arXiv subjects

Juan Vidal Alegría

Publications and source records attributed to Juan Vidal Alegría.

14 recordsLinked to original sources

arXiv2026

Trade-Offs in Decentralized Gigantic MIMO with Hard-Boundary Constraints

To maintain the antenna apertures offered by 5G massive MIMO systems operating at the sub-6GHz band, known as FR1, 6G base stations (BSs) using the upper-mid band, FR3, should increase the number of antennas by a factor 4-8, giving rise to gigantic MIMO. This poses challenges in terms of processing complexity and interconnection bandwidth. The WAX framework, previously introduced for exploring trade-offs in decentralized architectures, may offer the flexibility needed to tackle these challenges. However, no results have been established on the applicability of this framework in the presence of hard-boundary constraints. The current work explores gigantic MIMO implementations based on a novel adaptation of the WAX framework, where the decentralized processing is performed by non-cooperating hardware modules. These modules may be implemented through state-of-the-art massive MIMO baseband units (BBUs). The results show the potential of the proposed framework towards exploiting trade-offs between complexity and performance in practical gigantic MIMO implementations.

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arXiv2026

Robust High Mobility NLOS UE Beamforming Strategy for Gigantic MIMO

Maintaining robust and stable communication links in high-mobility scenarios is challenging for time-division duplex (TDD) reciprocity-based gigantic MIMO systems due to rapid channel variations, especially in non-line-of-sight (NLOS) conditions. This paper proposes a user equipment (UE) beamforming strategy that enables reliable links in high mobility without additional pilot overhead. The proposed strategy aligns the UE beamforming direction with the travel axis. Our analysis shows that this choice minimizes the Doppler spread of the channel, resulting in improved temporal stability. We evaluate this approach through simulations in scattering-rich environments representative of gigantic MIMO deployments. Numerical results confirm that movement-aligned UE beamforming enhances link robustness, increases achievable data rates, and reduces pilot signaling requirements, thereby lowering UE power consumption. These findings indicate that travel-axis-aligned UE beamforming is a promising method for improving reliability in future high-mobility wireless systems.

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arXiv2025

Zero-Forcing MU-MIMO Precoding under Power Amplifier Non-Linearities

In multi-user multiple-input multiple-output (MU-MIMO) systems, the non-linear behavior of the power amplifiers (PAs) may cause degradation of the linear precoding schemes dealing with interference between user equipments (UEs), e.g., the zero-forcing (ZF) precoder. One way to minimize this effect is to use digital-pre-distortion (DPD) modules to linearize the PAs. However, using perfect DPD modules is costly and it may incur significant power consumption. As an alternative, we consider the problem of characterizing non-linearity-aware ZF (NLA-ZF) precoding schemes, hereby defined as linear precoders that achieve perfect interference cancellation in the presence of PA non-linearity by exploiting knowledge of this non-linear response. We provide initial iterative solutions that allow achieving NLA-ZF (up to adjustable tolerance) in a two-UE downlink MU-MIMO scenario where the base station (BS) has an even number of antennas, and each antenna is connected to a PA exhibiting third-order memory-less non-linear behavior. The proposed approach allows for performance gains in scenarios with significant residual interference.

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arXiv2025

Large Intelligent Surfaces with Low-End Receivers: From Scaling to Antenna and Panel Selection

Feasibility of the promising large intelligent surface (LIS) concept, as well as its scalability, relies on the use of low-cost hardware components, raising concerns about the effects of hardware distortion. We analyze LIS systems with receive-chain (RX-chain) hardware distortion, showing how it may limit performance gains when scaling up these systems. In particular, using the memory-less polynomial model, analytical expressions are derived for the signal to noise plus distortion ratio (SNDR) after applying maximum ratio combining (MRC). We also study the effect of back-off and automatic gain control on the RX-chains. The derived expressions enable us to evaluate the scalability of LIS when hardware impairments are present. The cost of assuming ideal hardware is further analyzed by quantifying the minimum scaling required to achieve the same performance with non-ideal hardware. The analytical expressions derived in this work are also used to propose practical antenna selection schemes for LIS, and we show that such schemes can improve the performance significantly leading to increased energy efficiency. Specifically, by turning off RX-chains with lower contribution to the post-MRC SNDR, we can reduce the energy consumption while maintaining performance. We also consider a more practical scenario where the LIS is deployed as a grid of multi-antenna panels, and we propose panel selection schemes to optimize the complexity-performance trade-offs and improve the system overall efficiency.

