arXiv subjects

Taufik Abrão

Publications and source records attributed to Taufik Abrão.

At least 37 records · Page 2Linked to original sources

arXiv2023

Energy Efficiency and Throughput of Random Access Protocols for RIS-Aided IoT Networks

Green Internet of Things (IoT) aims to enable a sustainable smart world by making energy efficiency (EE) the main performance indicator for IoT hardware and software. With respect to network design, this implies in developing energy-efficient communication protocols and network architectures adapted to the ubiquitousness of the IoT machine-type devices (MTDs) and the sporadic traffic generated by them, keeping a low power consumption at the MTDs-side. In this sense, reconfigurable intelligent surfaces (RISs) have presented the capacity of significantly improving the network coverage using mostly passive reflecting elements, drastically reducing the power expenditure. In this paper, we develop a realistic power consumption model and an expression for the overall system EE for RIS-aided IoT networks that adopt a two time-scale random access (RA) protocol to handle the uplink transmissions. Specifically, during each time slot of the RA protocol, the RIS covers a specific area of interest in the communication cell with a predefined set of phase-shift configurations, changing the channel qualities of the contending MTDs. Numerical results comparing the RA protocol performance reveal that access policies that exploit information of the channel qualities are suitable for green IoT networks, simultaneously attaining competitive EE and throughput combined with low power consumption at the MTDs-side.

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arXiv2023

Randomized Control of Wireless Temporal Coherence via Reconfigurable Intelligent Surface

A reconfigurable intelligent surface (RIS) can shape the wireless propagation channel by inducing controlled phase shift variations to the impinging signals. Multiple works have considered the use of RIS by time-varying configurations of reflection coefficients. In this work we use the RIS to control the channel coherence time and introduce a generalized discrete-time-varying channel model for RIS-aided systems. We characterize the temporal variation of channel correlation by assuming that a configuration of RIS' elements changes at every time step. The analysis converges to a randomized framework to control the channel coherence time by setting the number of RIS' elements and their phase shifts. The main result is a framework for a flexible block-fading model, where the number of samples within a coherence block can be dynamically adapted.

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arXiv2023

Multi-Power Level $Q$-Learning Algorithm for Random Access in NOMA mMTC Systems

The massive machine-type communications (mMTC) service will be part of new services planned to integrate the fifth generation of wireless communication (B5G). In mMTC, thousands of devices sporadically access available resource blocks on the network. In this scenario, the massive random access (RA) problem arises when two or more devices collide when selecting the same resource block. There are several techniques to deal with this problem. One of them deploys $Q$-learning (QL), in which devices store in their $Q$-table the rewards sent by the central node that indicate the quality of the transmission performed. The device learns the best resource blocks to select and transmit to avoid collisions. We propose a multi-power level QL (MPL-QL) algorithm that uses non-orthogonal multiple access (NOMA) transmit scheme to generate transmission power diversity and allow {accommodate} more than one device in the same time-slot as long as the signal-to-interference-plus-noise ratio (SINR) exceeds a threshold value. The numerical results reveal that the best performance-complexity trade-off is obtained by using a {higher {number of} power levels, typically eight levels}. The proposed MPL-QL {can deliver} better throughput and lower latency compared to other recent QL-based algorithms found in the literature

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arXiv2023

Massive MIMO and NOMA Bits-per-Antenna Efficiency under Power Allocation Policies

A comparative resource allocation analysis in terms of received bits-per-antenna spectral efficiency (SE) and energy efficiency (EE) in downlink (DL) single-cell massive multiple-input multiple-output (mMIMO) and non-orthogonal multiple access (NOMA) systems considering a BS equipped with many ($M$) antennas, while $K$ devices {operate} with a single-antenna, and {the loading of devices} $ρ= \frac{K}{M}$ ranging in $0<ρ\leq 2$ is carried out under three different \ac{PA} strategies: the inverse of the channel power allocation (PICPA), a modified water-filling ($Δ$-WF) allocation method, and the equal power allocation (EPA) reference method. Since the two devices per cluster are overlapped in the power domain in the NOMA system, the channel matrix requires transformation to perform the zero-forcing (ZF) precoding adopted in mMIMO. Hence, NOMA operating under many antennas can favor a group of devices with higher array gain, overcoming the mMIMO and operating conveniently in the higher loading range $0.6<ρ<2.0$. In such a scenario, a more realistic and helpful metric consists of {evaluating} the area under SE and EE curves, by measuring the bit-per-antenna and bit-per-antenna-per-watt efficiency, respectively. Our numerical results confirm a superiority of NOMA w.r.t. mMIMO of an order of 3x for the SE-area and 2x for the EE-area metric.

