular for channel estimation in OFDM systems. However, as the mobility in the system increases and one has to maintain a high accuracy in channel estimation, the pilot overhead typically has to be increased. Emerging cellular OFDM standards are expected to use about 6 - 12% pilot overhead per stream, and any possible reduction in pilot overhead would be useful. Decision directed channel tracking (DDCT) can help reduce pilot overhead, but is known to suffer from error propagation at high fade rates. In emerging broadband wireless systems promising peak bit rates of 50 Mbps or more, saving on pilot overhead by using DDCT schemes would be highly attractive provided: (a) Such a DDCT approach does not suffer from error propagation and has an error rate performance comparable to (or better than) PACT schemes even at high fade rates; (b) The computational complexity of such a DDCT approach is not significantly more than that of the MMSE based PACT scheme. In this work, we propose a low complexity DDCT method which exploits the structure of the regression matrix in conjunction with robust statistics to mitigate the effect of error propagation. It has a much lower computational complexity and a better error rate performance than the decision directed EM-Kalman and other existing robust statistics based DDCT schemes. The proposed method also outperforms a 12.5% pilot overhead based PACT scheme with only a modest increase in computational complexity.

Adaptive orthogonal frequency division multiplexing (OFDM) has been shown to have significant improvement over basic OFDM in terms of spectral efficiency and bit error rate performance. However, the most important condition to achieve these benefits is the availability of accurate channel state information (CSI) at the transmitter. Imperfect CSI arises from noise at the receiver and due to a delay in providing the CSI from the receiver to the transmitter for the next data transmission. In this paper, we have studied the impact of imperfect CSI on the spectral efficiency of adaptive OFDM systems. We have proposed a robust loading algorithm to improve the performance of such systems. This algorithm applies the bit under loading phenomenon and makes efficient use of M-ary Phase Shift Keying (MPSK) modulation. Plots of spectral efficiency via simulations are provided.

In this paper, we investigate channel estimation and robust detection for uplink massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with in-phase and quadrature-phase imbalances (IQI). We represent the received signal with a real-valued model, where the combination of the IQI and the wireless channel is treated as the effective channel. Based on the real-valued model, we then perform the minimum mean square error (MMSE) criterion based estimation for the effective channel. We use adjustable phase shift pilots (APSPs) to reduce the pilot overhead and verify that the MSE of the effective channel estimation can be minimized by scheduling the pilot phase shifts properly. We propose a pilot phase shift scheduling algorithm based on the covariance matrices of the effective channels. Since the channel estimation performance might be degraded due to using APSPs, we develop an MMSE criterion based data detection scheme which is robust to the channel estimation error. Using operator-valued free probability theory, we derive an asymptotic deterministic equivalent for the ergodic achievable sum rate of the proposed detection scheme. The performance of the channel estimation with APSPs and the robust data detection is demonstrated via extensive numerical results.

This paper focuses on the compensation of carrier frequency offsets (CFO) for spatial multiplexing multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems with distributed transmit antennas, where the CFOs between the receiver and each transmit antenna are possibly different and the zero-forcing (ZF) detection is used. Rather than the conventional linear frequency-domain equalization method that involves computationally complex matrix inversions, we propose a CFO compensation method without need for matrix inversion by exploiting the underlying characteristics of ZF detection and assuming relatively small root-mean-square (r.m.s.) delay spread of the multipath channel. Simulation results show that the symbol error rate (SER) performance of the system is significantly improved by using the proposed CFO compensation method in the low delay spread case (i.e., channel A) of the three test environments in the ITU-R M.1225 channel model.

This paper analyzes the performance of selected mapping and partial transmit sequences in the multiinput multioutput orthogonal frequency division multiplexing (MIMO-OFDM) system using a space-time block code. The phase rotation vector, which is transmitted as side information, is considered. Simulation results show that the PAPR is decreased about 5 dB in the PTS, and about 3.5 dB in SLM, compared with the original MIMO-OFDM system, when QPSK is used. In order to conform to the linear amplifier, the input back off (IBO) of 4 dB is required for the PTS, and the IBO of 6 dB for the SLM due to the predistorter. Without the SLM or the PTS, the IBO of about 8 dB is needed