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arXiv2025

Spectrum Efficiency and Processing Latency Trade-offs in Panel-Based LIS

The next generation wireless systems will face stringent new requirements, including ultra-low latency, high data rates and enhanced reliability. Large Intelligent Surfaces, is one proposed solution that has the potential to solve these high demands. The real-life deployment of such systems involves different design considerations with non-trivial trade-offs. This paper investigates the trade-off between spectral efficiency and processing latency, considering different antenna distribution schemes and detection algorithms. A latency model for the physical layer processing has been developed, using real FPGA and application-specific instruction processor (ASIP) hardware implementation results. Simulation results using an indoor environment show that distributing antennas throughout the scenario improves overall reliability, while the impact from this on latency is limited both when using zero-forcing (ZF) and Minimum Mean Square Error (MMSE) detection. Changing the detection algorithm to maximum-ratio combining (MRC) from ZF or MMSE, however, reduces the latency significantly, even if a larger number of antennas are needed to achieve a similar spectrum efficiency.

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arXiv2025

Dimensionally-Efficient Transmission and Storage of Unitary Matrices

Unitary matrices are the basis of a large number of signal processing applications. In many of these applications, finding ways to efficiently store, and even transmit these matrices, can significantly reduce memory and throughput requirements. In this work, we study the problem of efficient transmission and storage of unitary matrices. Specifically, we explicitly derive a dimensionally-efficient parametrization (DEP) for unitary matrices that allows identifying them with sequences of real numbers, where the dimension coincides with the dimension of the unitary group where they lie. We also characterize its inverse map that allows retrieving the original unitary matrices from their DEP. The proposed approach effectively allows halving the dimension with respect to naively considering all the entries of each unitary matrix, thus reducing the resources required to store and transmit these matrices. Furthermore, we show that the sequence of real numbers associated to the proposed DEP is bounded, and we delimit the interval where these numbers are contained, facilitating the implementation of quantization approaches with limited distortion. On the other hand, we outline ways to further reduce the dimension of the DEP when considering more restrictive constraints for matrices that show up in certain applications. The numerical results showcase the potential of the proposed approach in general settings, as well as in three specific applications of current interest for wireless communications research.

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arXiv2025

Over-the-Air DPD and Reciprocity Calibration in Massive MIMO and Beyond

Non-linear transceivers and non-reciprocity of downlink and uplink channels are two major challenges in the deployment of massive multiple-input-multiple-output (MIMO) systems. We consider an over-the-air (OTA) approach for digital pre-distortion (DPD) and reciprocity calibration to jointly address these issues. In particular, we consider a memory-less non-linearity model for the base station (BS) transmitters, and we propose a method to perform both linearization and reciprocity calibration based on mutual coupling OTA measurements between BS antennas. We show that, by using only the OTA-based data, we can linearize the transmitters and design the calibration to compensate for both the non-linearity and non-reciprocity of BS transceivers. This allows alleviating the requirement to have dedicated hardware modules for transceiver linearization. Moreover, the proposed reciprocity calibration method is solely based on closed-form linear transformations, achieving a significant complexity reduction over state-of-the-art reciprocity methods, which assume linear transceivers, and rely on iterative methods. Simulation results showcase the potential of our approach in terms of the calibration matrix estimation error and downlink data-rates when applying zero-forcing (ZF) precoding after using our OTA-based DPD and reciprocity calibration method.

