This work focuses on providing robust line-of-sight (LoS) spatial multiplexing at flexible communications distances and directions. Considering oblique LoS uniform linear arrays, we first derive the rank-deficient and orthogonal conditions for the LoS MIMO channel matrices. With this discovery, the topology of high spatial-resolution on one side of the link is shown with a wide full-rank-channel guarantee interval over distance and direction variations. Additionally, to reduce the implementation costs and power consumption, we propose to use low amplitude-resolution quantizers at the side of high spatial-resolution. With numerical evaluations on systems having few-bit analog-to-digital converters (ADCs), the proposed system design is shown to simultaneously achieve a higher spectrum efficiency and higher energy efficiency compared to a conventional single-stream high-amplitude-resolution over a wide signal-to-noise-ratio (SNR) range. Furthermore, we investigate channel equalization under the extreme case of using 1-bit ADCs. After providing a new viewpoint on the generalized approximate message passing (GAMP) algorithm from constrained Bethe free energy minimization, our simulations on bit-error-rates show that the GAMP algorithm can significantly reduce the performance degradation due to coarse quantization and can significantly outperform the Bussgang decomposition based linear minimum-mean-square-error estimator, especially at high SNRs.

In this paper, we investigate a sparse channel estimation problem for broadband massive multiple-input-multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. We propose a hidden Markov model to capture the structured sparsity and temporal dependency characteristic of massive MIMO-OFDM channels in the angle-delay domain, and this probability model exhibits extensive adaptability to different realistic propagation scenarios. Then we solve the channel estimation problem based on a novel optimization framework named constrained Bethe free energy (BFE) minimization, which is valid for a generic statistical model. Under this systematic theoretical framework, a hierarchical hybrid message passing (HHMP) algorithm is proposed to track dynamic channel parameters recursively. The proposed method can adaptively learn the sparse structure and temporal correlation of multiuser channels without requiring the knowledge of hidden Markov channel parameters. Numerical simulations demonstrate that the proposed HHMP algorithm can accurately estimate angle-delay domain channels with reduced iteration times and pilot overhead.

Although the combination of the orthogonal time frequency space (OTFS) modulation and the massive multiple-input multiple-output (MIMO) technology can make communication systems perform better in high-mobility scenarios, there are still many challenges in downlink channel estimation owing to inaccurate modeling and high pilot overhead in practical systems. In this paper, we propose a channel state information (CSI) acquisition scheme for downlink massive MIMO-OTFS in presence of the fractional Doppler, including deterministic pilot design and channel estimation algorithm. First, we analyze the input-output relationship of the single-input single-output (SISO) OTFS based on the orthogonal frequency division multiplexing (OFDM) modem and extend it to massive MIMO-OTFS. Moreover, we formulate an accurate model for the practical system in which the fractional Doppler is considered and the influence of subpaths is revealed. A deterministic pilot design is then proposed based on the model and the structure of the pilot matrix to reduce pilot overhead and save memory consumption. Since channel geometry changes very slowly relative to the communication timescale, we put forward a modified sensing matrix based channel estimation (MSMCE) algorithm to acquire the downlink CSI. Simulation results demonstrate that the proposed downlink CSI acquisition scheme has significant advantages over traditional algorithms.

Variational message passing (VMP), belief propagation (BP) and expectation propagation (EP) have found their wide applications in complex statistical signal processing problems. In addition to viewing them as a class of algorithms operating on graphical models, this article unifies them under an optimization framework, namely, Bethe free energy minimization with differently and appropriately imposed constraints. This new perspective in terms of constraint manipulation can offer additional insights on the connection between different message passing algorithms and is valid for a generic statistical model. It also founds a theoretical framework to systematically derive message passing variants. Taking the sparse signal recovery (SSR) problem as an example, a low-complexity EP variant can be obtained by simple constraint reformulation, delivering better estimation performance with lower complexity than the standard EP algorithm. Furthermore, we can resort to the framework for the systematic derivation of hybrid message passing for complex inference tasks. Notably, a hybrid message passing algorithm is exemplarily derived for joint SSR and statistical model learning with near-optimal inference performance and scalable complexity.

