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All-digital architectures enable taking full advantage of the large number of antennas that can be integrated into mmWave transceivers, with fully flexible beamforming that enables the number of simultaneous users K sharing the band to scale with the number of antennas N. The small carrier wavelength in these bands allows realization of antenna arrays with a large number of elements with a relatively small footprint, opening a path to truly massive Multiple Input Multiple Output (MIMO) systems. However, two key bottlenecks to realizing this potential are the cost/power consumption of Radio Frequency (RF) frontends at high carrier frequencies and the high complexity incurred in the digital baseband processing due to the large number of antennas. In this dissertation, we develop approaches for addressing these bottlenecks by adapting signal processing architectures and algorithms to hardware design considerations, while taking advantage of the unique characteristics of the mmWave band. We first develop an analytical model for the impact of nonlinearities such as RF amplifiers and Analog-to-Digital Converters (ADCs) on the performance of a mmWave massive MIMO uplink. We illustrate the utility of this model in providing specific guidelines for hardware design based on desired system-level perfor- mance. For example, the framework allows specification of the desired 1 dB compression point for RF amplifiers and the desired number of bits of ADC precision in order for the system outage at a target bit error rate to be below 5%. These hardware design prescriptions depend on coarse system-level parameters such as the number of antennas N,the number of simultaneous users K, and the maximum and minimum link distances to be supported. An important conclusion from the analytical framework is that hardware specifications can be substantially relaxed by reducing the load factor, defined as the ratio β = K/N.We then consider the problem of scaling digital signal processing in this regime. For a relatively small number of antennas and users, the Linear Minimum Mean Squared Error (MMSE) approach is a standard technique for handling multiuser interference at reasonable complexity. However, for fixed load factor β, the complexity of computing the LMMSE detector, as well as the complexity of using it for demodulation, grow polynomially with the number of antennas. We propose complexity reduction techniques that substantially improve scaling by taking advantage of the spatial sparsity of the mmWave channel. Specifically, we use a spatial Discrete Fourier transform (DFT) across antennas to create N discrete beams, transforming from antenna space to beamspace. We show that each user’s energy is concentrated in a small number of DFT bins in beamspace. Assuming ideal single path channels, we show that each user can be demodulated reliably using a local LMMSE detector which employs a beamspace window whose size does not scale with the number of antennas. The local LMMSE detector approaches the performance of standard LMMSE detection at substantially reduced complexity, and these performance-complexity tradeoffs become more favorable at lower system load factor β. For larger load factors, the beamspace window required by the local LMMSE detector increases, but we show that it is possible to scale well in such regimes by adding a layer of nonlinear interference cancellation on top of the local LMMSE receiver.Next, drawing on the duality between uplink linear multiuser detection and downlink linear precoding, we demonstrate the efficacy of beamspace techniques for linear precoding on the downlink, in order to reduce the interference seen by a given user due to signals directed at other users by the base station.Finally, we address the problem of simultaneous scaling of bandwidth and number of antennas. As bandwidth and hence symbol rate increase, the signal from a given user impinging on a large antenna array incurs a multi-symbol delay spread across the array, which smears the spatial frequency for each user across the band. We introduce a novel technique that combines DFTs in the spatial and time domains, together with an interpolation technique that limits the dispersion of spatial frequency across the band. We show that this results in significantly reduced complexity in computing LMMSE weights for uplink multiuser detection.

We consider performance comparison and application specific integrated circuit (ASIC) designs of linear minimum mean-square error (LMMSE) and K-best list sphere detector (LSD) algorithms for 4 × 4 and 8 × 8 multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Requirements for higher data rate and lower power consumption set new challenges for implementation. In order to minimize the power consumption, an optimal detector would be able to switch the detection algorithm to suit the channel conditions. The detectors are designed for three different modulation schemes using 28 nm complementary metal oxide semiconductor (CMOS) technology. The communications performance is evaluated in the Third Generation Partnership Project (3GPP) Long-Term Evolution (LTE) system. The impact of transmit precoding is considered. The ASIC designs aim at providing the hardware design aspects to the comparison of detectors. The designs are synthesized and complexity and power consumption results are found. Based on the ASIC synthesis and communications performance results, we show the performance–energy efficiency and performance–complexity comparison. We also present the most suitable scenarios for a low-power detector and show how the transmit precoding impacts the detector selection.