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arXiv2025

Channel Orthogonalization in Panel-Based LIS

Large intelligent surface (LIS) has gained momentum as a potential 6G-enabling technology that expands the benefits of massive multiple-input multiple-output (MIMO). On the other hand, orthogonal space-division multiplexing (OSDM) may give a promising direction for efficient exploitation of the spatial resources, analogous as what is achieved with orthogonal frequency-division multiplexing (OFDM) in the frequency domain. To this end, we study how to enforce channel orthogonality in a panel-based LIS (P-LIS) scenario. Our proposed method consists of having a subset of active LIS-panels coherently serving a set of users, and another subset of LIS-panels operating in a novel low-power mode by implementing a receive and re-transmit (RRTx) process. This results in an inter-symbol interference (ISI) channel, where we characterize the RRTx processing required to achieve simultaneous orthogonality in time and space. We then employ the remaining degrees of freedom (DoFs) from the orthogonality constraint to minimize the RRTx processing power, where we derive a closed-form global minimizer, allowing for efficient implementation of the proposed scheme.

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arXiv2025

Channel Orthogonalization with Reconfigurable Surfaces: General Models, Theoretical Limits, and Effective Configuration

We envision a future in which multi-antenna technology effectively exploits the spatial domain as a set of non-interfering orthogonal resources, allowing for flexible resource allocation and efficient modulation/demodulation. We may refer to this paradigm as orthogonal space-division multiplexing (OSDM). On the other hand, reconfigurable intelligent surface (RIS) has emerged as a promising technology which allows shaping the propagation environment for improved performance. This paper studies the ability of three extended types of reconfigurable surface (RS), including the recently proposed beyond diagonal RIS (BD-RIS), to achieve perfectly orthogonal channels in a general multi-user multiple-input multiple-output (MU-MIMO) scenario. We consider practical implementations for the three types of RS consisting of passive components, and obtain the corresponding restrictions on their reconfigurability. We then use these restrictions to derive closed-form conditions and explicit expressions for achieving arbitrary (orthogonal) channels. We also study the problem of exploiting the degrees of freedom (DoFs) from the channel orthogonality constraint to maximize the channel gain while maintaining the passive RS constraints, and we propose some initial methods with satisfying performance. Finally, we provide some channel estimation and RS configuration techniques within this framework, where the computations are assumed to be performed at the BS, and we derive some limits on the amount of overhead required to achieve channel orthogonalization with RSs. The numerical results confirm the theoretical findings, showing that channel orthogonality with passive RSs can be effectively achieved in practical environments as long as the direct channel is not significant with respect to the RS cascaded channel. We thus take some important steps towards realizing OSDM.

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arXiv2025

Hardware Distortion Modeling for Panel Selection in Large Intelligent Surfaces

Hardware distortion in large intelligent surfaces (LISs) may limit their performance when scaling up such systems. It is of great importance to model the non-ideal effects in their transceivers to study the hardware distortions that can affect their performance. Therefore, we have focused on modeling and studying the effects of nonlinear RX-chains in LISs. We first derive expressions for SNDR of a LIS with a memory-less polynomial-based model at its RX-chains. Then we propose a simplified double-parameter exponential model for the distortion power and show that compared to the polynomial based model, the exponential model can improve the analytical tractability for SNDR optimization problems. In particular, we consider a panel selection optimization problems in a panel-based LIS scenario and show that the proposed model enables us to derive two closed-form sub-optimal solutions for panel selection, and can be a favorable alternative to high-order polynomial models in terms of computation complexity, especially for theoretical works on hardware distortion in MIMO and LIS systems. Numerical results show that the sub-optimal closed-form solutions have a near-optimal performance in terms of SNDR compared to the global optimum found by high-complexity heuristic search methods.