In machine-type communication scenarios, sparse code multiple access (SCMA) is a promising non-orthogonal multiple access (NOMA) scheme owing to shaping gain by combining constellation modulation and spreading patterns together. In this paper, to fully exploit the channel knowledge contained in received data sequences, we propose the joint active user detection (AUD), channel estimation (CE), multi-user detection (MUD), and decoding receiver without knowing users' activity parameters in uplink grant-free massive multiple-input multiple-output (MIMO) SCMA systems. To avoid the permutation and scaling ambiguities of estimation results, the proposed receiver estimates channel based on both received short pilot and data sequences. We introduce auxiliary active state indicators in AUD to describe the sporadic transmission feature. The joint CE and MUD module is constructed as a bilinear inference problem with joint column-wise sparsity. Furthermore, we exploit the SCMA codewords sparsity feature and put Gaussian approximations on modulated symbols in joint CE and MUD module to reduce the receiver complexity. Simulation results show that the proposed unified receiver has substantial performance improvement and lower computational complexity than the conventional two-stage receiver and joint receiver in the literature.

To alleviate the blockage effects involved in millimeter-wave propagation, we investigate network massive multiple-input multiple-output (MIMO) transmission where only statistical channel state information is available at base stations (BSs). We first establish a network massive MIMO transmission model over millimeter-wave bands using per-beam synchronization. We Figure out that the beam domain is in favor of performing transmission in this scenario. We also demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence is further proposed to solve the problem. Numerical results reveal that the proposed network massive MIMO transmission approach can effectively alleviate the blockage effects and provide substantial performance gains over the existing transmission approaches.

This work focuses on the initial beam acquisition/alignment of millimeter wave (mmWave) communication systems. To detect the angle of arrival (AoA) and/or angle of departure (AoD), we propose a training protocol which probes all beamformers from a given codebook simultaneously by exploiting the sparse nature of mmWave channels. By applying a frequency-selective beam probing network, we can map each beamformer from the codebook to different frequencies and a spectral analysis at the receiver allows us to deduce favorable beamformers or AoDs. For practical reasons, we elaborate this idea of steering direction to frequency mapping for an orthogonal frequency division multiplexing (OFDM) communication system, i.e., we map each beamformer to specific pilot subcarriers. Under two different hardware designs, we investigate the feasibility of building such beamformer to frequency mappings for one additional radio frequency (RF) chain next to an existing OFDM communication system. We show that parallel beam training is able to achieve better effective transmission rates than exhaustive search in fast-time varying environments due to high temporal efficiency. This is crucial for mmWave communication systems which have access to large beamforming codebooks but suffer from short coherence times due to mobility and high spatial resolution.

Mobility and blockage are two critical challenges in wireless transmission over millimeter-wave (mmWave) and Terahertz (THz) bands. In this paper, we investigate network massive multiple-input multiple-output (MIMO) transmission for mmWave/THz downlink in the presence of mobility and blockage. Considering the mmWave/THz propagation characteristics, we first propose to apply per-beam synchronization for network massive MIMO to mitigate the channel Doppler and delay dispersion effects. Accordingly, we establish a transmission model. We then investigate network massive MIMO downlink transmission strategies with only the statistical channel state information (CSI) available at the base stations (BSs), formulating the strategy design as an optimization problem to maximize the network sum-rate. We show that the beam domain is favorable to perform transmission, and demonstrate that BSs can work individually when sending signals to user terminals. Based on these insights, the network massive MIMO precoding design is reduced to a network sum-rate maximization problem with respect to beam domain power allocation. By exploiting the sequential optimization method and random matrix theory, an iterative algorithm with guaranteed convergence performance is further proposed for beam domain power allocation. Numerical results reveal that the proposed network massive MIMO transmission approach with the statistical CSI can effectively alleviate the blockage effects and provide mobility enhancement over mmWave and THz bands.

