No abstract

Millimeter-wave (mmWave) and multiple-input multiple-output (MIMO) combination technologies have attracted extensive attention from both academia and industry for meeting future communication challenges and requirements. As a viable option to deal with the trade-off between hardware complexity and system performance, hybrid analog/digital architectures are regarded as efficient mmWave MIMO transceivers. While acquiring channel state information (CSI) is a challenging task to design the optimal beamformers/combiners, especially in mmWave communications due to a lot of challenges. Fortunately, the sparse nature of the channel allows to leverage the compressed sensing (CS) tools and theories. However, the critical challenge to develop a CS-based formulation for estimating the mmWave channel is the codebook design (sensing matrices) and its pilot symbol numbers. In this paper, we proposed a multistage CS-based algorithm to estimate the channel explicitly using pilot and data symbols which enable increasing the number of measurements to enhance the estimation accuracy and maximize the spatial diversity by reducing the overlapping between training beams. Simulations confirmed that our proposed method has the best results compared to the existing methods based on codebook schemes.

Since millimeter wave (mmWave) communications have wideband channels, mmWave signal corruptions increase due to radio-channel frequency selectivity. In this case, the combination of the orthogonal frequency division multiplexing (OFDM) with the mmWave MIMO system is envisioned as a candidate technique to address the degradation of communication. As hybrid analog/digital architecture offers potential energy and spectral efficiency for the mmWave MIMO device. Matrix factorization formulation with singular value decomposition (SVD) is the most used method for designing the hybrid precoder/combiner. However, using SVD decomposition in precoding/combining designing requires power allocation schemes due to the different signal-to-noise ratios (SNRs) of different subchannels. To achieve a high wireless communications capacity, we propose in this work an alternating minimization algorithm based on a manifold optimization technique using the geometric mean decomposition (GMD) (called MO-AltMin-GMD) to derive unconstrained optimal precoders and combiners from the channel state information (CSI). The main advantage is that the proposed hybrid design avoids any allocation schemes in order to reduce the hybrid architecture complexity. Numerical simulations show that the proposed hybrid design provides high results compared to the existing methods in terms of spectral efficiency.

Millimeter-wave bands (mmWave) are considered as a strong candidate for achieving high-quality communication links for the future outdoor cellular systems to overcome the spectrum congestion problem. Due to the extremely high path loss in mmWave band, large antenna arrays at both the transmitter and receiver are necessary. Hybrid beamforming architectures are used to exploit the potential array gain with several RF chains, which poses a problem of complexity when estimating the mmWave channel. To address the challenge of this hardware complexity, we propose in this paper an approach to design a multiresolution hierarchical codebook to meet parallel multistream data based on the physical design via hybrid analog/digital architecture with low complexity, i.e., 2-bit phase state and few numbers of RF chains. The simulation results verify that our proposed method to design the codebook has better design performance of beams and can achieve higher average spectral efficiency gains of channel estimation compared to the one based on high hardware complexity.hybrid beamforming, millimeter wave communications, MIMO, multiresolution hierarchical codebook design, orthogonal matching pursuit, parallel multistream data | INTRODUCTIONDue to the intensive evolution of the internet and the huge interconnected networks, fifth-generation and beyond (5G) is the next generation of cellular networks that will be enabling the collection and exchange of information and data globally between people, communities, and more importantly, businesses, 1 thanks to leading applications, such as internetof-things (IoT), 2 ultra-high-definition (UHD) 3D video streaming, cloud-based services, and augmented reality. 3 However, to enhance the communication of these applications, 5G cellular networks must address six challenges that are not effectively addressed by fourth-generation (4G), i.e., higher capacity, higher data rate, lower end to end latency, massive device connectivity, reduced cost, and consistent quality of experience provisioning. 4 To accommodate these challenges, millimeter-wave (mmWave) communication is a promising technology for 5G-and-beyond mobile cellular networks, as well as for emerging Gbps-speed Wi-Fi networks based on the IEEE 802.11ad and draft IEEE 802.11ay standards. 5 On the other hand, one of the challenges of using mmWave frequencies to provide outdoor coverage in a cellular system is the increased path loss encountered at these frequencies. 6 For this reason, to overcome the increased path losses, narrow-beam communications powered by multiinput multioutput (MIMO) systems are typically required. In the mmWave MIMO system, to solve the dilemma of hardware complexity and system performance, hybrid analog/digital