Performance Analysis and Transceiver Design for Adaptive BIC-MIMO-OFDM Systems

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.

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.

Space-time block codes (STBC) and spatial multiplexing (SM) are promising techniques that effectively exploit multipleinput multiple-output (MIMO) transmission to achieve more reliable communication and a higher multiplexing rate, respectively. In this paper, we study a practical design for hybrid scheme with multi-input multi-output orthogonal frequency division multiplexing (MIMOOFDM) systems to flexibly maximize the tradeoff between diversity and multiplexing gains. Unlike the existing STBC and SM designs which are suitable for the integer multiplexing rate, the proposed design can achieve arbitrary number of multiplexing rate.

In this paper, we investigate the channel estimation for massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Using the sampled steering vectors in the space and frequency domain, we first establish a space-frequency (SF) beam based statistical channel model. The accuracy of the channel model can be guaranteed with sufficient sampling steering vectors. With the channel model, the channel estimation is formulated as obtaining the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a \ posteriori$</tex-math></inline-formula> information of the beam domain channel. We solve this problem by calculating an approximation of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a\ posteriori$</tex-math></inline-formula> distribution's marginals within the information geometry framework. Specifically, by viewing the set of Gaussian distributions and the set of the marginals as a manifold and its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$e$</tex-math></inline-formula> -flat submanifold, we turn the calculation of the marginals into an iterative projection process between submanifolds with different constraints. We derive the information geometry approach (IGA) for channel estimation by calculating the solutions of projections. We prove that the mean of the approximate marginals at the equilibrium of IGA is equal to that of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$a \ posteriori$</tex-math></inline-formula> distribution. Simulations demonstrate that the proposed IGA can accurately estimate the beam domain channel within limited iterations.

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

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, 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.

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.

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.

The authors investigate quantisation and feedback of channel state information in a multiuser (MU) multiple-input multiple-output (MIMO) system. Each user may receive multiple data streams. The authors design minimises the sum mean squared error (SMSE) while accounting for the imperfections in channel state information (CSI) at the transmitter. This study makes three contributions: first, the authors provide an end-to-end SMSE transceiver design that incorporates receiver combining, feedback policy and transmit precoder design with channel uncertainty. This enables the proposed transceiver to outperform the previously derived limited feedback MU linear transceivers. Second, the authors remove dimensionality constraints on the MIMO system, for the scenario with multiple data streams per user, using a combination of maximum expected signal combining and minimum MSE receiver. This makes each user's feedback independent of the others and the resulting feedback overhead scales linearly with the number of data streams instead of the number of receiving antennas. Finally, the authors analyse the SMSE of the proposed algorithm at high signal-to-noise ratio (SNR) and large number of transmit antennas. As an aside, the authors show analytically why the bit error rate, in the high SNR regime, increases if quantisation error is ignored.

The performance of signal detection in a multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) optical wireless communication (OWC) system is significantly affected by channel fading and atmospheric turbulence. Accurate channel state information (CSI) is crucial for reliable channel estimation, but obtaining it typically requires a high pilot overhead. To address this issue, we propose a joint channel estimation and signal detection (JCESD) algorithm that reduces the pilot overhead while improving the detection performance with a moderate increase in computational complexity. By leveraging convolutional neural network (CNN) and long short-term memory (LSTM) networks, the JCESD algorithm learns the mapping relationship between atmospheric turbulence channels and signals, thereby mitigating the challenges associated with high-complexity signal processing at the receiver. Additionally, layer normalization is applied during data pre-processing to enhance overall performance. Simulation results demonstrate that the proposed JCESD algorithm outperforms the LS-ZF algorithm and achieves an approximate 1dB gain over the LS-MMSE algorithm under weak turbulence conditions at a bit error rate (BER) of 10-5 in the 2×2 system when using eight pilots, with a 0.9dB gain when using four pilots-highlighting its capability to maintain superior performance while reducing pilot overhead. Additionally, the JCESD algorithm exhibits superior robustness in atmospheric turbulence channels while effectively reducing pilot overhead and maintaining manageable 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.

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.

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.