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1. Massive MIMO Cognitive Cooperative Relaying

   https://doi.org/10.1007/978-3-030-23597-0_8  

   Dinh, S.; Liu, H.; Ouyang, F. (June 2019, Springer LNCS Wireless Algorithms, Systems, and Applications)

   This paper proposes a novel cognitive cooperative transmission scheme by exploiting massive multiple-input multiple-output (MMIMO) and non-orthogonal multiple access (NOMA) radio technologies, which enables a macrocell network and multiple cognitive small cells to cooperate in dynamic spectrum sharing. The macrocell network is assumed to own the spectrum band and be the primary network (PN), and the small cells act as the secondary networks (SNs). The secondary access points (SAPs) of the small cells can cooperatively relay the traffic for the primary users (PUs) in the macrocell network, while concurrently accessing the PUs’ spectrum to transmit their own data opportunistically through MMIMO and NOMA. Such cooperation creates a “win-win” situation: the throughput of PUs will be significantly increased with the help of SAP relays, and the SAPs are able to use the PU’s spectrum to serve their secondary users (SUs). The interplay of these advanced radio techniques is analyzed in a systematic manner, and a framework is proposed for the joint optimization of cooperative relay selection, NOMA and MMIMO transmit power allocation, and transmission scheduling. Further, to model network-wide cooperation and competition, a two-sided matching algorithm is designed to find the stable partnership between multiple SAPs and PUs. The evaluation results demonstrate that the proposed scheme achieves significant performance gains for both primary and secondary users, compared to the baselines. 

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2. A Novel Transceiver Design in Wideband Massive MIMO for Beam Squint Minimization

   https://doi.org/10.1109/TCOMM.2024.3372885  

   Afeef, Liza; Kihero, Abuu B; Arslan, Hüseyin (July 2024, IEEE Transactions on Communications)

   When using ultra-wideband signaling on massive multiple-input multiple-output (mMIMO) systems, the electromagnetic wave incurs an extra delay (across the array elements) comparable to or larger than the symbol duration, which translates into a shift in beam direction known as the beam squint effect. The beam squinting problem degrades the array gain and reduces the system capacity. This paper proposes a novel transceiver design based on lens antenna subarray and analog subband filters to compensate for the beam squinting effect. Specifically, the proposed design chunks the wideband signal from the phase shifters into groups of narrowband signals and controls their squints through an exhaustive search-based switching/precoding mechanism under the lenses. Furthermore, a simplified, thresholded search-based precoding algorithm is proposed, which demonstrates good performance while significantly minimizing complexity. The proposed system is analyzed in terms of beam gain, complexity, power consumption, and capacity. The numerical results demonstrate significant performance enhancement for the proposed system design as compared to the conventional mMIMO system with an uncompensated beam squinting problem. 

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3. Using a Drone Sounder to Measure Channels for Cell-Free Massive MIMO Systems

   https://doi.org/10.1109/WCNC51071.2022.9771649  

   Choi, Thomas; Gomez-Ponce, Jorge; Bullard, Colton; Kanno, Issei; Ito, Masaaki; Ohseki, Takeo; Yamazaki, Kosuke; Molisch, Andreas F. (May 2022, IEEE WCNC)

   Measurements of the propagation channels in realworld environments form the basis of all realistic system performance evaluations, as foundation of statistical channel models or to verify ray tracing. This is also true for the analysis of cell-free massive multi-input multi-output (CF-mMIMO) systems. However, such experimental data are difficult to obtain, due to the complexity and expense of deploying tens or hundreds of channel sounder nodes across the wide area a CF-mMIMO system is expected to cover, especially when different configurations and number of antennas are to be explored. In this paper, we provide a novel method to obtain channel data for CF-mMIMO systems using a channel sounder based on a drone, also known as a small unmanned aerial vehicle (UAV). Such a method is efficient, flexible, simple, and low-cost, capturing channel data from thousands of different access point (AP) locations within minutes. In addition, we provide sample 3.5 GHz measurement results analyzing deployment strategies for APs and make the data open source, so they may be used for various other studies. To our knowledge, our data are the first large-scale, real-world CF-mMIMO channel data. 

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4. Uplink Energy Efficiency of Cell-Free Massive MIMO With Transmit Power Control in Measured Propagation Channels

   https://doi.org/10.1109/SiPS52927.2021.00037  

   Choi, Thomas; Ito, Masaaki; Kanno, Issei; Oseki, Takeo; Yamazaki, Kosuke; Molisch, Andreas F. (October 2021, 021 IEEE Workshop on Signal Processing Systems (SiPS))

   Abstract—Cell-free massive MIMO (CF-mMIMO) is expected to provide reliable wireless services for a large number of user equipments (UEs) using access points (APs) distributed across a wide area. When the UEs are battery-powered, uplink energy efficiency (EE) becomes an important performance metric for CF-mMIMO systems. Therefore, if the “target” spectral efficiency (SE) is met, it is important to optimize the uplink EE when setting the transmit powers of the UEs. Also, such transmit power control (TPC) method must be tested on channel data from real-world measurements to prove its effectiveness. In this paper, we compare three different TPC algorithms using zero-forcing reception by applying them to 3.5 GHz channel measurement data featuring 30,000 possible AP locations and 8 UE locations in a 200m×200m area. We show that the max-min EE algorithm is highly effective in improving the uplink EE at a target SE, especially if the number of single-antenna APs is large, circuit power consumption is low, and the maximum allowed transmit power of the UEs is high. 

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5. Deep Unfolding Hybrid Beamforming Designs for THz Massive MIMO Systems

   https://doi.org/10.1109/TSP.2023.3322852  

   Nguyen, Nhan Thanh; Ma, Mengyuan; Lavi, Ortal; Shlezinger, Nir; Eldar, Yonina C; Swindlehurst, A Lee; Juntti, Markku (January 2023, IEEE Transactions on Signal Processing)

   Hybrid beamforming (HBF) is a key enabler for wideband terahertz (THz) massive multiple-input multiple-output (mMIMO) communications systems. A core challenge with designing HBF systems stems from the fact their application often involves a non-convex, highly complex optimization of large dimensions. In this article, we propose HBF schemes that leverage data to enable efficient designs for both the fully-connected HBF (FC-HBF) and dynamic sub-connected HBF (SC-HBF) architectures. We develop a deep unfolding framework based on factorizing the optimal fully digital beamformer into analog and digital terms and formulating two corresponding equivalent least squares (LS) problems. Then, the digital beamformer is obtained via a closed-form LS solution, while the analog beamformer is obtained via ManNet, a lightweight sparsely-connected deep neural network based on unfolding projected gradient descent. Incorporating ManNet into the developed deep unfolding framework leads to the ManNet-based FC-HBF scheme. We show that the proposed ManNet can also be applied to SC-HBF designs after determining the connections between the radio frequency chain and antennas. We further develop a simplified version of ManNet, referred to as subManNet, that directly produces the sparse analog precoder for SC-HBF architectures. Both networks are trained with an unsupervised procedure. Numerical results verify that the proposed ManNet/subManNet-based HBF approaches outperform the conventional model-based and deep unfolded counterparts with very low complexity and a fast run time. For example, in a simulation with 128 transmit antennas, ManNet attains a slightly higher spectral efficiency than the Riemannian manifold scheme, but over 600 times faster and with a complexity reduction of more than by a factor of six (6). 

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