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arXiv2023

GA-Aided Directivity in Volumetric and Planar Massive-Antenna Array Design

The problem of directivity enhancement, leading to the increase in the directivity gain over a certain desired angle of arrival/departure (AoA/AoD), is considered in this work. A new formulation of the volumetric array directivity problem is proposed using the rectangular coordinates to describe each antenna element and the desired azimuth and elevation angles with a general element pattern. Such a directivity problem is formulated to find the optimal minimum distance between the antenna elements $d_\text{min}$ aiming to achieve as high directivity gains as possible. {An expedited implementation method is developed to place the antenna elements in a distinctive plane dependent on ($θ_0$; $ϕ_0$). A novel concept on optimizing directivity for the uniform planar array (OUPA) is introduced to find a quasi-optimal solution for the non-convex optimization problem with low complexity. This solution is reached by deploying the proposed successive evaluation and validation (SEV) method. {Moreover, the genetic} algorithm (GA) method was deployed to find the directivity optimization solution expeditiously. For a small number of antenna elements {, typically $N\in [4,\dots, 9]$,} the achievable directivity by GA optimization demonstrates gains of $\sim 3$ dBi compared with the traditional beamforming technique, using steering vector for uniform linear arrays (ULA) and uniform circular arrays (UCA), while gains of $\sim1.5$ dBi are attained when compared with an improved UCA directivity method. For a larger number of antenna elements {, two improved GA procedures, namely GA-{\it marginal} and GA-{\it stall}, were} proposed and compared with the OUPA method. OUPA also indicates promising directivity gains surpassing $30$ dBi for massive MIMO scenarios.

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arXiv2022

User-Centric Perspective in Random Access Cell-Free Aided by Spatial Separability

In a cell-free massive multiple-input multiple-output (CF-mMIMO) network, multiple access points (APs) actively cooperate to serve users' equipment (UEs). We consider how the random access (RA) problem can be addressed by such a network under the occurrence of pilot collisions. To find a solution, we embrace the user-centric perspective, which basically dictates that only a preferred set of APs needs to serve a UE. Due to the success of the strongest-user collision resolution (SUCRe) protocol for cellular (Ce) mMIMO, we extend it by considering the new setting. Besides, we establish that the user-centric perspective naturally equips a CF network with robust fundamentals for resolving collisions. We refer to this foundation as spatial separability, which enables multiple colliding UEs to access the network simultaneously. We then propose two novel RA protocols for CF-mMIMO: i) the baseline cell-free (BCF) that resolves collisions with the concept of spatial separability alone, and ii) the cell-free SUCRe (CF-SUCRe) that combines SUCRe and spatial separability principle to resolve collisions. We evaluate our proposed RA protocols against the Ce-SUCRe. Respectively, the BCF and CF-SUCRe can support 7x and 4x more UEs' access on average compared to the Ce-SUCRe with an average energy efficiency gain based on total power consumed (TPC) by the network per access attempt of 52x and 340x. Among our procedures, even with a higher overhead, the CF-SUCRe is superior to BCF regarding TPC per access attempt. This is because the combination of methods for collision resolution allows many APs to be disconnected from the RA process without sacrificing much the performance. Finally, our numerical results presented in this work can be reproduced using the code package available on: github.com/victorcroisfelt/cf-ra-spatial-separability.

