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1. Stabilizing Terahertz MIMO Channel Capacity with Controlled Diffuse Reflections

   https://doi.org/10.1109/ICC45041.2023.10279474  

   Singh, Suresh; Tran, Ha; Le, Thanh (May 2023, IEEE International Conference on Communications)

   Communication at the terahertz band is increasingly seen as vital for future short-range very high datarate channels. However, these channels suffer from significant environmental impairments and, as a result, providing coverage in indoor settings requires the use of directional line of sight (LoS) paths to visible users and reflected paths, using smooth metal reflectors, for users in the shadow of an obstruction. Previous work has shown that these types of reflected paths display similar characteristics to LoS paths and we call them R-LoS (reflected LoS). MIMO for LoS and R-LoS channels is feasible at terahertz frequencies and delivers very high capacity at some distances. Unfortunately, channel capacity varies greatly with small changes in distance (the channel matrix fluctuates between full rank and rank 1) which is undesirable for communication systems. In this paper, we utilize diffusive reflectors to create multiple reflections such that the MIMO channel capacity for R-LoS is better behaved. We conduct experiments at 410 GHz for reflections from different artificially created diffuse surfaces. We use measurements to estimate channel capacity for 2×2 MIMO when the only path is the diffuse reflected one. We show that by creating multiple controlled reflections, it is possible to achieve relatively stable capacity up to 13 - 16 bits/sec/Hz at varying distances. We also analyze the phase of the received signals and the beam profile in detail. Overall, our results indicate that by utilizing artificially created reflections, we can maintain a stable MIMO channel at high capacity. 

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2. Spatially Correlated MIMO Broadcast Channel with Partially Overlapping Correlation Eigenspaces

   https://doi.org/10.1109/ISIT.2018.8437592  

   Zhang, Fan; Nosratinia, Aria (June 2018, IEEE International Symposium on Information Theory (ISIT))

   The spatially correlated MIMO broadcast channel has grown in importance due to emerging interest in massive MIMO and mm-wave communication, but much about this channel remains unknown. In this paper, we study a two-user MIMO broadcast channel where the spatial correlation matrices corresponding to the two receivers have eigenspaces that are neither identical nor disjoint, but are partially overlapped. Spatially correlated channels occur in e.g. massive MIMO and furthermore different links may credibly have correlation eigenspaces that are neither disjoint nor equal, therefore this problem is practically motivated. This paper develops a new approach for this scenario and calculates the corresponding degrees of freedom. Our technique involves a careful decomposition of the signaling space to allow a combination of pre-beamforming along directions that depend on the relative positioning of the non-overlapping and overlapping components of the eigenspaces, along with the product superposition technique. The ideas are demonstrated with a toy example, are developed in two conditions of varying complexity, and are illuminated by numerical results. 

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3. Bayesian Iterative Channel Estimation and Turbo Equalization for Multiple-Input–Multiple-Output Underwater Acoustic Communications

   https://doi.org/10.1109/JOE.2019.2956299  

   Qin, Xiangzhao; Qu, Fengzhong; Zheng, Yahong Rosa (July 2020, IEEE Journal of Oceanic Engineering)

   This article investigates a robust receiver scheme for a single carrier, multiple-input–multiple-output (MIMO) underwater acoustic (UWA) communications, which uses the sparse Bayesian learning algorithm for iterative channel estimation embedded in Turbo equalization (TEQ). We derive a block-wise sparse Bayesian learning framework modeling the spatial correlation of the MIMO UWA channels, where a more robust expectation–maximization algorithm is proposed for updating the joint estimates of channel impulse response, residual noise, and channel covariance matrix. By exploiting the spatially correlated sparsity of MIMO UWA channels and the second-order a priori channel statistics from the training sequence, the proposed Bayesian channel estimator enjoys not only relatively low complexity but also more stable control of the hyperparameters that determine the channel sparsity and recovery accuracy. Moreover, this article proposes a low complexity space-time soft decision feedback equalizer (ST-SDFE) with successive soft interference cancellation. Evaluated by the undersea 2008 Surface Processes and Acoustic Communications Experiment, the improved sparse Bayesian learning channel estimation algorithm outperforms the conventional Bayesian algorithms in terms of the robustness and complexity, while enjoying better estimation accuracy than the orthogonal matching pursuit and the improved proportionate normalized least mean squares algorithms. We have also verified that the proposed ST-SDFE TEQ significantly outperforms the low-complexity minimum mean square error TEQ in terms of the bit error rate and error propagation. 

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4. Spectral Clustering Aided User Grouping and Scheduling in Wideband MU-MIMO Systems

   Hsu, Chih-Ho; Feres, Carlos; Ding, Zhi (May 2023, IEEE International Conference on Communications)

   Multiuser MIMO (MU-MIMO) technologies can help provide rapidly growing needs for high data rates in modern wireless networks. Co-channel interference (CCI) among users in the same resource-sharing group (RSG) presents a serious user scheduling challenge to achieve high overall MU-MIMO capacity. Since CCI is closely related to correlation among spatial user channels, it would be natural to schedule co-channel user groups with low inter-user channel correlation. Yet, establishing RSGs with low co-channel correlations for large user populations is an NP-hard problem. More practically, user scheduling for wideband channels exhibiting distinct channel characteristics in each frequency band remains an open question. In this work, we proposed a novel wideband user grouping and scheduling algorithm named SC-MS. The proposed SC-MS algorithm first leverages spectral clustering to obtain a preliminary set of user groups. Next, we apply a post-processing step to identify user cliques from the preliminary groups to further mitigate CCI. Our last step groups users into RSGs for scheduling such that the sum of user clique sizes across the multiple frequency bands is maximized. Simulation results demonstrate network performance gain over benchmark methods in terms of sum rate and fairness. 

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5. Sparsity-Based Channel Estimation Exploiting Deep Unrolling for Downlink Massive MIMO

   Chen, An; Xu, Wenbo; Lu, Liyang; Wang, Yue (December 2023, 2023 IEEE Global Communications Conference)

   Massive multiple-input multiple-output (MIMO) enjoys great advantage in 5G wireless communication systems owing to its spectrum and energy efficiency. However, hundreds of antennas require large volumes of pilot overhead to guarantee reliable channel estimation in FDD massive MIMO system. Compressive sensing (CS) has been applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel but suffer from high complexity. To overcome this challenge, this paper develops a hybrid channel estimation scheme by integrating the model-driven CS and data-driven deep unrolling technique. The proposed scheme consists of a coarse estimation part and a fine correction part to respectively exploit the inter- and intraframe sparsities of channels to greatly reduce the pilot overhead. Theoretical result is provided to indicate the convergence of the fine correction and coarse estimation net. Simulation results are provided to verify that our scheme can estimate MIMO channels with low pilot overhead while guaranteeing estimation accuracy with relatively low complexity. 

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