Distributed Massive MIMO

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs), where spatially distributed sensors transmit binary decisions over a shared flat-fading channel. To enhance fusion efficiency, a reconfigurable metasurface is positioned in the near-field of a few receive antennas, enabling a holographic architecture that harnesses large-aperture gains with minimal RF hardware. A generalized likelihood ratio test is derived for fixed metasurface settings, and two lowcomplexity joint design strategies are proposed to optimize both fusion and metasurface configuration. These suboptimal schemes achieve a balance between performance, complexity, and system knowledge. The goal is to ensure reliable detection of a localized phenomenon at the fusion center, under energy-efficient constraints aligned with IoT requirements. Simulation results validate the effectiveness of the proposed holographic fusion, even under simplified designs.

Cell-free Massive Multiple-Input Multiple-Output (mMIMO) networks are a promising solution for the Sixth Generation of mobile systems (6G) and beyond. These networks utilize multiple distributed antennas to transmit and receive signals coherently, under an apparently non-cellular communication paradigm that eliminates the traditional concept of cells in mobile networks. This shift poses significant deployment challenges, as conventional tools designed for cellular systems are in adequate for planning and evaluating cell-free mMIMO architectures. In this sense, the literature has been developing models specific to cell-free mMIMO that deal with system coordination, fronthaul signaling, required computational complexities of processing procedures, segmented fronthaul, transitioning from cellular network deployments, and integration to Open Radio Access Network (O-RAN) technologies. These advancements are instrumental in transforming cell-free mMIMO from a theoretical system to a practical application. Despite this, further study is needed to integrate existing models and develop practical evaluation tools to assess the feasibility of cell-free mMIMO and its enablers. This thesis addresses these gaps by proposing new tools to evaluate the feasibility of cell-free mMIMO networks regarding reliability and costs. The first tool focuses on evaluating the reliability of cell-free mMIMO. It is used to improve the understanding of possible failure impacts and to develop effective protection schemes for the fronthaul network of cell-free mMIMO networks. Results for an indoor office implementation with an area of 100 m2 and a Transmission-Reception Point (TRP) spacing of 20 m, demonstrate that cell-free systems with segmented fronthaul, i.e., with serial fronthaul connections between TRPs, require protection strategies. It is shown that interconnecting serial chains and partially duplicating serial chains (40% redundancy) are effective protection schemes. Finally, in the considered indoor scenarios, interconnection appears to be the most feasible alternative when the number of serial chains is higher than three. The second tool assesses the Total Cost of Ownership (TCO) of cell-free mMIMO and its enablers, considering essential aspects, like user demands, fronthaul bandwidth limitations, and hardware processing capacities. The tool is used to evaluate the costs of two functional splits from the literature that are equivalent to distributed and centralized processing architectures for cell-free mMIMO networks. Results for an ultra-dense urban scenario covering an area of 0.25 km2 with up to 800 TRPs, reveal that centralized processing is more feasible for most user demands, hardware configurations of TRP, and cost considerations. Despite this, distributed processing may be more feasible in limited cases of low demand (up to 50 Mbps per user) and under massive cost reductions for expenses related to TRPs deployment.

Distributed massive multiple-input multiple-output (DM-MIMO) gives a higher spectral efficiency and enhanced coverage area, compared to collocated massive MIMO (CM-MIMO). In general, for massive MIMO, time division duplex is preferable as it enables downlink (DL) precoding based on uplink (UL) channel estimation. A time division duplex (TDD) reciprocity calibration is then essential to compensate the gap between the UL-DL channels, which can be done completely in the base station relying on sounding reference signals (SRS). For a collocated array, relying on mutual coupling between antenna elements, each SRS is received with sufficient power in the array, enabling a reliable estimate of the calibration coefficients. Nevertheless, for DM-MIMO, much less power is collected in distant inter-cluster antennas which degrades the accuracy of the estimated calibration. In this paper, we propose a novel inter-cluster combining method (ICCM) which improves the signal-to-quantization-noise ratio (SQNR) of the SRS, and hence achieves a more robust calibration accuracy for practical DM-MIMO systems. Our experimental results of two 32-antenna arrays distributed in an indoor environment show that ICCM outperforms the existing state-of-the-art algorithms in the sense of lower DL error vector magnitude (EVM) by exploiting diversity and array gain efficiently.

This paper presents a measurement-based comparison between distributed and concentrated massive multiple-input multiple-output (MIMO) systems, which are called D-mMIMO and C-mMIMO systems, in an indoor environment considering a 400 MHz bandwidth centered at 3.5 GHz. In both cases, we have considered an array of 64 antennas in the base station and eight simultaneously active users. The work focuses on the characterization of both schemes in the up-link, considering the analysis of the sum capacity, the total spectral efficiency (SE) achievable by using the zero forcing (ZF) combining method, as well as the user fairness. The effect of the power imbalance between the different transmitters or user terminal (UT) locations, and thus, the benefits of performing an adequate power control are also investigated. The differences between the C-mMIMO and D-mMIMO channel performances are explained through the observation of the structure of their respective measured channel matrices through par...

This paper investigates channel-aware decision fusion empowered
by massive MIMO systems and reconfigurable intelligent surfaces
(RIS). By integrating both, we aim to improve goal-oriented (fusion)
performance despite the unique propagation challenges introduced.
Specifically, we investigate traditional favorable propagation properties
in the context of RIS-aided Massive MIMO decision fusion. The
above analysis is then leveraged (i) to design three sub-optimal simple
fusion rules suited for the large-array regime and (ii) to devise an
optimization criterion for RIS reflection coefficients based on longterm
channel statistics. Simulation results confirm the appeal of the
presented design.