Random coding error exponents for flat fading channels with realistic channel estimation

Multiple Input Multiple Output(MIMO) has been in much importance in recent past because of high capacity gain over a single antenna system. In this article, analysis over the capacity of the MIMO channel systems with spatial channel with modified precoder and decoder has been considered when the channel state information (CSI) is considered partial. Due to delay in acquiring transmitted information at the receiver end, the time selective fading wireless channel often induces incomplete or partial CSI. The dynamic CSI model has also been implemented consisting channel mean and covariance which leads to extracting of channel estimates and error covariance which then further applied with the modified precoder and decoder utilizing both the parameters indicating the CSI quality since these are the functions of temporal correlation factor, and based on this, the model covers data from perfect to statistical CSI, either partially or fully blind. It is found that in case of partial and imperfect CSI, the capacity depends on the statistical properties of the error in the CSI which has been manipulated according to the precoder and decoder conditions. Based on the knowledge of statistical distribution of the deviations in CSI knowledge, a new approach which maximizes the capacity of spatial channel model with modified precoder and decoder has been tried. The interference then interactively reduced by employing the iterative channel estimation and data detection approach, where by utilizing the detected symbols from the previous iteration, multiuser/MIMO channel estimation and symbol detection is improved.

In wideband multi-pair two-way relay networks, the performance of beamforming at a relay station (RS) is intimately related to the accuracy of the channel state information (CSI) available. The accuracy of CSI is determined by Doppler spread, delay between beamforming and channel estimation, and density of pilot symbols, including transmit power of pilot symbols. The coefficient of the Gaussian-Markov CSI error model is modeled as a function of CSI delay, Doppler spread, and signal-to-noise ratio, and can be estimated in real time. In accordance with the real-time estimated coefficients of the error model, an adaptive robust maximum signal-to-interferenceand- noise ratio (Max-SINR) plus maximum signal-to-leakage-and-noise ratio (Max-SLNR) beamformer at an RS is proposed to track the variation of the CSI error. From simulation results and analysis, it is shown that: compared to existing non-adaptive beamformers, the proposed adaptive beamformer is more robust and performs much better in the sense of bit error rate (BER); with increase in the density of transmit pilot symbols, its BER and sum-rate performances tend to those of the beamformer of Max-SINR plus Max-SLNR with ideal CSI.

Reconfigurable intelligent surfaces (RISs) have recently emerged as an innovative technology for improving the energy and/or spectrum efficiency of future wireless communications. In this paper, we propose a transmission protocol for the wideband RIS-assisted single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system to execute channel estimation and passive beamforming simultaneously. A new channel estimation method is proposed to progressively resolve the channel state information (CSI) over the training symbols, based on which the passive beamforming at the RIS is optimized to improve the channel gains on the data tones in the remaining training symbols. Based on the incomplete CSI, we formulate an optimization problem to maximize the average achievable rate by designing the passive beamforming at the RIS, which needs to balance the received signal power over different subcarriers and different receive antennas. Moreover, we propose a low-complexity algorithm to find a high-quality solution for the formulated problem. Simulation results validate the effectiveness of the proposed channel estimation and beamforming optimization methods.

The knowledge of the impact of imperfect channel state information (CSI) on data transmission techniques is helpful to improve the performance of wireless communication systems. In this paper, based on the proposed cooperative downlink (DL) transmission scheme applying adaptive scheduling of mobile stations (MSs), significant channel selection, and joint transmission (JT) with partial CSI, i.e., JT considering only part of the useful channels and the interference channels, a novel performance assessment is performed taking imperfectness of the CSI into consideration. The performance degradation caused by the imperfectness of the CSI which is used in the adaptive scheduling, the significant channel selection and the JT with partial CSI is investigated. The question about how much CSI should be taken into consideration as a function of the extent of imperfectness of the CSI in order to achieve optimum system performance is answered based on numerical results.

In this paper, a single input multiple output system is considered with L receive antennas and the underlying channels are assumed to be time varying with temporal correlation coefficient a and spatially correlated with correlation coefficient $$\rho $$ . Further, the channel is assumed to be identically distributed using Rayleigh fading channels and characterized by first order autoregressive model. For the detection, we assume partial channel state information (CSI) available at the receiver. The partial CSI is in the form of a known preamble (one symbol) only before beginning of a frame of size N symbols. Further, the preamble is imperfectly known with a variance of error $$\sigma _e^2$$ . For the assumed system, closed form expressions of symbol error rate (SER) are derived for M-PSK and M-QAM constellations under the compound effect of spatially correlated channels, temporally correlated channels and partial CSI at the Receiver. The derived expressions are functions of average SNR per symbol, $$\rho $$ , a, $$\sigma _e^2$$ , N, L and modulation order M. Further, these expressions are reduced for some special cases and compared with prevailing results in literature. The analytical expressions are also validated by comparing them with the corresponding simulation results. The derived expressions are very useful to select N, L and M to overcome the deterioration in SER due to adverse effects of $$\rho $$ , a and $$\sigma _e^2$$ .

