A Novel Design for Hybrid Space-Time Block Codes and Spatial Multiplexing Scheme

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.

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

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.

A novel blind carrier frequency offset (CFO) estimation algorithm was proposed which is based on estimating signal parameter via rotational invariance techniques (ESPRIT) for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with virtual carriers. The algorithm estimates CFO in closed-form by jointly exploiting the shift-invariant structure of received signals on multiple receive antennas. It can work effectively in different type of channel. Performance analysis of the proposed algorithm using Monte Carlo simulations and comparisons with some existing approaches are also reported.

In recent years, energy efficiency has become a critical metric for green system design and drawn universal attention. In this paper, we propose a new radio resource allocation algorithm to minimize the power consumption of the base station for the multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with different quality of service constraints of users. The discontinuous transmission (DTX) technique is applied in the systems with mixed orthogonal frequency division access and space division multiple access. Radio resources in time, frequency and space domain are optimally allocated to users in two steps: In the first step, the active time in the DTX mode is estimated under the assumption that every user experiences slow time-varying fading in an OFDM frame. In the second step, subcarriers, bits and power are allocated in the active time slots. Although the proposed algorithm is suboptimal due to high computation complexity, simulation results show that the proposed algorithm can reduce the power consumption of the base station and improve resource utilization.

In this paper, we propose a new approach to compensate the residual frequency offset (RFO) in multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system with nonlinear channel working in the burst mode. The proposed approach consists of two stages. Firstly a decision aided method is proposed to eliminate the nonlinearity introduced by high power transmit amplifier (HPA). Then a new decision aided approach is employed to achieve the RFO compensation on the nonlinearity-free symbols. The effectiveness of the proposed approach has been verified by computer simulations.

In this paper, a blind carrier frequency offset (CFO) estimation method for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is proposed. The proposed method is based on minimizing the power difference between all subcarriers in two consecutive space-time coding (STC) blocks. The cost function has a simple closed-form expression and the CFO can be estimated accurately using only three trial values. The performance of the proposed estimator is assessed via Monte Carlo simulation. Simulation results illustrate the superior performance of the proposed scheme for MIMO-OFDM systems when compared with other blind techniques.

Multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) has become a promising candidate for next generation broadband wireless communications. However, like a single-input single-output (SISO)-OFDM, one main disadvantage of the MIMO-OFDM is the high peak-to-average power ratio (PAPR), which can be reduced by using an amplitude clipping. In this paper, we propose clipped signal reconstruction methods for the MIMO-OFDMs with spatial diversity, such as space-time and space-frequency block codes (STBC/SFBC). The proposed methods are based on the technique called iterative amplitude reconstruction (IAR) for SISO-OFDM. It is shown that the IAR can be easily employed for the STBC-OFDM, but it cannot be directly applied to the SFBC-OFDM, because the transmitted sequences over different antennas are dependent due to the use of space-frequency code. We propose a new SFBC transmitter for clipped OFDM, which has approximately half the computational complexity of conventional SFBC-OFDM. The proposed clipping preserves the orthogonality of transmitted signals, and the clipped signals are iteratively recovered at the receiver. Further, we theoretically analyze the performance of IAR with optimum equalization, and also provide highly accurate channel estimation of the OFDM with amplitude clipping. Simulation results show that the proposed receivers effectively recover contaminated OFDM signals with a moderate computational complexity.

In this work we propose a power control algorithm for a multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) multi-hop collaborative relaying network. Using orthogonal and quasi-orthogonal block codes with three stage processing our algorithm optimally distributes available transmission power based on the architecture and the channel condition at each stage so as to minimize the end-to-end bit error rate (BER) of the entire relay network. For high data rate applications with maximum throughput (as expected for future OFDM systems), we employ a regenerative relaying process, where the relays at each stage decode and through collaborative processing, re-encode the received data before onward transmission to the next stage and then to the destination. We provide simulation results that confirm the performance improvement in the end-to-end bit error rate (BER) of the network using our explicit power allocation algorithm as compared with equal power distribution technique.

