This paper proposes the joint use of digital self-interference cancellation (DSIC) and spatial suppression to mitigate far-field self-interference (SI) in full-duplex multiple-input multiple-output (MIMO) systems. Far-field SI, caused by echoes from environmental scatterers, is modeled based on the scatterers' angle and delay parameters, stored in a scatterer map. For each scatterer, the most suitable action regarding communication is selected from transmit beamforming, receive beamforming, DSIC, and no-action. This selection is based on simple metrics that show the expected uplink and downlink communication performance. In addition, emerging scatterers that deteriorate the communication are detected, and their delay and angles are acquired, providing an up-to-date scatterer map and presenting a \emph{sensing for communication} case. The proposed selection policy is compared with the individual implementations of DSIC and spatial suppression, highlighting the failure cases for each. It is shown that the proposed policy stays unaffected in these problematic cases and achieves SI-free performance.

Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique offering a promising solution for future generations of communications. This paper studies the capacity of FTN signaling in multiple-input multiple-output (MIMO) channels for high acceleration factors. In our previous study [1], we found the capacity for MIMO FTN channels if the acceleration factor is larger than a certain threshold, which depends on the bandwidth of the pulse shape used. In this paper we extend the capacity analysis to acceleration factors smaller than this mentioned threshold. In addition to capacity, we conduct instantaneous-to-average power ratio (IAPR) analysis and simulation for MIMO FTN for varying acceleration factors for both Gaussian and QPSK symbol sets. Our analysis reveals important insights about transmission power and received signal-to-noise ratio (SNR) variation in FTN. As the acceleration factor approaches 0, if the transmission power is fixed, the received SNR diminishes, or if the received SNR is fixed, IAPR at the transmitter explodes.

Accurate signal detection in wireless communication systems is heavily influenced by temporal and spatial channel variations, particularly in the presence of angular spread and Doppler effects. These challenges necessitate advanced signal processing strategies to achieve robust channel estimation. This paper presents an Angular and Doppler Spread Aware Orthogonal Matching Pursuit (OMP) algorithm tailored for Integrated Sensing and Communication (ISAC) systems. By employing basis decomposition in the angular domain and directly modeling Doppler-induced phase variations, the proposed method efficiently estimates channel coefficients while addressing the complexities introduced by channel dynamics. The approach improves the reliability of both sensing and communication functionalities, offering a more seamless integration of these tasks within ISAC frameworks.

Faster-than-Nyquist signaling serves as a promising solution for improving spectral efficiency in future generations of communications. However, its nature of fast acceleration brings highly overlapped pulses that lead to worse peak-to-average power ratio (PAPR) performance. In this paper, we investigate the PAPR behavior of MIMO FTN using Gaussian symbols under optimal power allocation for two power constraints: fixed transmit power and fixed received signal-to-noise-ratio (SNR). Our findings reveal that PAPR is mainly determined by the acceleration factor and the power constraint, but power allocation optimization does not change the PAPR behavior for Gaussian signaling.

In cell-free (CF) networks, access points (APs) jointly serve the user equipments (UEs). In user-centric (UC) variant, only a handful of APs serve a particular UE, namely the ones with the best channels. In this paper, we propose a hybrid beamforming aided uplink receiver scheme for highly loaded UC massive MIMO (mMIMO) networks, operating in millimeter-wave (mmWave) frequencies, employing single carrier modulation under frequency selective channels. Every AP employs its own analog beamformer (AB) called the modified eigen-beamformer (MEB), which promotes a trend of fairness. In the digital domain, two detectors are proposed; Cell-Free Iterative Detector (CFI-D) and User-Centric Iterative Subset-Detector (UCIS-D). CFI-D introduces the iterative block decision feedback equalization (IB-DFE) method for CF networks. UCIS-D is an alternative to CFI-D. UCIS-D is UC, has low computational complexity (CCP) and is scalable. UCIS-D relies on parallel interference cancellation (PIC) aided IB-DFE. Interfering UEs are divided into three sets. Weak interferers are processed with PIC, strong interferers with IB-DFE which considers the residual interference from PIC. Weakest interferers are ignored. Both PIC and IB-DFE utilize the soft decisions from previous iterations. Simulation results show that these receivers can handle ultra high loads in terms of bit-error-rate (BER) and achievable information rate (AIR).

