Distributed phased arrays based multiple-input multiple-output (DPA-MIMO) is a newly debuted architecture that enables both spatial multiplexing and beamforming while facilitating highly reconfigurable hardware implementation in millimeter-wave (mmWave) frequency bands. With a DPA-MIMO system, we focus on channel state information (CSI) acquisition and hybrid precoding. As benefited from a coordinated and open-loop pilot beam pattern design, all the subarrays can simultaneously perform channel sounding with less training overhead compared to the time-sharing operation of each subarray. Furthermore, two sparse channel recovery algorithms, known as joint orthogonal matching pursuit (JOMP) and joint sparse Bayesian learning with @math reweighting (JSBL- @math ), are proposed to exploit the hidden structured sparsity in the beam-domain channel vector. Finally, successive interference cancellation (SIC) based hybrid precoding through subarray grouping is illustrated for the DPA-MIMO system, which decomposes the joint subarray RF beamformer design into an interactive per-subarray-group handle. Simulation results show that the proposed two channel estimators fully take advantage of the partial coupling characteristic of DPA-MIMO channels to perform channel recovery, and the proposed hybrid precoding algorithm is suitable for such array-of-subarrays architecture with satisfactory performance and low complexity.
In mmWave wireless systems, channel state information (CSI) acquisition is unconditionally required prior to beamforming to enable sufficient link margin. Most of the previous research works in this area focus on the fully-connected structure. For instance, a multi-stage channel sounding approach for this structure was developed by exploiting the sparse scattering nature of the mmWave channel @cite_2 . From the compressed sensing (CS) perspective, this feedback based divide-and-conquer searching process which uses a hierarchical multi-resolution codebook leads to an adaptive equivalent measurement matrix with fewer measurements. Compared to the closed-loop beam training methods in @cite_3 @cite_2 @cite_29 , a CS-based open-loop channel estimator can decrease feedback overhead by using deterministic pilot beam patterns which are designed through minimizing the total coherence of the equivalent measurement matrix @cite_31 . Following the design criteria of multi-resolution codebooks in @cite_2 , subarray based coordinated beam training with time-delay phase shifters is proposed for sub-Terahertz (sub-THz) communication systems with an array-of-subarrays architecture @cite_46 . Due to the high complexity of jointly optimizing the RF beam directions across multiple subarrays, two complementary approaches are developed to obtain a small set of dominant candidate directions in @cite_56 .
@cite_31 @cite_29 @cite_3 @cite_56 @cite_2 @cite_46
2730027154 2964056649 2015301486 2116193517
Millimeter-wave (mmWave) and sub-Terahertz (THz) communications are compelling as an enabler for next-generation wireless networks. In this paper, we study mmWave and sub-THz systems with array-of-subarray architecture. To accommodate the ultrabroad bandwidth in the mmWave and sub-THz bands, time-delay phase shifters are introduced in system design. Our goal is to investigate beamforming training with hybrid processing to extract the dominant channel information, which would fully exploit channel characteristics while respecting the nature of circuit hardware. In particular, codebooks based on time-delay phase shifters are defined and structured. Then, two multi-resolution time-delay codebooks are designed through subarray coordination. One is built on adaptation of physical beam directions, and the other relies on dynamic approximation of beam patterns. Also, a low-complexity system implementation with modifications on the time-delay codebooks is studied. Furthermore, based on the proposed codebooks, a hierarchical beamforming training strategy with reduced overhead is developed to enable simultaneous training for multiple users. Simulation results show that the proposed multi-resolution time-delay codebooks could provide sufficient beam gains and are robust over large bandwidth. Also, the effectiveness of the hierarchical beamforming training is verified. We study RF-enabled wireless energy transfer (WET) via energy beamforming, from a multi-antenna energy transmitter (ET) to multiple energy receivers (ERs) in a backscatter communication system such as RFID. The acquisition of the forward-channel (i.e., ET-to-ER) state information (F-CSI) at the ET (or RFID reader) is challenging, since the ERs (or RFID tags) are typically too energy-and-hardware-constrained to estimate or feedback the F-CSI. The ET leverages its observed backscatter signals to estimate the backscatter-channel (i.e., ET-to-ER-to-ET) state information (BS-CSI) directly. We