The performance of free space optical communication system using multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) with a modified receiver structure is evaluated in this paper referred to as MIMO-OFDM-FSO with the modified receiver (MIMO-OFDM-FSO-MR). Utilizing equal gain combining the analysis is done by taking closed-form expression of average bit error rate and throughput. The simulation is performed to estimate various performance parameters such as throughput and bit error rate (BER) for the proposed MIMO-OFDM-FSO-MR model under different turbulence and weather conditions. Simulation reveals that the proposed model exhibits significant BER performance and provides better throughput and spectral efficiency as compared with conventional FSO systems, OFDM-FSO systems, MIMO-FSO systems and Low-density Parity Check (LDPC) coded MIMO-OFDM-FSO systems.

Using communication signals for target detection is a key advantage of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) integrated sensing and communication (ISAC) systems. However, existing methods rely on the unrealistic assumptions that power amplifier (PA) nonlinearities and memory effects are completely eliminated. Furthermore, the estimation accuracy of range and velocity in these target detection algorithms is limited by the discrete Fourier transform (DFT) operator. To address these issues, we propose a spatial DFT normalized compensation and dual-Hankel subspace decomposition method. In the first stage, a nonlinear normalization compensation factor is applied after the spatial DFT to mitigate PA-induced distortion. In the second stage, by exploiting the Vandermonde structure in the fast-slow time domain, a dual-Hankel subspace decomposition is employed to achieve super-resolution estimates of range and velocity. Numerical results demonstrate that the proposed method effectively suppresses PA nonlinear effects while improving range and velocity estimation accuracy by an order of magnitude compared to existing methods.

Multi-input multi-output (MIMO) systems are often realized with low cost front-end architectures, e.g. the so called direct conversion architectures. However, such systems are very sensitive to imperfections in the analog front-end, resulting in radio frequency (RF) impairments such as in-phase/quadrature-phase (IQ) imbalance and carrier frequency offset (CFO). The RF impairments in such low cost front-end systems are unavoidable and can result in a severe performance degradation. In this paper we propose a generally applicable equalization technique for space-time block coded (STBC) MIMO orthogonal frequency division multiplexing (OFDM) communication systems. The Alamouti based STBC scheme is examined in detail. It is shown that the compensation scheme can be easily extended to other higher order STBC systems. We consider a digital compensation scheme for joint transmitter and receiver IQ imbalance along with front-end filter mismatch, CFO and frequency selective channel distortions.

MIMO-OFDM (Multi-Input, Multi-Output Orthogonal Frequency Division Multiplexing) signals are very prone to nonlinear distortion effects, which can lead to high irreducible error floors. This can be particularly serious when low-resolution quantizers are employed. In this paper we consider MIMO-OFDM schemes with low-resolution quantizers. It is shown that, although this leads to strong nonlinear distortion effects and very poor performance when conventional receivers are employed, this is not necessarily the case when optimum receivers are considered. Since the complexity of the optimum receiver for MIMO-OFDM is prohibitively high, we develop sub- optimum receivers with moderate complexity. It is shown that these receivers can have huge performance gains when the nonlinear distortion levels are high, eliminating irreducible error floors and even outperforming linear MIMO-OFDM schemes in some scenarios.

In this paper, we propose a transmission scheme to obtain a diversity gain in orthogonal frequency division multiplexing (OFDM) systems using spatial multiplexing (SM) transmission. In multiple- input multiple-output (MIMO) transmission, SM is advantageous for high-speed transmission when the signal to noise power ratio (SNR) is sufficiently high, whereas transmit diversity is preferable to SM when SNR is low. That is, there is a trade-off between high-speed transmission and reliable transmission. In this paper, we propose mutual superposition transmission (MST) in order to realize reliable SM-OFDM systems without reducing the transmission speed. In the proposed MST, two data symbols of two different subcarriers are mutually superposed on respective subcarriers after being weighted. Since each of superposed symbols contains two data symbols and is transmitted on each subcarrier, diversity can be achieved. As a result, the proposed method achieves a reliable SM transmission. We illustrate three types of MST and verify their effectiveness. From simulation results, we can show that the SM- OFDM system with the proposed MST of Type 1 achieves a better error rate performance than its original system conditioned on the same transmission rate.

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.