This article provides a comprehensive analysis aimed at addressing the impact of power amplifier nonlinearities on massive multiple-input multiple-output (MIMO) communication systems. Specifically, we derive a closed-form expression for the received distortion power, a critical aspect that has not been explored in literature. Our derived expression takes into account the combined influence of spatial diversity and frequency-selectivity on received distortion, with a particular emphasis on the slowly varying parameters, primarily the locations of users and delay spread. In the context of massive MIMO systems, characterized by an exceptionally large number of antennas, our work highlights the crucial role of distortion correlation among antennas. Neglecting distortion correlation can result in significant inaccuracies in system evaluation and algorithmic design. Utilizing the analytical framework we have developed, we formulate a power allocation problem that explicitly exploits the distortion correlation. The proposed solution outperforms methods that neglect the spatial distortion correlation, particularly in terms of spectral efficiency (SE). This superior performance is crucial in optimizing the overall system performance and ensuring desirable power allocation decisions.

Integrating radar and communication systems for economical use of hardware and spectrum resources is projected to be a crucial aspect of sixth-generation (6G) systems, leading to extensive research in the integrated sensing and communication (ISAC) area. In this article, we propose a radar detector structure for a multiple-input multiple-output (MIMO) ISAC system using orthogonal frequency-division multiplexing (OFDM) modulated waveforms. These waveforms are utilized to communicate with downlink (DL) users while receiving echoes from targets and clutter, in addition to separate OFDM-modulated uplink (UL) communication waveforms. The transmitter (Tx) and receiver (Rx) employ hybrid beamformers, with analog beamformers precisely designed to cover flat angular sectors. Tx and Rx beams are directed towards DL and UL users, respectively, with the possibility of overlapping or separate angular sectors. We introduce a Doppler-aware Code Bank (DACB) as the initial processing stage, thoroughly investigating the effects of Doppler mismatch. Following DACB, a sub-optimal sequential angle-range processing (SARP) method is proposed to maximize the output signal-to-interference-plus-noise ratio (SINR) while maintaining feasible processing. A two-variant detector scheme is proposed to address this processing’s suboptimality. Four different detectors with varying complexities, including a fully adaptive detector, are introduced. The potential beam-squint (BS) effect due to increased bandwidth is also considered, and a subband approach is proposed to mitigate these effects. Simulation results for all four detectors, as well as a conventional 3-dimensional periodogram detector commonly used as a benchmark in the literature, are provided. The results demonstrate that the proposed detectors can significantly enhance SINR and the probability of detection, particularly when accounting for the coupling between the clutter Doppler and Tx OFDM symbols.

This paper aims to investigate the isolated effect of the far-field self-interference (SI) for full-duplex MIMO communications. It is shown that the far-field SI is effective only when the scatterers are close to the users. Due to this observation, an SI detector and delay estimator mechanism is proposed for a simpler operation. Additionally, two SI mitigation approaches are compared, namely SI cancellation and spatial SI suppression. Various methods are proposed and examined using SI channel estimate or scatterer angle. It is shown that both approaches can reach SI-free performance given a proper design.

In this study, we propose a method for estimating cascaded channel covariance matrices (C-CCMs) for millimeterwave (mmWave) reconfigurable intelligent surface (RIS)-aided massive multiple-input multiple-output (MIMO) systems. Our approach utilizes a generic ray tracing channel model, wherein each multipath cluster (MPC) comprises several rays scattered randomly around a mean angle, following a specific probability density function (pdf). Initially, we estimate the mean angles of arrival (AoAs) at the base station (BS). Subsequently, we introduce a subspace-aware (SA) orthogonal matching pursuit (OMP) algorithm to estimate mean cascaded angles-delays-Doppler shifts. These estimated parameters are then used to construct subspace basis matrices, from which initial CCM estimates are derived. We further refine the initial CCM estimates by fitting pdfs to their eigenvalues. The effectiveness of our proposed methods is demonstrated through root mean square error (RMSE), normalized mean square error (NMSE), and power angular spectrum (PAS) metrics.