In this paper, we show a novel algorithm for strong line-of-sight (LoS) multiple-input-multiple-output (MIMO) channel equalization. With optimally spaced antennas under specific arrangements, the LoS channels can be made spatially orthogonal. In practice, antenna displacements are expected. Conventional algorithms like zero-forcing (ZF) do not consider the special properties of the LoS MIMO channel and result in high complexity. We show that the LoS MIMO channel can be factorized into a product of three matrices. Thereby, the two diagonal matrices at the outer product positions are the most varying terms and should be compensated dynamically. Being a good tradeoff between complexity and robustness, the proposed sequential channel equalization is applied in a reverse order of the factorization. The algorithm can be applied to LoS MIMO systems with uniform linear or rectangular arrays, which are making use of digital or analog equalization. By considering the usage of the Winograd butterfly and Butler matrices, the number of multiplications in both digital and analog implementations of the proposed solution is increasing approximately linearly with respect to the number of antennas, while the complexity of the state-of-the-art designs grows quadratically. As found numerically and verified experimentally, the proposed method performs nearly as well as the ZF-based algorithms with near-optimal arrangements, while having significantly lower complexity.

With large bandwidths, millimeter wave (mmWave) line-of-sight (LoS) multi-input multi-output (MIMO) technology employing parallel stream transmission is well suited for future wireless backhaul systems. However, hardware imperfections of mmWave transceivers cause additional signal processing challenges. Considering the channel and hardware properties, this paper proposes a novel frame structure with high temporal efficiency. The required MIMO processing can be decomposed into two steps. The first one removes the interantenna interference including the carrier frequency offsets. In the second step, the intersymbol interference brought by the frequency-selective components is canceled with a parallel structure. As a comparison, a more general decision-directed least-mean-square approach (DD-LMS) for joint processing of all effects is introduced. With a 60 GHz 2 × 2 LoS MIMO demonstrator, only the widely linear version of DD-LMS achieves performance of about 0.2-0.6 dB better than the proposed method but requires approximately four times more complexity. The proposed method with parallel processing chains is well suited especially for larger LoS MIMO systems, e.g., for a system with 16 streams, its required complexity is less than 7% of the widely linear DD-LMS.

Spatial multiplexing is an important factor usable for improving the throughput of future millimeter-wave (mm-wave) backhaul links. One conventional strategy in mm-wave multiple-input multiple-output (MIMO) systems uses densely packed antennas and exploits the spatial signature of multiple paths. Meanwhile, spatial multiplexing over a single line-of-sight (LoS) path, known as LoS MIMO communication, offers an alternative option with widely spaced antennas exploiting the phases of spherical waves. In this paper, we first show that those two conventional approaches exploit two different degrees in the channel matrices, which we denote as inter- and intra-path multiplexing, respectively. Then, we show that the two kinds of spatial multiplexing can be jointly exploited and identify their different requirements on system design. Fulfilling all requirements simultaneously, we propose a system with multiple widely spaced subarrays. With the help of analog beamforming, the intra-path multiplexing of conventional LoS MIMO systems can be introduced to other non-LoS paths owing to its robustness. Merging the two kinds of spatial multiplexing in a combined channel model and connecting it with a hybrid beamforming architecture, the proposed system achieves spatial multiplexing of a higher order than conventional ones. Simulation results for a backhaul scenario illustrate that the channel of the proposed method has higher ranks than that of conventional approaches.

Turbo equalization has demonstrated to be a powerful approach for wireless transmission over frequency-selective channels introducing intersymbol interferences (ISI). Regarding multiple-input multiple-output (MIMO) systems, tree-search based MIMO detection techniques (e.g., sphere detection) are well suited for significantly reducing multi-antenna interferences. However, direct application of these detection techniques under the presence of ISI leads to vast processing complexity, increasing exponentially with the channel length and the number of transmitting antennas. In this paper, a low-complex two-stage approach is described splitting the interference reduction into a frequency-domain equalization stage and a time-domain sphere detection stage. Both are able to take a-priori information into account. Based on this, we derive a novel multi-iterative receiver, enabling powerful and adaptable processing with respect to the dominating interferences.