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arXiv2021

Decentralized Design of Fast Iterative Receivers for Massive and Extreme-Large MIMO Systems

Despite the extensive use of a centralized approach to design receivers at the base station for massive multiple-input multiple-output (M-MIMO) systems, their actual implementation is a major challenge due to several bottlenecks imposed by the large number of antennas. One way to deal with this problem is by fully decentralizing the classic zero-forcing receiver across multiple processing nodes based on the gradient descent method. In this paper, we first explicitly relate this decentralized receiver to a distributed version of the Kaczmarz algorithm and to the use of the successive interference cancellation (SIC) philosophy to mitigate the residual across nodes. In addition, we propose two methods to further accelerate the initial convergence of these iterative decentralized receivers by exploring the connection with the Kaczmarz algorithm: 1) a new Bayesian distributed receiver, which can eliminate noise on an iteration basis; 2) a more practical method for choosing the relaxation parameter. The discussion also consider spatial non-stationarities that arise when the antenna arrays are extremely large (XL-MIMO). We were able to improve the numerical results for both spatially stationary and non-stationary channels, but mainly the non-stationary performance can still be improved compared to the centralized ZF receiver. Future research directions are provided with the aim of further improving the applicability of the receiver based on the principle of successive residual cancellation (SRC).

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arXiv2021

Exploring the Non-Overlapping Visibility Regions in XL-MIMO Random Access Protocol

The recent extra-large scale massive multiple-input multiple-output (XL-MIMO) systems are seen as a promising technology for providing very high data rates in increased user-density scenarios. Spatial non-stationarities and visibility regions (VRs) appear across the XL-MIMO array since its large dimension is of the same order as the distances to the user-equipments (UEs). Due to the increased density of UEs in typical applications of XL-MIMO systems and the scarcity of pilots, the design of random access (RA) protocols and scheduling algorithms become challenging. In this paper, we propose a joint RA and scheduling protocol, namely non-overlapping VR XL- MIMO (NOVR-XL) RA protocol, which takes advantage of the different VRs of the UEs for improving RA performance, besides seeking UEs with non-overlapping VRs to be scheduled in the same payload data pilot resource. Our results reveal that the proposed scheme achieves significant gains in terms of sum rate compared with traditional RA schemes, as well as reducing access latency and improving connectivity performance as a whole.

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arXiv2021

Accelerated Randomized Methods for Receiver Design in Extra-Large Scale MIMO Arrays

Massive multiple-input-multiple-output (M-MIMO) features a capability for spatial multiplexing of large number of users. This number becomes even more extreme in extra-large (XL-MIMO), a variant of M-MIMO where the antenna array is of very large size. Yet, the problem of signal processing complexity in M-MIMO is further exacerbated by the XL size of the array. The basic processing problem boils down to a sparse system of linear equations that can be addressed by the randomized Kaczmarz (RK) algorithm. This algorithm has recently been applied to devise low-complexity M-MIMO receivers; however, it is limited by the fact that certain configurations of the linear equations may significantly deteriorate the performance of the RK algorithm. In this paper, we embrace the interest in accelerated RK algorithms and introduce three new RK-based low-complexity receiver designs. In our experiments, our methods are not only able to overcome the previous scheme, but they are more robust against inter-user interference (IUI) and sparse channel matrices arising in the XL-MIMO regime. In addition, we show that the RK-based schemes use a mechanism similar to that used by successive interference cancellation (SIC) receivers to approximate the regularized zero-forcing (RZF) scheme.

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arXiv2021

Mixed Gibbs Sampling Detector in High-Order Modulation Large-Scale MIMO Systems

A neighborhood restricted Mixed Gibbs Sampling (MGS) based approach is proposed for low-complexity high-order modulation large-scale Multiple-Input Multiple-Output (LS-MIMO) detection. The proposed LS-MIMO detector applies a neighborhood limitation (NL) on the noisy solution from the MGS at a distance d - thus, named d-simplified MGS (d-sMGS) - in order to mitigate its impact, which can be harmful when a high order modulation is considered. Numerical simulation results considering 64-QAM demonstrated that the proposed detection method can substantially improve the MGS algorithm convergence, whereas no extra computational complexity per iteration is required. The proposed d-sMGS-based detector suitable for high-order modulation LS-MIMO further exhibits improved performance vs. complexity tradeoff when the system loading is high, i.e., when K >= 0.75. N. Also, with increasing the number of dimensions, i.e., increasing the number of antennas and/or modulation order, a smaller restriction of 2-sMGS was shown to be a more interesting choice than 1-sMGS.