Multiple input and multiple output (MIMO) technology and non-orthogonal multiple access (NOMA) are crucial methods for future wireless systems as they provide many advantages over conventional systems. Power domain NOMA methods are investigated in MIMO systems extensively, whereas there is little work on the integration of practical code domain NOMA schemes and MIMO, which is the subject of this study. We propose a generic maximum a posteriori (MAP) receiver, where sparse code multiple access (SCMA) and multi-user shared access (MUSA) can be directly adapted. The proposed technique combines the index modulation in code domain with channel division multiple access (ChDMA) on which a novel receiver architecture is structured at significantly reduced complexity for highly dispersive channels. It outperforms conventional methods with very limited number of radio frequency (RF) chains. Furthermore, we provide bit-error rate performance simulations and analysis in terms of achievable information rate (AIR) showing mismatched decoding capacity under different code lengths and overloading scenarios for uplink transmissions where flexible structure of MUSA is exploited.

Massive multiple-input multiple-output (MIMO) systems have gained a lot of attention due to their notable benefits with respect to spectral and energy efficiency. In order to make these systems more cost and power-efficient, using low-resolution digital-to-analog converters (DAC) at each antenna is a preferred implementation method. However, using such low-resolution DACs can introduce quantization distortion which needs to be mitigated by employing various quantized precoding algorithms. In this study, an iterative oversampling precoder (IDAPR) that can be used with quantized massive MIMO under Rician fading channels is presented. It is demonstrated IDAPR outperforms existing oversampling precoders of similar computational complexity with respect to bit error-rate and/or out-of-band (OOB) emissions under spatially correlated mmWave Rician fading channels. Additionally, it is shown that IDAPR can achieve the performance of unquantized precoders for many cases.

Massive multiple-input multiple-output (MIMO) systems have received significant attention in academia and industry for their remarkable advantages regarding spectral and energy efficiency. Employing a pair of low-resolution digital-to-analog converters (DAC) at each antenna of the massive MIMO array is a cost and power efficient implementation scheme for such systems. To mitigate the resulting quantization distortion introduced by low-resolution DAC, various quantized precoding algorithms are proposed in the related literature. This study proposes an iterative distortion-aware precoder that can work with oversampling DACs in massive MIMO systems. We show that the precoder we propose outperforms the existing oversampling precoders of comparable computational complexity in the literature in terms of error-rate and/or out-of-band (OOB) emission performances. We also show that the proposed detector can perform close to the ideal unquantized zero-forcing precoder in many presented cases.

This study investigates an interference-aware (IA) and beam-squint-aware (BSA) statistical reconfigurable intelligent surface (RIS) design that considers the practical amplitude-phase coupling for RIS-aided spatial wideband massive multiple-input multiple-output (MIMO) systems. First, we find the optimal ideal IA-BSA statistical frequency-independent and hypothetical frequency-dependent RIS designs, which are based on maximizing the signal-to-interference-and-noise ratio (SINR) and statistical mutual information (SMI), respectively. In these unconstrained designs, the amplitudes and phases of the RIS elements are assumed to take arbitrary values independently. Second, we propose a frequency-independent statistical quantized IA-BSA RIS beamforming based on the mean SMI maximization considering the amplitude-phase coupling of the practical RIS structures. The proposed practical constrained design is obtained by a computationally efficient iterative phase refinement (IPR) algorithm based on alternating optimization (AO). The computational complexity of the proposed RIS design is presented, and its performance is evaluated in terms of beam pattern, outage capacity, and spectral efficiency (SE).