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arXiv2025

Decentralized Multi-Antenna Architectures with Unitary Constraints

The increase in the number of base station (BS) antennas calls for efficient solutions to deal with the increased interconnection bandwidth and processing complexity of traditional centralized approaches. Decentralized approaches are thus gaining momentum, since they achieve important reductions in data/processing volume by preprocessing the received signals before forwarding them to a central node. The WAX framework offers a general description of decentralized architectures with arbitrary interplay between interconnection bandwidth and decentralized processing complexity, but the applicability of this framework has only been studied assuming unrestricted baseband processing. We consider an adaptation of the WAX framework where the decentralized processing has unitary restriction, which allows for energy-efficient implementations based on reconfigurable impedance networks at the cost of some performance loss. Moreover, we propose an effective method to minimize the performance gap with respect to centralized processing. The previous method gives a first step towards characterizing the information-lossless trade-off between interconnection bandwidth and processing complexity in decentralized architectures with unitary constraints.

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arXiv2023

Trade-Offs in Decentralized Multi-Antenna Architectures: Sparse Combining Modules for WAX Decomposition

With the increase in the number of antennas at base stations (BSs), centralized multi-antenna architectures have encountered scalability problems from excessive interconnection bandwidth to the central processing unit (CPU), as well as increased processing complexity. Thus, research efforts have been directed towards finding decentralized receiver architectures where a part of the processing is performed at the antenna end (or close to it). A recent paper put forth an information-lossless trade-off between level of decentralization (inputs to CPU) and decentralized processing complexity (multiplications per antenna). This trade-off was obtained by studying a newly defined matrix decomposition--the WAX decomposition--which is directly related to the information-lossless processing that should to be applied in a general framework to exploit the trade-off. {The general framework consists of three stages: a set of decentralized filters, a linear combining module, and a processing matrix applied at the CPU; these three stages are linear transformations which can be identified with the three constituent matrices of the WAX decomposition. The previous work was unable to provide explicit constructions for linear combining modules which are valid for WAX decomposition, while it remarked the importance of these modules being sparse with 1s and 0s so they could be efficiently implemented using hardware accelerators.} In this work we present a number of constructions, as well as possible variations of them, for effectively defining linear combining modules which can be used in the WAX decomposition. Furthermore, we show how these structures facilitate decentralized calculation of the WAX decomposition for applying information-lossless processing in architectures with an arbitrary level of decentralization.

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arXiv2021

Trade-offs in Decentralized Multi-Antenna Architectures: The WAX Decomposition

Current research on multi-antenna architectures is trending towards increasing the amount of antennas in the base stations (BSs) so as to increase the spectral efficiency. As a result, the interconnection bandwidth and computational complexity required to process the data using centralized architectures is becoming prohibitively high. Decentralized architectures can reduce these requirements by pre-processing the data before it arrives at a central processing unit (CPU). However, performing decentralized processing introduces also cost in complexity/interconnection bandwidth at the antenna end which is in general being ignored. This paper aims at studying the interplay between level of decentralization and the associated complexity/interconnection bandwidth requirement at the antenna end. To do so, we propose a general framework for centralized/decentralized architectures that can explore said interplay by adjusting some system parameters, namely the number of connections to the CPU (level of decentralization), and the number of multiplications/outputs per antenna (complexity/interconnection bandwidth). We define a novel matrix decomposition, the WAX decomposition, that allows information-lossless processing within our proposed framework, and we use it to obtain the operational limits of the interplay under study. We also look into some of the limitations of the WAX decomposition.

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arXiv2020

Trade-offs In Quasi-Decentralized Massive MIMO

Typical massive multiple-input multiple-output (MIMO) architectures consider a centralized approach, in which all baseband data received by each antenna has to be sent to a central processing unit (CPU) to be processed. Due to the enormous amount of antennas expected in massive MIMO base stations (BSs), the number of connections to the CPU required in centralized massive MIMO is not scalable. In recent literature decentralized approaches have been proposed to reduce the number of connections between the antennas and the CPU. However, the reduction in the connections to the CPU requires more outputs per antenna to be generated. We study the trade-off between number of connections to the CPU and number of outputs per antenna. We propose a generalized architecture that allows exploitation of this trade-off, and we define a novel matrix decomposition that allows lossless linear equalization within our proposed architecture.

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