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arXiv2020

Antenna Selection for Improving Energy Efficiency in XL-MIMO Systems

We consider the recently proposed extra-large scale massive multiple-input multiple-output (XL-MIMO) systems, with some hundreds of antennas serving a smaller number of users. Since the array length is of the same order as the distance to the users, the long-term fading coefficients of a given user vary with the different antennas at the base station (BS). Thus, the signal transmitted by some antennas might reach the user with much more power than that transmitted by some others. From a green perspective, it is not effective to simultaneously activate hundreds or even thousands of antennas, since the power-hungry radio frequency (RF) chains of the active antennas increase significantly the total energy consumption. Besides, a larger number of selected antennas increases the power required by linear processing, such as precoding matrix computation, and short-term channel estimation. In this paper, we propose four antenna selection (AS) approaches to be deployed in XL-MIMO systems aiming at maximizing the total energy efficiency (EE). Besides, employing some simplifying assumptions, we derive a closed-form analytical expression for the EE of the XL-MIMO system, and propose a straightforward iterative method to determine the optimal number of selected antennas able to maximize it. The proposed AS schemes are based solely on long-term fading parameters, thus, the selected antennas set remains valid for a relatively large time/frequency intervals. Comparing the results, we find that the genetic-algorithm based AS scheme usually achieves the best EE performance, although our proposed highest normalized received power AS scheme also achieves very promising EE performance in a simple and straightforward way.

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arXiv2020

Stochastic Channel Models for Massive and XL-MIMO Systems

In this paper, stochastic channel models for massive MIMO (M-MIMO) and extreme large MIMO (XL- MIMO) system applications are described, evaluated and systematically compared. This work aims to cover new aspects of massive MIMO stochastic channel models in a comprehensive and systematic way. For that, we compare different models, presenting graphically and intuitively the behavior of each model. Each massive MIMO channel model emulates the environment using different methodologies and properties. Using metrics such as capacity, SINR, singular values decomposition (SVD), and condition number, one can understand the influence of each characteristic on the modelling and how it differentiates from other models. Moreover, in new XL-MIMO scenarios, where the near-field and visible region (VR) effects arise, our finding demonstrate that for the two assumed schemes of clusters distribution, the clusters location influences the performance of the conjugate beamforming and zero-forcing (ZF) precoding due to the correlation effect, which have been analysed from the geometric massive MIMO channel models.

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arXiv2020

XL-MIMO Energy-Efficient Antenna Selection under Non-Stationary Channels

Massive multiple-input-multiple-output (M-MIMO) is a key technology for 5G networks. Within this research area, new types of deployment are arising, such as the extremely-large regime (XL- MIMO), where the antenna array at the base station (BS) has extreme dimensions. As a consequence, spatial non-stationary properties appear as the users see only a portion of the antenna array, which is called visibility region (VR). In this challenging transmission-reception scenario, an algorithm to select the appropriate antenna-elements for processing the received signal of a given user in the uplink (UL), as well as to transmit the signal of this user during downlink (DL) is proposed. The advantage of not using all the available antenna-elements at the BS is the computational burden and circuit power consumption reduction, improving the energy efficiency (EE) substantially. Numerical results demonstrate that one can increase the EE without compromising considerably the spectral efficiency (SE). Under few active users scenario, the performance of the XL-MIMO system shows that the EE is maximized using less than 20% of the antenna-elements of the array, without compromising the SE severely.