Nonlinear distortion stemming from low-cost power amplifiers may severely affect wireless communication performance through out-of-band (OOB) radiation and in-band distortion. The distortion is correlated between different transmit antennas in an antenna array, which results in a beamforming gain at the receiver side that grows with the number of antennas. In this paper, we investigate how the strength of the distortion is affected by the frequency selectivity of the channel. A closed-form expression for the received distortion power is derived as a function of the number of multipath components (MPCs) and the delay spread, which highlight their impact. The performed analysis, which is verified via numerical simulations, reveals that as the number of MPCs increases, distortion exhibits distinct characteristics for in-band and OOB frequencies. It is shown that the received in-band and OOB distortion power is inversely proportional to the number of MPCs, and it is reported that as the delay spread gets narrower, the in-band distortion power is beamformed towards the intended user, which yields higher received in-band distortion compared to the OOB distortion.

Massive MIMO and non-orthogonal multiple access (NOMA) are key technologies for next generation wireless systems due to their distinct advantages. We previously proposed a general framework on unification of code-domain NOMA and joint spatial division and multiplexing (JSDM) which is a spatial user-grouping based hybrid beamforming method for massive MIMO. In this paper, we propose a constrained analog beam-former design for JSDM based systems. Moreover, we propose joint group processing for the digital beamformer in order to reduce the interference introduced by constrained beamformers. We provide a comprehensive performance evaluation for different operational modes of JSDM with code-domain NOMA when constrained analog beamformers are utilized.

In this study, a novel iterative turbo digital self interference cancellation (DSIC) algorithm is proposed to eliminate remaining self interference (SI) signal, which is contaminated by signal of interest (SoI), subject to time varying SI channel. SoI creates a noise floor for the estimation of SI signal; hence, estimation accuracy significantly degrades. In order to improve the estimation accuracy, it is proposed that for each block, SoI is decoded via signal decomposition based on generalized memory polynomial (GMP) and neural network (NN) nonlinear regressors and its copy subtracted from received signal iteratively so that SoI contamination is decreased. This procedure is performed in an online manner during data transmission stage; therefore, it does not yield extra training overhead. In addition, since each block is processed independently, the proposed algorithm can track varying channel efficiently. Both numerical results and hardware implementations are presented to demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose a detector structure for fully connected hybrid massive MIMO systems with transceiver impairments at user equipments. In literature, memoryless compensation of nonlinear effects at the receiver side is recently proposed for systems employing frequency domain equalization. However, in these studies, the system model is restricted to be symbol sampled, where pulse shaping and fractional delays in channel impulse response (CIR) are not taken into consideration. However, memory effects of nonlinear distortion become more apparent when the system is considered in the upsampled signal domain. In this study, we propose a receiver architecture, where the received signal of each user is nonlinearly modified by the post-distortion unit with memory to recover the transmitted symbols. In addition, based on the proposed receiver structure, a novel detection criterion is introduced in order to compensate the distortion amplification due to fractional delays in the CIR. Lastly, performance improvement brought by the proposed detector is verified by numerical simulations.

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.

In this paper, we propose a receiver structure for single-carrier uplink transmission with frequency domain equalization (FDE) that is exposed to power amplifier (PA) nonlinearities. A two-stage approach is adopted, in which linear communication channel is equalized at the first stage, and it is followed by post-distortion, where nonlinear distortion is reduced. In the literature, nonlinear processing techniques are proposed, which perform memoryless compensation of nonlinear distortion together with FDE. However, in this study, we show that even if memoryless nonlinearity exists, the received signal is impaired by nonlinear inter-symbol-interference. Therefore, we propose a class of symbol rate post-distortion techniques, which use neighboring received symbols to suppress the nonlinear interference. Three different post-distortion methods, Gaussian process regression (GPR), neural network (NN) and Volterra series (VS) based post-distorters, are considered. Also, a combiner, which intelligently combines the outputs of fractional delayed bank of FDE’s after post-distortion, is proposed to overcome performance degradation of FDE for frequency selective channels under nonlinear distortion. Performances of the proposed techniques are compared with state-of-the-art approaches in terms of bit error rate (BER) and achievable information rate (AIR) metrics. Simulation results demonstrate that post-distortion methods together with bank of FDE outperform state-of-the-art techniques.

