A Reduced Complexity Ungerboeck Receiver for Quantized Wideband Massive SC-MIMO

Employing low resolution analog-to-digital converters in massive multiple-input multiple-output (MIMO) has many advantages in terms of total power consumption, cost and feasibility of such systems. However, such advantages come together with significant challenges in channel estimation and data detection due to the severe quantization noise present. In this study, we propose a novel iterative receiver for quantized uplink single carrier MIMO (SC-MIMO) utilizing an efficient message passing algorithm based on the Bussgang decomposition and Ungerboeck factorization, which avoids the use of a complex whitening filter. A reduced state sequence estimator with bidirectional decision feedback is also derived, achieving remarkable complexity reduction compared to the existing receivers for quantized SC-MIMO in the literature, without any requirement on the sparsity of the transmission channel. Moreover, the linear minimum mean-square-error (LMMSE) channel estimator for SC-MIMO under frequency-selective channel, which do not require any cyclic-prefix overhead, is also derived. We observe that the proposed receiver has significant performance gains with respect to the existing receivers in the literature under imperfect channel state information.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Kablosuz sönümleme kanallar için kapasiteye yakın çalışan pratik alıcı-verici yapıları.

Multiple-input multiple-output (MIMO) systems have received much attention due to their multiplexing and diversity capabilities. It is possible to obtain remarkable improvement in spectral efficiency for wireless systems by using MIMO based schemes. However, sophisticated equalization and decoding structures are required for reliable communication at high rates. In this thesis, capacity achieving practical transceiver structures are proposed for MIMO wireless channels depending on the availability of channel state information at the transmitter (CSIT). First, an adaptive MIMO scheme based on the use of quantized CSIT and reduced precoding idea is proposed. With the help of a very tight analytical upper bound obtained for limited rate feedback (LRF) MIMO capacity, it is possible to construct an adaptive scheme varying the number of beamformers used according to the average SNR value. It is shown that this strategy always results in a significantly higher achievable rate than that of the schemes which does not use CSIT, if the number of transmit antennas is greater than that of receive antennas. Secondly, it is known that the use of CSIT does not bring significant improvement over capacity, when similar number of transmit and receive antennas are used; on the other hand, it reduces the complexity of demodulation at the receiver by converting the channel into noninterfering subchannels. However, it is shown in this thesis that it is still possible to achieve a performance very close to the outage probability and exploit the space-frequency diversity benefits of the wireless fading channel without compromising the receiver complexity, even if the CSIT is not used. The proposed receiver structure is based on iterative forward and backward filtering to suppress the interference both in time and space followed by a spacetime decoder. The rotation of multidimensional constellations for block fading channels and the single-carrier frequency domain equalization (SC-FDE) technique for wideband MIMO channels are studied as example applications.M.S. - Master of Scienc

Dağıtıcı kanallarda çok kodlu sinyalleşme için düşük karmaşıklıklı ungerboeck tipinde alıcı yapıları.

The main aim in this thesis is to propose multiple signaling waveforms (multi-code) based yet spectrally efficient modulation schemes and competent receiver architectures realizing soft-input-soft-output (SISO) detection. We search for generic suboptimal receiver architectures for Multi-Code Signaling (MCS), which can be represented as selection of one out of M waveforms per signaling interval. The proposed receiver architectures exhibit almost optimal performance at significantly reduced complexity in highly dispersive channels. First, an efficient reduced complexity implementation of the Ungerboeck type Maximum a Posteriori (MAP) receiver, directly operating on unwhitened channel matched filter and code matched filter outputs, is proposed for MCS by forming the factor graph (FG) and sum-product algorithm (SPA) framework. The proposed MAP receiver, generating the a posteriori probabilities by bidirectional reduced state sequence estimation (RSSE) recursions, is substantiated with symbol rate bidirectional decision feedback based on surviving paths in order to eliminate the post- and pre-cursor inter-symbol interference (ISI) as well as multi-code interference due to the non-ideal properties of the signaling waveforms and multipath channel. Second, we extend the proposed Ungerboeck receiver to be exploited in multiple access channel by unifying the bidirectional RSSE applied to each user and the mitigation of multi-user interference fulfilled by the SPA based on the obtained Ungerboeck type FG, resulting in linear complexity in the number of interfering users. Finally, error probability analysis, which provides significant insight on the success of the proposed reduced state Ungerboeck receivers in case of uncoded MCS transmission, and the packet error rate analysis based on the random coding approach that determines the cutoff rate for coded transmission are provided. These analyses help the designer determine system parameters and open up new possibilities for a performance enhancement of reduced complexity Ungerboeck receivers via a proper selection of a modulation scheme for the general class of MCS especially in long ISI channels. To sum up, the proposed receiver architectures here confirm, compare many previous works, and complement reduced complexity Ungerboeck structure by changing several system parameters generalized to MCS format with the help of the developed analytical tools.Ph.D. - Doctoral Progra

A Circular Postamble Structure Enabling Low Complexity Equalization in Frequency Domain for Noncausal Channels: Cyclic Suffix

Realizable root-raised cosine (RRC) filters have finite length. When these filters are used in transmitter and receiver side, frequency response of the effective filter does not have flat fading characteristic and it results two-sided intersymbol interference (ISI). In addition, relatively low up sampling factors result in synchronization errors and thus, two-sided ISI. Consequently, impulse response of the effective filter becomes noncausal. Moreover, nonlinearities in transmitter and receiver chains strengthen ISI. Such a channel-like behaviour of the noncausal effective filter can be overcome by conventional Half-Duplex systems where it should be avoided by In-Band Full-Duplex and higher order constellation systems due to being vulnerable to even low powered effects. In this article, we are providing a new cyclic postamble structure, namely Cyclic Suffix (CS), for constructing circularly symmetric convolution matrices for noncausal channels. CS provides the utilization of frequency domain equalization (FDE) in a low complex manner for cancelling the effects of such noncausal channels