In this work we show that the analog equalizing networks are suitable for low resolution quantization with limited mutual information loss in strong line-of-sight MIMO communication. More specifically, a simplified analog equalizing network design in comparison with state-of-the-art works is proposed in this work. Additionally, the new network design works equally good for larger displacement ranges. Furthermore, by using analog equalizing networks in line-of-sight MIMO systems, it is shown that the drawbacks of low resolution quantization can be minimized. This increases the energy efficiency of the analog-to-digital converters via minimizing the required magnitude resolution. Generally, low resolution quantization causes low entropy on the receive vectors which will effectively reduce the mutual information of the desired transmission. The analog equalizing network reshapes the distribution and reduces the dynamics of the received signals in the complex constellation plane before quantization. Therefore, the entropy loss after low resolution quantization is reduced and the system performs essentially as good as a system with higher resolutions. Finally, an algorithm is proposed to remove ambiguities which arise after analog-to-digital conversion with a given low resolution.

In this work, we show a new design of analog equalizing network for N-stream strong Line-of-Sight MIMO communication, aiming at improved robustness. The design includes a core fixed equalizing network that equalizes ideal spatially orthogonal channels. Existing works show that the fixed equalizing network can equalize a spatially orthogonal MIMO system with parallel arrays. However, it is observed that such a fixed equalizing network is very sensitive to displacement errors. To make the system robust, state-of-the-art approaches use N2 fully controlled analog elements to perfectly equalize the channel via aligning the structured interferences. In this work, the terms causing the sensitiveness of fixed analog equalizing networks are identified. By compensating the tackled sensitive terms, the proposed design uses only 2N fully controlled analog elements and the robustness of the system is improved significantly. Meanwhile, by exploring the channel property, this work shows that if the spatially orthogonality is achieved by uniform rectangular arrays, the equalization can be applied with a new two-stage scheme. The scheme can be applied to spatially orthogonal MIMO systems with digital and/or analog equalization. Meanwhile, the computational complexity, required components number, as well as the complexity of the hardware design are significantly reduced.

In this work, a two-level hierarchical MIMO system is proposed to combine the spatial multiplexing gain and beamforming gain in a strong LOS channel. The superior is a MIMO system that consists of specially arranged sub-arrays to fully exploit the spatial multiplexing gain in deterministic channels. Additionally, a deterministic spherical-wave channel model is introduced. This channel model includes the radiation patterns of the sub-arrays, orthogonal phase relations introduced by the specific sub-array arrangement and the path loss considering deployment in practical scenarios. The attenuation includes the free space path loss, the oxygen absorption, the rain attenuation in bad weather and the front-end loss. The regulations for the maximum radiated power and the available bandwidth at 60 GHz were also investigated. Furthermore, the maximum transmission rate and upper bound of the energy efficiency are modeled and calculated for the proposed system operating at 60 GHz compliant to those regulations, as well for a constraint of the maximum available transmit power on-board. The result shows that the proposed system architecture is promising to achieve over 100 Gbps for macro-cell backhaul links with reasonable antenna sizes and high energy efficiency.

A Multi-View Video (MVV) is a set of related videos that capture an interesting scene from different perspectives at overlapping times. The work at hand is concerned with the design of innovative user interfaces (UI) for viewing MVVs. As a first contribution four different MVV UIs are designed. While different in design, their common aim is to allow a pleasant viewing and perspective switching experience by reducing the cognitive effort associated with constructing a mental map of the scene. This is achieved by incorporating the spatial relationships of the available views in the UI elements. As a second contribution a quality model is developed and a methodical evaluation process is designed. This is used to evaluate and compare the UIs. In a third contribution we use principal component analysis (PCA) to reveal information about the perceptual quality space which helps validating our proposed quality model. Based on the findings, a series of conclusions for best design practices are provided.

A broadcast system with ACK/NAK feedback is considered where each link is modeled as a Rayleigh fading channel. The performance improvement is investigated if the erroneously received packets are not discarded and the soft information within them is exploited to reduce the number of transmission attempts. An outer bound on the packet reception rate region on the physical layer is provided by extending that region on the packet layer. A new transmission algorithm is proposed that combines log-likelihood ratios on the physical layer with network coding. This transmission algorithm does not require the transmitter to know any prior or instantaneous information about the channel and considers network coding on both the packet layer and the physical layer. Simulations show that the transmission algorithm with soft decoding achieves rates close to an outer bound for the packet reception rate region.