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. 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 first derive linear minimum mean-square-error channel estimator for cyclic-prefix (CP) free single carrier MIMO (SC-MIMO) under frequency-selective channel. Then, we propose a novel iterative receiver for quantized CP-free uplink wideband SC-MIMO for uncorrelated or correlated Rayleigh and Rician fading channels. This detector utilizes an efficient message passing algorithm based on Bussgang decomposition, reduced state sequence estimation and Ungerboeck factorization. Therefore, it achieves remarkable complexity reduction and exhibits significant performance advantages compared to the existing quantized SC-MIMO receivers from the literature.

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.

In this paper, we present a general framework for the investigation of the effects of power amplifier (PA) non-linearities on the hybrid beamforming based massive multiple-input multiple-output (MIMO) systems. In literature, much of the attention is devoted to phase shifters as the analog beamformer stage; however, in this work, a more comprehensive scenario, based on fully connected analog beamformer architecture, is considered. Firstly, out-of-band (OOB) radiation pattern is evaluated to investigate the spatial characteristics of the undesired radiation. It is observed that OOB radiation due to nonlinear distortion is concentrated on the beamforming directions. In addition, a framework is proposed to design digital predistorters (DPD) and a conventional DPD architecture is adopted to the proposed design framework. It is shown that conventional DPD can solve the OOB problem for a single user environment; however, its performance degrades in the multi-user case. Even though, there are many studies on the effects of nonlinear PA distortions, a few of them consider the achievable information rate (AIR). In this work, a framework is adopted to study the capacity of the considered system under PA nonlinearities.

In this paper, an efficient hybrid beamforming architecture together with a novel spatio-temporal receiver processing is proposed for single-carrier (SC) mm-wave wideband massive MIMO channels in time-domain duplex (TDD) mode. 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 receiver processing is proposed. Furthermore, a least square type channel estimator, based on a proper subspace projection via radio frequency (RF) processing, is proposed. It is shown to achieve a remarkable robustness to pilot contamination with a significant reduction in pilot length. Moreover, the effect of utilizing inaccurate estimates for channel covariance matrices (CCMs) on achievable information rate (AIR) is analyzed. The sensitivity of proposed techniques with respect to estimation accuracy of central angle and the angular spread (AS) of angle-of-arrival (AoA) values is investigated.

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.

This work presents a single-carrier massive MIMO transmission system for the frequency selective Gaussian multi-user channel. It considers both cases of spatially uncorrelated and correlated channel and compares them in terms of the user sum-rate as well as the general performance. We consider a channel with M antennas at the base station which provides services for K single- antenna users. We develop a general expression for the achievable rate among users in the channel with a correlation among antennas at the base station. It has been shown that, when there is no correlation between base station antennas or users, the channel matched filter precoder outperforms any other precoder. In a highly- correlated massive MIMO channel however, the conventional channel matched filter precoder does not perform as expected. We show the failure of the channel matched filter in the presence of a correlation pattern among antennas at the base station with theoretical analysis and simulations. We apply three different precoders to enhance the achievable rate and their performances are determined by simulations. We show that the precoders outperform the channel matched filter in a highly-correlated channel with a large number of users. By increasing the number of users in the system, the conventional precoders show even better performance in terms of the user's sum- rates.

