Resource allocation and block coding within a three-stage collaborative broadband relay network

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 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.

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.

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 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.

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.

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 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.

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 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.

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.

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.

This paper studies the pilot design for compressed sensing (CS)-based sparse channel estimation in multi-input–multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. To improve the performance of estimating multiple jointly sparse channels, based on the assumption that the modulus of the pilot symbol at each pilot carrier position is equal to a constant, the pilot is currently allocated by means of lowering the sensing matrix’s total coherence (TC). However, according to the block compressed sensing (BCS) theory, the recovery ability of the sensing matrix is determined by the block coherence (BC) and subblock coherence (SC), which should be as small as possible. Therefore, we propose a novel scheme, which designs the pilot by simultaneously minimizing the TC, BC, and SC of the sensing matrix to improve the channel estimation accuracy. We first formulate the jointly sparse channel estimation as a block sparse signal recovery problem, and the pilot allocation problem is comprised of allocating the pilot index for each transmitter and allocating the pilot symbol at each carrier. Then, we derive the error bound of BCS-based channel estimation, where the pilot symbols bear any value. Finally, a novel sequential joint criterion design (SJCD) method is proposed to design pilots with a joint criterion, where the pilot pattern and pilot power are designed by BC and TC, respectively. Simulation results show that, compared with existing algorithms, the proposed algorithm can achieve a better channel estimation performance in terms of normalized mean square error (NMSE) and bit error rate (BER).

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.

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.