Pilot Decontamination in CMT-based Massive MIMO Networks

Pilot contamination problem in massive MIMO networks operating in time-division duplex (TDD) mode can limit their expected capacity to a great extent. This paper addresses this problem in cosine modulated multitone (CMT) based massive MIMO networks; taking advantage of their so-called blind equalization property. We extend and apply the blind equalization technique from single antenna case to multi-cellular massive MIMO systems and show that it can remove the channel estimation errors (due to pilot contamination effect) without any need for cooperation between different cells or transmission of additional training information. Our numerical results advocate the efficacy of the proposed blind technique in improving the channel estimation accuracy and removal of the residual channel estimation errors caused by the users of the other cells.Comment: Accepted in ISWCS 201

Frequency Spreading Equalization in Multicarrier Massive MIMO

Application of filter bank multicarrier (FBMC) as an effective method for signaling over massive MIMO channels has been recently proposed. This paper further expands the application of FBMC to massive MIMO by applying frequency spreading equalization (FSE) to these channels. FSE allows us to achieve a more accurate equalization. Hence, higher number of bits per symbol can be transmitted and the bandwidth of each subcarrier can be widened. Widening the bandwidth of each subcarrier leads to (i) higher bandwidth efficiency; (ii) lower complexity; (iii) lower sensitivity to carrier frequency offset (CFO); (iv) reduced peak-to-average power ratio (PAPR); and (iv) reduced latency. All these appealing advantages have a direct impact on the digital as well as analog circuitry that is needed for the system implementation. In this paper, we develop the mathematical formulation of the minimum mean square error (MMSE) FSE for massive MIMO systems. This analysis guides us to decide on the number of subcarriers that will be sufficient for practical channel models.Comment: Accepted in IEEE ICC 2015 - Workshop on 5G & Beyond - Enabling Technologies and Application

Channel Rectification and Signal Estimation Based on EIV Model in Massive MIMO System

The performance of Massive MIMO is severely limited by channel estimation error, which is caused by pilot contamination and channel aging. In this paper, we propose an estimation algorithm based on the weighted total least-squares method with errors-in-variables (EIV) model to alleviate the influence of pilot contamination and channel aging. Then, a channel rectification method has been investigated to diminish the inaccuracy of channel estimation. Comparing with the traditional methods, it not only helps to make the signal estimation more accurate, but also provides opportunities to correct the channel model with estimation error and update the aged channel statement information. Simulations are provided to verify the efficacy of this method

Doctor of Philosophy

dissertationThe use of multicarrier techniques has allowed the rapid expansion of broadband wireless communications. Orthogonal frequency division multiplexing (OFDM) has been the most dominant technology in the past decade. It has been deployed in both indoor Wi-Fi and cellular environments, and has been researched for use in underwater acoustic channels. Recent works in wireless communications include the extension of OFDM to multiple access applications. Multiple access OFDM, or orthogonal frequency division multiple access (OFDMA), has been implemented in the third generation partnership project (3GPP) long- term evolution (LTE) downlink. In order to reduce the intercarrier interference (ICI) when user's synchronization is relaxed, filterbank multicarrier communication (FBMC) systems have been proposed. The first contribution made in this dissertation is a novel study of the classical FBMC systems that were presented in 1960s. We note that two distinct methods were presented then. We show that these methods are closely related through a modulation and a time/frequency scaling step. For cellular channels, OFDM also has the weakness of relatively large peak-to-average power ratios (PAPR). A special form of OFDM for the uplink of multiple access networks, called single carrier frequency division multiple access (SC-FDMA), has been developed to mitigate this issue. In this regard, this dissertation makes two contributions. First, we develop an optimization method for designing an effective precoding method for SC-FDMA systems. Second, we show how an equivalent to SC-FDMA can be developed for systems that are based on FBMC. In underwater acoustic communications applications, researchers are investigating the use of multicarrier communication systems like OFDM in underwater channels. The movement of the communicating vehicles scales the received signal along the time axis, which is often referred to as Doppler scaling. To undo the signal degradation, researchers have investigated methods to estimate the Doppler scaling factor and restore the original signal using resampling. We investigate a method called nonuniform fast Fourier transform (NUFFT) and apply that to increase the precision in the detection and correction of the Doppler scaling factor. NUFFT is applied to both OFDM and FBMC and its performance over the experimental data obtained from at sea experiments is investigated

Downlink Transmission in FBMC-based Massive MIMO with Co-located and Distributed Antennas

This paper introduces a practical precoding method for the downlink of Filter Bank Multicarrier-based (FBMC-based) massive multiple-input multiple-output (MIMO) systems. The proposed method comprises a two-stage precoder, consisting of a fractionally spaced prefilter (FSP) per subcarrier to equalize the channel across each subcarrier band. This is followed by a conventional precoder that concentrates the signals of different users at their spatial locations, ensuring each user receives only the intended information. In practical scenarios, a perfect channel reciprocity may not hold due to radio chain mismatches in the uplink and downlink. Moreover, the channel state information (CSI) may not be perfectly known at the base station. To address these issues, we theoretically analyze the performance of the proposed precoder in presence of imperfect CSI and channel reciprocity calibration errors. Our investigation covers both co-located (cell-based) and cell-free massive MIMO cases. In the cell-free massive MIMO setup, we propose an access point selection method based on the received SINRs of different users in the uplink. Finally, we conduct numerical evaluations to assess the performance of the proposed precoder. Our results demonstrate the excellent performance of the proposed precoder when compared with the orthogonal frequency division multiplexing (OFDM) method as a benchmark.Comment: arXiv admin note: text overlap with arXiv:2201.1073

Channel Estimation for Multicell Multiuser Massive MIMO Uplink Over Rician Fading Channels

Pilot contamination (PC) is a major problem in massive multiple-input multiple-output (MIMO) systems. This paper proposes a novel channel estimation scheme for such a system in Rician fading channels. First, the possible angle of arrivals (AOAs) of users served by a base station (BS) are derived by exploiting the channel statistical information, assuming a traditional pilot structure, where the pilots for the same-cell users are orthogonal but are identical for the same-indexed users from different cells. Although with this pilot structure the channel state information (CSI) derived contains CSI from other-cell users caused by PC, the line-of-sight (LOS) component of the desired user is PC-free when the number of antennas equipped at the BS is large. Then, based on the AOAs and the contaminated CSI, the LOS component of each user served by a BS is estimated, and data are detected by using the derived LOS components. Finally, the decoded data are used to update the CSI estimate via an iterative process. The achievable spectral efficiency of the proposed scheme is analyzed in detail, and simulation results are presented to compare the performance of the proposed scheme with that of three existing schemes

Advanced wireless communications using large numbers of transmit antennas and receive nodes

The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. First, we propose practical open-loop and closed-loop training frameworks to reduce the overhead of the downlink training phase. We then discuss efficient CSI quantization techniques using a trellis search. The proposed CSI quantization techniques can be implemented with a complexity that only grows linearly with the number of transmit antennas while the performance is close to the optimal case. We also analyze distributed reception using a large number of geographically separated nodes, a scenario that may become popular with the emergence of the Internet of Things. For distributed reception, we first propose coded distributed diversity to minimize the symbol error probability at the fusion center when the transmitter is equipped with a single antenna. Then we develop efficient receivers at the fusion center using minimal processing overhead at the receive nodes when the transmitter with multiple transmit antennas sends multiple symbols simultaneously using spatial multiplexing