When the requirements of internet of things (IoT) and machine-type communication (MTC) are considered, the 5G technology needs to support high spectral efficiency and massive connectivity of users/devices, and the demand for low latency, short packet duration, low power, high mobility and diverse service types. In this paper, first a new multiple access technique belonging to the class of non-orthogonal multiple access (NOMA) based on multi-code signaling (MCS) is proposed for the uplink data transmission mode in severe multi-path fading channels in order to handle the key challenges of 5G technology. This new technique combines the index modulation in code domain with channel division multiple access (ChDMA) on which a novel receiver architecture is structured at significantly reduced complexity. Second, an efficient message passing algorithm (MPA), which realizes iterative maximum a posteriori probability (MAP) detection with linear complexity in the number of interfering users, is proposed. The novel feature of the proposed MPA stems from the use of Ungerboeck factorization, where the whitening filter is avoided before MAP detection. The joint task of equalization and multi-user detection (MUD) is fulfilled via the introduced Ungerboeck type sum-product algorithm (SPA) framework. This architecture for general NOMA transmission, operating on the resultant Ungerboeck type factor-graph (FG) with bidirectional feedback, achieves dramatic complexity reduction (compared to conventional FG based algorithms).

In this paper, the problem of sequential beam construction and adaptive channel estimation based on reduced rank (RR) Kalman filtering for frequency-selective massive multiple-input multiple-output (MIMO) systems employing single-carrier (SC) in time division duplex (TDD) mode are considered. In two-stage beamforming, a new algorithm for statistical pre-beamformer design is proposed for spatially correlated time-varying wideband MIMO channels under the assumption that the channel is a stationary Gauss-Markov random process. The proposed algorithm yields a nearly optimal pre-beamformer whose beam pattern is designed sequentially with low complexity by taking the user-grouping into account, and exploiting the properties of Kalman filtering and associated prediction error covariance matrices. The resulting design, based on the second order statistical properties of the channel, generates beamspace on which the RR Kalman estimator can be realized as accurately as possible. It is observed that the adaptive channel estimation technique together with the proposed sequential beamspace construction shows remarkable robustness to the pilot interference. This comes with significant reduction in both pilot overhead and dimension of the pre-beamformer lowering both hardware complexity and power consumption.

In this paper, we propose a generic receiver based on optimum maximum likelihood sequence estimation (MLSE) employing a generalized Ungerboeck metric with significantly reduced complexity for the general class of M-ary quasi orthogonal signaling (MOS) in severe multipath fading channels. First, by using the factor graph (FG) of the obtained generalized Ungerboeck metric for MOS and sum-product algorithm (SPA) framework, a highly efficient reduced state sequence estimation (RSSE) algorithm is proposed that operates on forward and backward directions and is directly applied to unwhitened channel matched filter (CMF) and code matched filter outputs at symbol rate. The proposed structure, generating the a posteriori probabilities (APP) by bidirectional RSSE recursions, are substantiated with symbol rate bidirectional decision feedback (BDF) based on surviving paths in order to eliminate the post- and pre-cursor inter-symbol interference (ISI) as well as multi code interference (MCI) due to the non-ideal properties of the signaling waveforms and multipath channel. It appears as the unification of Ungerboeck type reduced trellis equalization framework for MOS and achieves near-optimum performance especially for channels with large dispersion. Second, we identify a bias term through an error probability analysis which helps us improve the proposed receiver's performance. Furthermore, a tight approximation for the bit error rate (BER) of the proposed receiver is derived. These analyses lead to significant insight on the selection of system parameters and clearly demonstrate the high performance and efficiency of the proposed scheme by a proper choice of the signaling parameters.

The paper proposes a general framework for both time-domain (TD) and frequency-domain (FD) iterative blockwise equalization in single carrier (SC) wideband multiple-input multiple-output (MIMO) channels. First, a novel turbo blockwise operating equalizer structure is proposed by jointly optimizing the feed-forward and feedback filters at each iteration based on the minimum mean squared error (MMSE) criterion. Optimization of the filter coefficients, utilized for feed-forward equalization and decision feedback, is performed in both time and frequency domains, and the equivalence between them is established by unifying the iterative block equalization with the feed-forward and feedback filters in various domains. The corresponding filters are obtained analytically in a closed form as a solution of the constrained Wiener-Hopf equation with the use of average reliability information. Second, asymptotic performance and diversity analysis of the proposed single carrier frequency domain equalizer (SC-FDE) is carried out that leads to insights on the structures of the filters in the high SNR regime. Furthermore, we elaborate on the rate and diversity tradeoff of the space-time coded SC-FDE based on the proposed equalizer structure and the space-frequency coded OFDM systems. Performance comparison between the proposed SC-FDE scheme and OFDM is made. The proposed SC-FDE scheme performs very close to the genie-aided performance bounds and better than OFDM based transmission.