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arXiv2020

A Grant-based Random Access Protocol in Extra-Large Massive MIMO System

Extra-large massive multiple-input multiple-output (XL-MIMO) systems is a new concept, where spatial non-stationarities allow activate a high number of user equipments (UEs). This paper focuses on a grant-based random access (RA) approach in the novel XL-MIMO channel scenarios. Modifications in the classical Strongest User Collision Resolution (SUCRe) protocol have been aggregated to explore the visibility regions (VRs) overlapping in XL-MIMO. The proposed grant-based RA protocol takes advantage of this new degree of freedom for improving the number of access attempts and accepted UEs. As a result, the proposed grant-based protocol for XL-MIMO systems is capable of reducing latency in the pilot allocation step.

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arXiv2020

Direction-of-Arrival Estimation Methods: A Performance-Complexity Tradeoff Perspective

This work analyses the performance-complexity tradeoff for different direction of arrival (DoA) estimation techniques. Such tradeoff is investigated taking into account uniform linear array structures. Several DoA estimation techniques have been compared, namely the conventional Delay-and-Sum (DS), Minimum Variance Distortionless Response (MVDR), Multiple Signal Classifier (MUSIC) subspace, Estimation of Signal Parameters via Rotational Invariance Technique (ESPRIT), Unitary-ESPRIT and Fourier Transform method (FT-DoA). The analytical formulation of each estimation technique as well the comparative numerical results are discussed focused on the estimation accuracy versus complexity tradeoff. The present study reveals the behavior of seven techniques, demonstrating promising ones for current and future location applications involving DoA estimation, especially for 5G massive MIMO systems.

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arXiv2020

Heuristic Chaotic Hurricane-aided Efficient Power Assignment for Elastic Optical Networks

In this paper we propose a dynamical transmission power allocation for elastic optical networks (EONs) based on the evolutionary hurricane search optimization (HSO) algorithm with a chaotic logistic map diversification strategy with the purpose of improving the capability to escape from local optima, namely CHSO. The aiming is the dynamical control of the transmitted optical powers according to the each link state variations due to traffic fluctuations, channel impairments, as well as other channel-power coupling effects. Such realistic EON scenarios are affected mainly by the channel estimation inaccuracy, channel ageing and power fluctuations. The link state is based on the channel estimation and quality of transmission (QoT) parameters obtained from the optical performance monitors (OPMs). Numerical results have demonstrated the effectiveness of the CHSO to dynamically mitigate the power penalty under real measurements conditions with uncertainties and noise, as well as when perturbations in the optical transmit powers are considered.

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arXiv2020

Low-Complexity Distributed XL-MIMO for Multiuser Detection

In this paper, the zero-forcing and regularized zero-forcing schemes operating in crowded extra-large MIMO (XL-MIMO) scenarios with a fixed number of subarrays have been emulated using the randomized Kaczmarz algorithm (rKA). For that, non-stationary properties have been deployed through the concept of visibility regions when considering two different power normalization methods of non-stationary channels. We address the randomness design of rKA based on the exploitation of spatial non-stationary properties. Numerical results show that, in general, the proposed rKA-based combiner applicable to XL-MIMO systems can considerably decrease computational complexity of the signal detector by paying with small performance losses.

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arXiv2020

Achieving Fair Random Access Performance in Massive MIMO Crowded Machine-Type Networks

The use of massive multiple-input multiple-output (MIMO) to serve a crowd of user equipments (UEs) is challenged by the deficit of pilots. Assuming that the UEs are intermittently active, this problem can be addressed by a shared access to the pilots and a suitable random access (RA) protocol. The strongest-user collision resolution (SUCRe) is a previously proposed RA protocol that often privileges the UEs closer to the base station (BS). In contrast, we propose a novel RA protocol using a decentralized pilot power allocation method that aims at a fairer performance. The proposed access class barring with power control (ACBPC) protocol allows each UE to estimate, without additional overhead, how many UEs collided for the chosen pilot and calculate an ACB factor, which is then used to determine the pilot retransmission probability in the next protocol step. The results show that the proposed ACBPC protocol is superior to SUCRe in terms of providing a fair connectivity for very crowded networks, although still being distributed and uncoordinated as the original SUCRe protocol.

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