An Adaptive Hybrid Beamforming Scheme for Time-Varying Wideband Massive MIMO Channels

© 2020 IEEE.In this paper, adaptive hybrid beamforming methods are proposed for millimeter-wave range massive MIMO systems considering single carrier wideband transmission in uplink data mode. A statistical analog beamformer is adaptively constructed in slow-time, while the channel is time-varying and erroneously estimated. Proposed recursive filtering approach is shown to bring a remarkable robustness against estimation errors. Then, analytical modifications are applied on an analog beamformer design method and approximated expressions are obtained for channel covariance matrices that decouple angular spread and center angle of multipath components. Resultant adaptive construction methods use only the estimated power levels on angular patches and they are shown to be very efficient such that they reduce computational complexity significantly while the performance remains almost the same

On the Impact of Fast-Time and Slow-Time Preprocessing Operations on Adaptive Target Detectors

Conventional adaptive detectors assume independent and identically distributed (iid) secondary data vectors which is not contaminated with the target signal. Yet, the input to the adaptive detectors are produced by preprocessing the raw data in fast-time and slow-time dimensions, in general. This paper mainly aims to study the impact of fast-time matched filtering on the adaptive target detectors, namely Kelly's detector, adaptive matched filter (AMF) and adaptive coherence estimator (ACE). It is shown that the application of matched filtering prior to the adaptive detection violates the requirements of conventional adaptive detectors unless the range side-lobes of the radar pulse is zero at all lags. An alternative preprocessing method, based on an unitary transformation mapping, is suggested and it is shown the alternative approach exactly satisfies the requirements. Numerical comparisons are provided to examine the performance gain of the suggested approach in comparison with the conventional one, i.e. fast-time matched filtering

A Nearly Optimal Hybrid Precoder Design for Downlink Single-Carrier Wideband Massive MIMO Channels

In this paper, an efficient hybrid precoding architecture is proposed for single-carrier (SC) downlink wideband spatially correlated massive MIMO channels. The design of two-stage beamformers is realized by using a virtual sectorization via second-order channel statistics based user grouping. The novel feature of the proposed architecture is that the effect of both inter-group-interference (due to non-orthogonality of virtual angular sectors) and the inter-symbol-interference (due to SC wideband transmission) are taken into account. While designing the analog beamformer, we examine the dimension reduction problem and proper subspace (beamspace) construction (by exploiting the joint angle-delay sparsity map and power profile of the multi-user channel) based on which a highly efficient spatio-temporal digital precoding is proposed

Efficient User Grouping for Hybrid Beamforming in Single Carrier Wideband Massive MIMO Channels

In this paper, three types of user grouping algorithms in which our own performance metric is utilized are investigated for single carrier downlink wideband spatially correlated massive MIMO channels by using hybrid beamforming structure motivated by the joint spatial division and multiplexing (JSDM) framework. The user grouping procedure consists of two stages. Internally, our own metric called as the achievable information rate (AIR) is calculated given a user grouping input by considering both inter-group and inter-symbol interferences. This metric value shows how efficient the given user grouping input is for the user scenario in the system. Externally, three different user grouping algorithms use this value to find the most efficient user grouping for the user scenario in the system. Based on the simulation results, AIR metric based user grouping via merge and split algorithm gives the nearly optimal user grouping set for the hybrid beamforming structure compared to the fully digital block zero forcing with multi-carrier frequency bound

An efficient spatial channel covariance estimation via joint angle-delay power profile in hybrid massive MIMO systems

© 2020 IEEE.In this paper, an efficient construction method for channel covariance matrices (CCMs) together with joint angle-delay power profile (JADPP) and sparsity map estimation is proposed for single-carrier (SC) mm-wave wideband massive multiple-input multiple-output (MIMO) channels when hybrid beamforming architecture is utilized. We consider slow-time beam acquisition mode for training stage of time division duplex (TDD) based systems where pre-structured hybrid beams are formed to scan intended angular sectors. The joint angle-delay sparsity map together with power intensities of each user channels is obtained by using a novel constant false alarm rate (CFAR) thresholding algorithm inspired from adaptive radar detection theory. The proposed thresholding algorithm employs a spatiooral adaptive matched filter (AMF) type estimator, taking the strong interference due to simultaneously active multipath components (MPCs) of different user channels into account, in order to estimate JADPP of each user. After applying the proposed thresholding algorithm on the estimated power profile, the angle-delay sparsity map of the massive MIMO channel is constructed, based on which the CCMs are formed with significantly reduced amount of training snapshots. The proposed techniques attain the channel estimation accuracy of minimum mean square error (MMSE) filter with true knowledge of CCMs. At the same time, they allow non-orthogonal pilot sequences among different users while reducing the training overhead (which is basically constant with the number of active users in the system) considerably

A General Framework and Novel Transceiver Architecture Based on Hybrid Beamforming for NOMA in Massive MIMO Channels

© 2020 IEEE.In this paper, a generic user-grouping based hybrid beamforming framework for mm-wave massive MIMO systems with code domain non-orthogonal multiple access (NOMA), which is considered as an intra-group process, is proposed. It is shown that message passing algorithm (MPA) for decoding sparse code multiple access (SCMA) can be directly applied in this framework without additional complexity. While classical multi-user shared access (MUSA) receiver is adapted for downlink, a novel receiver architecture which is an improvement over classical one is proposed for uplink MUSA. This receiver makes MUSA preferable over SCMA for uplink transmission with lower complexity. In addition, simulation results showed that code domain NOMA methods outperform conventional methods where users are spatially close to each other and number of radio frequency (RF) chains at the base station (BS) is very limited