In this paper, we study the performance of block diagonalization (BD) scheme for a downlink multiuser multi-input multi-output (MIMO) system with imperfect channel state information (CSI). At each mobile user, information about the channel is obtained by applying the minimum mean-square-error (MMSE) channel estimation method. The channel state information is fed back to the base station through error-free uplink channels. A theoretical analysis is performed showing that channel estimation errors contribute to co-channel interferences thus deteriorating sum rate capacity. Computer simulations are performed to evaluate the impact of channel estimation errors on the sum rate capacity. The results show that if the MSE of the channel estimation is not less than 10-2, the impact of channel estimation errors is significant and cannot be neglected. To combat this adverse effect, a proper transmit power level is required for the training signals.

A novel approach is proposed for correlated multiple-input multiple-output (MIMO) channel estimation based on reduced-rank (RR) technique and partial channel state information (CSI). In contrast to previous proposals that used the channel correlation matrix (CCM) and its eigendecomposition, this paper shows that close linear minimum mean-square-error (LMMSE) performance can be achieved with the use of predefined bases derived from the knowledge of the maximum angular dispersion. A theoretical framework to synthesize a suitable set of bases is provided, from which discrete prolate spheroidal sequences (DPSSs) are identified as one of the appropriate predefined bases for spatial channel representation. The robustness of the proposed estimator allows changes in the propagation scenario to be managed according to the demands of realistic communications systems. The performance analysis of the channel estimator is shown and corroborated with simulation results.

In this paper, we consider the downlink transmission over a single-cell massive multiple-input-multiple-output (MIMO) system in the presence of multiple single-antenna eavesdroppers (massive MIMOME). We concentrate on the practical scenario where partial channel state information (CSI) of legitimate users and no CSI of eavesdroppers are available at the base station and consider both types of eavesdroppers including the non-colluding and colluding eavesdroppers. Random unitary beamforming (RUB) based scheme is used to describe the partial CSI of legitimate users. We derive the closed-form expression of ergodic secrecy rate for RUB based massive MIMOME transmission, and its single legitimate user particular case. We also present numerical results to illustrate the performance-complexity tradeoff among different massive MIMO transmission schemes. We show that RUB based scheme can enhance secrecy performance of massive MIMO transmission with lower implementation complexity.

This paper investigates the massive multi-input multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels. multi-output (MIMO) system in practical deployment scenarios, in which, to balance the economic and energy efficiency with the system performance, the number of radio frequency (RF) chains is smaller than the number of antennas. The base station employs antenna selection (AS) to fully harness the spatial multiplexing gain. Conventional AS techniques require full channel state information (CSI), which is time-consuming as the antennas cannot be simultaneously connected to the RF chains during the channel estimation process. To tackle this issue, we propose a novel joint channel prediction and AS (JCPAS) framework to reduce the CSI acquisition time and improve the system performance under temporally correlated channels. Our proposed JCPAS framework is a fully probabilistic model driven by deep unsupervised learning. The proposed framework is able to predict the current full CSI, while requiring only a historical window of partial observations. Extensive simulation results show that the proposed JCPAS can significantly improve the system performance under temporally correlated channels, especially for very large-scale systems with highly correlated channels.

In this paper, the optimum tradeoff between diversity and multiplexing gains in a two-user quasi-static Rayleigh fading interference channel (IC) is studied. The diversity and multiplexing gains are two basic performance measures in wireless networks which characterize the transmission reliability and the data rate, respectively. It would be of interest to investigate the optimal tradeoff between these two measures. First, we develop a coding scheme for the two-user quasi-static Rayleigh fading Gaussian IC with interference level α := log INR/log SNR ≥ 1. Then, for this coding scheme the achievable diversity-multiplexing tradeoff (DMT) is characterized. Our achievable DMT coincides with its outer bound. In the low and high rate regions (to be defined later), the proposed coding scheme is a one-level Gaussian code, independent of the channel state information (CSI). In the middle rate region (to be defined later), the proposed coding scheme, depending on the partial CSI, can be a one-level or a two-level Gaussian code. We show that the relevant partial CSI can be represented by only one bit determined by the absolute value of the channel gain. In the middle rate region, we assume that the single-bit partial CSI for all the four channel gains of the Gaussian IC are available at both transmitters.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) is the promising technology for next generation communication systems due to high throughput. Due to the coherent receiving and demodulation at the receiver, accurate channel state information (CSI) is indispensable. Conventional rich assumption-based channel estimators have been proposed at the cost of enough training resource which leads to extra spectrum waste. However, physical measurements have verified that the wireless channels tend to exhibit sparse structure in high-dimensional space, e.g., delay spread, Doppler spread and space spread. Some sparse channel estimation methods for the MIMO-OFDM have been proposed. These estimation methods utilize either greedy algorithm or convex optimization. In this paper, we propose a novel sparse channel estimation method using sparse cognitive matching pursuit (SCMP) algorithm. Compared to other compressive algorithms in the state of art, the major innovation of the SCMP sparse channel estimation method (SCMP-SCE) is the ability of obtaining the accurate CSI without prior information of sparsity. Simulation results confirm that the proposed method has better estimation performance and lower estimation complexity.