In this paper, we propose a general framework for both time domain (TD) and frequency domain (FD) block iterative equalization schemes for wideband multiple-input multiple-output (MIMO) channels. The equalization systems are designed by jointly optimizing the forward and backward filters based on minimum mean square error (MMSE) criterion. The equivalence of block iterative TD and FD equalization with decision feedback is established by analytically showing that the same mean square error (MSE) is achieved by the two systems. Asymptotic performance of the studied block iterative FD equalization scheme is analyzed, and the rate-diversity tradeoffs of space time / frequency coded MIMO SC-FDE and MIMO OFDM are compared.

The case of rotation, in use together with coding in a channel with B fading blocks, is analyzed in terms of diversity properties of the coded systems with multi-dimensional rotated constellations (coded-rotated system) output. First closed-form expression obtained is related to the diversity performance of unrotated generic coding structures with given minimum distance, d, block length, and number of fading blocks. Afterwards, another closed-form expression is derived for distinguishing the effect of rotation applied on the given coding structures. This latter expression yields a strict lower bound on the probability of attaining full diversity for any coded-rotated system and gives us a tool to evaluate the improvement with respect to systems involving only coding and also systems with only rotation. The results shown summarize the benefits of utilizing rotation together with coding, even in the cases of very simple non-full-diversity rotations like DFT rotations.

Single carrier frequency domain equalization (SC-FDE) is receiving considerable attention recently due to its comparable complexity and performance with OFDM. In this paper, an iterative SC-FDE with decision feedback in frequency domain is proposed for wideband multiple-input multiple-output (MIMO) channels and more general multipath vector channels as a generalization of previous works considering FDE with decision feedback for single-input single-output (SISO) systems. The proposed detector is based on iterative forward and backward filtering followed by a MIMO multi-stream detector that uses a priori log-likelihood ratios (LLR) of coded symbols. Forward and feedback filters are jointly optimized according to the minimum mean square error (MMSE) criterion to minimize both self inter-symbol-interference (ISI) and interference from other streams transmitted at different antennas. It has been observed that the proposed structure exploits the multi-path diversity sources of the channel effectively and a performance very close to MIMO-OFDM outage probability can be achieved. Therefore, our proposed iterative SC-FDE technique for MIMO wideband channels can be viewed as a strong alternative to MIMO-OFDM schemes with similar complexity.

It is known that rotated multidimensional constellations can be used effectively to achieve full-rate and full-diversity transmission in block fading channels. However, optimal decoding complexity is exponential with the number of fading blocks (or degrees of freedom). In this paper, we propose a reduced-complexity iterative receiver structure operating on a block basis for coded modulation schemes with rotated constellations. The proposed detector is based on iterative forward and backward filtering followed by a channel decoder that uses a priori log- likelihood ratios (LLR) of coded symbols. Forward and feedback filters are jointly optimized according to the minimum mean square error (MMSE) criterion to minimize the spatial interference induced by rotation. It is observed that the proposed structure achieves full diversity and performance close to outage probability for rotated inputs even with simple Discrete Fourier Transform (DFT) rotations.

We develop a technique to upper bound the point-to-point MIMO limited rate feedback (LRF) capacity under a wide class of vector quantization schemes. The upper bound turns out to be tight and can also be used to obtain an absolute upper bound by using a bounding distribution for Grassmannian beamforming. The bounding technique can be applied to other problems requiring the exact evaluation of the expected value of matrix determinant.