first analyze the harvested energy obtained using the estimated BS-CSI. Furthermore, we optimize the resource allocation to maximize the total utility of harvested energy. For WET to single ER, we obtain the optimal channel-training energy in a semiclosed form. For WET to multiple ERs, we optimize the channel-training energy and the energy allocation weights for different energy beams. For the straightforward weighted-sum-energy (WSE) maximization, the optimal WET scheme is shown to use only one energy beam, which leads to unfairness among ERs and motivates us to consider the complicated proportional-fair-energy (PFE) maximization. For PFE maximization, we show that it is a biconvex problem, and propose a block-coordinate-descent-based algorithm to find the close-to-optimal solution. Numerical results show that with the optimized solutions, the harvested energy suffers slight reduction of less than 10 , compared to that obtained using the perfect F-CSI. We propose joint spatial division and multiplexing (JSDM), an approach to multiuser MIMO downlink that exploits the structure of the correlation of the channel vectors in order to allow for a large number of antennas at the base station while requiring reduced-dimensional channel state information at the transmitter (CSIT). JSDM achieves significant savings both in the downlink training and in the CSIT uplink feedback, thus making the use of large antenna arrays at the base station potentially suitable also for frequency division duplexing (FDD) systems, for which uplink downlink channel reciprocity cannot be exploited. In the proposed scheme, the multiuser MIMO downlink precoder is obtained by concatenating a prebeamforming matrix, which depends only on the channel second-order statistics, with a classical multiuser precoder, based on the instantaneous knowledge of the resulting reduced dimensional “effective” channel matrix. We prove a simple condition under which JSDM incurs no loss of optimality with respect to the full CSIT case. For linear uniformly spaced arrays, we show that such condition is approached in the large number of antennas limit. For this case, we use Szego's asymptotic theory of Toeplitz matrices to show that a DFT-based prebeamforming matrix is near-optimal, requiring only coarse information about the users angles of arrival and angular spread. Finally, we extend these ideas to the case of a 2-D base station antenna array, with 3-D beamforming, including multiple beams in the elevation angle direction. We provide guidelines for the prebeamforming optimization and calculate the system spectral efficiency under proportional fairness and max-min fairness criteria, showing extremely attractive performance. Our numerical results are obtained via asymptotic random matrix theory, avoiding lengthy Monte Carlo simulations and providing accurate results for realistic (finite) number of antennas and users. The use of the millimeter (mm) wave spectrum for next generation (5G) mobile communication has gained significant attention recently. The small carrier wavelengths at mmwave frequencies enable synthesis of compact antenna arrays, providing beamforming gains that compensate the increased propagation losses. In this work, we investigate the feasibility of employing multiple antenna arrays (at the transmitter and or receiver) to obtain diversity multiplexing gains in mmwave systems, where each of the arrays is capable of beamforming independently. Considering a codebook-based beamforming system (the set of possible beamforming directions is fixed a priori, e.g., to facilitate limited feedback), we observe that the complexity of jointly optimizing the beamforming directions across the multiple arrays is highly prohibitive, even for very reasonable system parameters. To overcome this bottleneck, we develop reduced complexity algorithms for optimizing the choice of beamforming directions, premised on the sparse multipath structure of the mmwave channel. Specifically, we reduce the cardinality of the joint beamforming search space, by restricting attention to a small set of dominant candidate directions. To obtain the set of dominant directions, we develop two complementary approaches: 1) based on computation of a novel spatial power metric; a detailed analysis of this metric shows that, in the limit of large antenna arrays, the selected candidate directions approach the channel's dominant angles of arrival and departure, and 2) precise estimation of the channel's (long-term) dominant angles of arrival, exploiting the correlations of the signals received across the different receiver subarrays. Our methods enable a drastic reduction of the optimization search space (a factor of 100 reduction), while delivering close to optimal performance, thereby indicating the potential feasibility of achieving diversity and multiplexing gains in mmwave systems.