It is well known that the performance of the wireless communication systems can be enhanced by using multiple transmit and receive antennas, which is generally referred to as the MIMO technique, and has been incorporated into the IEEE 802.11n standard, i.e. one of the Wi-Fi systems. The MIMO technique has also been adopted by the 3GPP Long Term Evolution (LTE) and the 3GPP2 Ultra Mobile Broadband (UMB) standards. The space-time coding is a promising way to realize the gain in the wireless communications system using MIMO. The space time block code, and the orthogonal space time block code in particular have been proposed in research and implemented in practical wireless communication systems due to their low computational complexity in maximum likelihood decoding implementation. However, the code rates of the orthogonal space time block codes are usually lower than $3/4$ when the number of transmit antennas is greater than two, which limit their throughput. To increase the code rate and the throughput of the orthogonal space time block code for more than two transmit antennas, a family of new space-time block codes (STBC) for 3 and 4 transmit antennas in a MIMO wireless communication system have been proposed in this Ph.D. research. These new codes are orthogonal and achieve full diversity, full rate using Triple QPSK'' modulation, which is proposed in this research. We investigated the performance of these STBCs when the channel state information (CSI) is perfectly known at the receiver. The CSI is not known to the transmitter in all of the studies in this dissertation. An analytical bound of the probability of bit error is developed and the Monte Carlo simulation is used when investigating these new STBC codes. In practical wireless communication system, the CSI is generally not available at the receiver and it is usually estimated by the receiver through the pilot signals or training signals known to the receiver. No matter what channel estimation method is used, the estimate of CSI will not match the CSI exactly and is noisy. The performance of these new STBCs using theTriple QPSK'' modulation when the estimate of the CSI is employed to detect the symbol is investigated in this dissertation. An upper bound of the bit error probability is developed when the noisy CSI is used to detect the symbols. The performance of the new STBCs using the Triple QPSK'' modulation is also evaluated using Monte Carlo simulation. When the CSI is not available to either the receiver or the transmitter, the differential encoding and decoding algorithm is applied to these new STBCs, which use theTriple QPSK'' modulation. The results showed that these orthogonal STBCs achieve full diversity full rate using ""Triple-QPSK"" modulation with differential detection. This provides a means of achieving space diversity and multiplexing gain without the CSI. A new differential encoding and decoding algorithm is proposed and analyzed for STBCs with 3 transmit antennas, whose transmission matrix is not square matrix, as part of this Ph.D. research. The STBCs using this algorithm achieve full diversity the MIMO system can possibly provide.

Early Terrestrial Digital Video Broadcasting Systems (DVB-T) were planned to work in Line-of-Sight (LOS) environments where a significantly high Signal to Noise Ratio (SNR) is guaranteed for all times. However, the recent DVB standards are sought to work in non-LOS conditions where the SNR is relatively low. Hence, in such environments, there is a need for a successful channel estimation and detection technique to take care of the fast channel fluctuations. This article is dealing with LS channel estimation and detection method for DVB-T signals over various multipath fading channel. The proposed method has been proven successful for up to 16 Quadrature Amplitude Modulation (QAM) constellations even when there is no LOS. However, the performance is found to be insufficient for an acceptable user experience for higher number of constellations. 

Orthogonal frequency division multiplexing (OFDM) systems suffer performance degradation in fast fading channels due to intercarrier interference (ICI). Combining frequency domain equalization and bit-interleaved coded modulation (BICM), the iterative receiver is able to harvest both temporal and frequency diversity. In order to perform coherent detection and estimation, channel state information (CSI) is indispensable. Being able to accurately and effectively acquire CSI in the fast time-varying channel is critical. Conventional frequency domain channel estimation (CE) methods have an irreducible error floor at high normalized Doppler frequency fdTs, since ICI corrupts the orthogonality among subcarriers. Considering that the fast time-varying channel is also a source of temporal diversity, CE ought to take place in the pre-FFT time domain. Realizing channel variations in time are often smooth, this paper proposes a time domain channel estimator using pilot symbol assisted modulation (PSAM) with complexity O(N), where N is the FFT size. Simulation results demonstrate that a PSAM system with channel estimation provides excellent trade-off between performance, complexity and spectral efficiency.

In this letter, we study and present the performance of multi-user transmitter preprocessing (MUTP) assisted downlink (DL) communications, where the channel state information (CSI) required at the transmitter to carry out the MUTP is estimated at the receiver, and conveyed to the base station (BS) with the aid of a noisy feedback channel that undergoes fading as well. In particular, the CSI is estimated with a known channel estimation technique, and the phases and magnitudes of the CSI are vector quantized and fed back to the transmitter via a fading feedback channel. This vector quantized CSI which is also corrupted by noise is recovered with the aid of a minimum mean square error (MMSE) based hard detector. The recovered CSI is then exploited to formulate the preprocessing matrix which is based on creating an orthogonal subspace to the interfering subspace. Our simulations show that the impact of codebook size and quantization errors play a dominant role in determining the performance of the considered DL system in terms of the achievable symbol error rate (SER).