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Massive multiple-input-multiple-output (M-MIMO) features a capability for spatial multiplexing of large number of users. This number becomes even more extreme in extra-large (XL-MIMO), a variant of M-MIMO where the antenna array is of very large size. Yet, the problem of signal processing complexity in M-MIMO is further exacerbated by the XL size of the array. The basic processing problem boils down to a sparse system of linear equations that can be addressed by the randomized Kaczmarz (RK) algorithm. This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, it is limited by the fact that certain configurations of the linear equations may significantly deteriorate the performance of the RK algorithm. In this article, we embrace the interest in accelerated RK algorithms and introduce three new RK-based low-complexity receiver designs. In our experiments, our methods are not only able to overcome the previous scheme, but they are more robust against inter-user interference (IUI) and sparse channel matrices arising in the XL-MIMO regime. In addition, we show that the RK-based schemes use a mechanism similar to that used by successive interference cancellation (SIC) receivers to approximate the regularized zero-forcing (RZF) scheme. [ABSTRACT FROM AUTHOR]

In this article, we propose a three-dimensional (3D) multi-input multi-output (MIMO) channel model for air-to-ground (A2G) communications in unmanned aerial vehicles (UAV) environments, where the UAV transmitter and ground receiver are in motion in the air and on the ground, respectively. A novel angular estimation algorithm is proposed to estimate the real-time azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA) based on the non-stationary nature of the channel model. In the model, we investigate the time-varying spatial cross-correlation functions (CCFs) and temporal auto-correlation functions (ACFs) with respect to the different moving directions and velocities of the UAV transmitter and ground receiver. Furthermore, we derive and study the Doppler power spectral densities (PSDs) and power delay profiles (PDPs) of the proposed channel model. Numerical results show that characteristics of the proposed channel model are very close to those of practical measurements, which provide a new and practical approach to evaluate the performance of next generation UAV-MIMO communication systems. [ABSTRACT FROM AUTHOR]

A frequency-domain system identification-based multiple-input multiple-output (FDSI-MIMO)-SAR algorithm using multiple phase center multiple azimuth beams is proposed to obtain high-resolution wide-swath (HRWS) imaging. Frequency-division multiple access is used in such a way that each transmitter emits a conventional linear frequency-modulated (LFM) waveform modulated by a different carrier frequency and the obtained range resolution corresponds to the total transmitted bandwidth. In this article, an MIMO-SAR problem is modeled, using the principle of displaced phase center, as multiple but separate multiple-input single-output (MISO) system identification problems. The channel impulse responses of the individual MISO problems are identified in the range dimension using an FDSI-based estimation algorithm in such a way that the estimated range profile is free of interrange cell interference. In addition, the proposed algorithm does not require separating the subband waveforms at the receiver as they are processed jointly without a need to add guard bands between the adjacent subbands, which would allow utilizing the available bandwidth to the maximum efficiency. The method of synthesizing a wide transmit antenna beam from a narrow antenna beam applied in single-input multiple-output (SIMO) SAR is extended to MIMO SAR to remove the sidelobes effects of the receive beams and hence leading to the desired signal-to-noise ratio. A pulse repetition frequency lower than the Doppler bandwidth is used to obtain wide swath without experiencing aliasing in the Doppler spectrum. Finally, both simulated and constructed raw data are used to validate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

We investigate power optimization for throughput maximization in massive multiple-input multiple-output (MIMO) downlink networks with hybrid energy harvesting transmitter. In particular, a finite-capacity energy harvester is employed at the base station in conjunction with the fixed power grid for system energy supply. With availability of the noncausal knowledge about the channel state information (CSI) and the energy harvesting process, the throughput maximization is first formulated as a typical convex optimization problem, subject to a set of battery storage and power constraints. Especifically, this optimization is also featured by taking into account the circuit energy consumption that can not be neglected in massive MIMO systems with a large quality of radio frequency chains. Mathematically, Lagrange dual decomposition technique can be used to derive the optimal power variables. However, due to its offline calculation characteristics, the proposed approach is hard to implement in practice and can only serve as a performance upper bound. To facilitate real world application, we then heuristically present an online scheme that merely utilizes the causal information of the CSI and the statistics of the energy harvesting process, without significantly degrading the performance. Finally, simulation results are shown to verify the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]