We explore an all-digital architecture for a mmWave massive MIMO cellular uplink in which the number of users scales with the number of antenna elements at the base station. We consider the design of multiuser detection strategies after a spatial DFT, which concentrates the energy of each user onto a few DFT bins in “beamspace.” In this paper, we propose and investigate a local LMMSE receiver that exploits this property, using a small window in beamspace to demodulate each user. The proposed architecture is computationally efficient: the required window size depends on load factor (the number of users divided by the number of antenna elements) and does not scale with the number of elements. We also show that adaptive implementations of such local LMMSE receivers naturally extend to provide implicit channel estimation.

This dissertation deals with the channel estimation techniques for orthogonal frequency division multiplexing (OFDM) systems such as in IEEE 802.11. Although there has been a great amount of research in this area, characterization of typical wireless indoor environments and design of channel estimation schemes that are both robust and practical for such channel conditions have not been thoroughly investigated. It is well known that the minimum mean-square-error (MMSE) estimator provides the best mean-square-error (MSE) performance given a priori knowledge of channel statistics and operating signal- to-noise ratio (SNR). However, the channel statistics are usually unknown and the MMSE estimator has too much computational complexity to be realized in practical systems. In this work, we propose two simple channel estimation techniques: one that is based on mod-ifying the channel correlation matrix from the MMSE estimator and the other one with averaging window based on the LS estimates. We also study the characteristics of several realistic indoor channel models that are of potential use for wireless local area networks (LANs). The first method, namely MMSE-exponential-Rhh, does not depend heavily on the channel statistics and yet offer performance improvement compared to that of the LS estimator. The simulation results also show that the second method, namely averaging window (AW) estimator, provides the best performance at moderate SNR range.

In this paper, the effect of a training-based linear minimum mean squared error (LMMSE) channel estimator on the sum mutual information of the multiple-input multiple-output (MIMO) multiple access channel (MAC) is investigated. Adhering to the classical channel estimation philosophy, the overarching contribution of the present work consists in bridging pure information-theoretic bounds on the sum mutual information with practical system parameters that are inherent to the LMMSE channel estimator. The unboundness of the mutual information and conservation of the multiplexing gain is shown and, interestingly, the increase of the mutual information loss with respect of the perfect channel knowledge case with the increasing of the signal-to-noise-ratio (SNR) is revealed, with a close-form expression for the value bounding the loss for asymptotically high SNRs.

Multiple Input Multiple Output with Orthogonal Frequency Division Multiplexing (MIMOOFDM) has been widely adopted as one of the most promising air interface solutions for future broadband wireless communication systems due to its high rate transmission capability and robustness against multipath fading. However, these MIMO-OFDM advantages cannot be achieved unless the channel state information (CSI) can be obtained accurately and promptly at the receiver to assist coherent detection of data symbols. Channel estimation and training sequence design are, therefore, still open challenges of great interest. In this work, we investigate the Linear Minimum Mean Square Error (LMMSE) channel estimation and design nearly optimal training sequences for MIMO-OFDM systems in spatially correlated fading. We, first, review the LMMSE channel estimation model for MIMO-OFDM in spatially correlated fading channels. We, then, derive a tight theoretical lower bound of the channel estimation Mean Square Error (MSE). By exploiting the information on channel correlation matrices which is available at the transmitter, we design a practical and nearly optimal training sequence for MIMO-OFDM systems . The optimal transmit power allocation for training sequences is found using the Iterative Bisection Procedure (IBP). We also propose an approximate transmit power allocation algorithm which is computationally more efficient than the IBP while maintaining a similar MSE performance. The proposed training sequence design method is also applied to MIMO-OFDM systems where Cyclic Prefixing OFDM (CP-OFDM) is replaced by Zero Padding OFDM - OverLap-Add method (ZP-OFDM-OLA). The simulation results show that the performance of the proposed training sequence is superior to that of all existing training sequences and almost achieves the MSE theoretical lower bound.

This paper is devoted to the performance study of the Linear Minimum Mean Squared Error estimator for multidimensional signals in the large dimension regime. Such an estimator is frequently encountered in wireless communications and in array processing, and the Signal to Interference and Noise Ratio (SINR) at its output is a popular performance index. The SINR can be modeled as a random quadratic form which can be studied with the help of large random matrix theory, if one assumes that the dimension of the received and transmitted signals go to infinity at the same pace. This paper considers the asymptotic behavior of the SINR for a wide class of multidimensional signal models that includes general multi-antenna as well as spread spectrum transmission models. \\ The expression of the deterministic approximation of the SINR in the large dimension regime is recalled and the SINR fluctuations around this deterministic approximation are studied. These fluctuations are shown to converge in distribution to the Gaussian law in the large dimension regime, and their variance is shown to decrease as the inverse of the signal dimension.