arXiv subjects

Abolfazl Amiri

Publications and source records attributed to Abolfazl Amiri.

18 recordsLinked to original sources

arXiv2024

Overview of NR Enhancements for Extended Reality (XR) in 3GPP 5G-Advanced

Extended reality (XR) is unlocking numerous possibilities and continues attracting individuals and larger groups across different business sectors. With Virtual reality (VR), Augmented reality (AR), or Mixed reality (MR) it is possible to improve the way we access, deliver and exchange information in education, health care, entertainment, and many other aspects of our daily lives. However, to fully exploit the potential of XR, it is important to provide reliable, fast and secure wireless connectivity to the users of XR and that requires refining existing solutions and tailoring those to support XR services. This article presents a tutorial on 3GPP 5G-Advanced Release 18 XR activities, summarizing physical as well as higher layer enhancements introduced for New Radio considering the specifics of XR. In addition, we also describe enhancements across 5G system architecture that impacted radio access network. Furthermore, the paper provides system-level simulation results for several Release 18 enhancements to show their benefits in terms of XR capacity and power saving gains. Finally, it concludes with an overview of future work in Release 19 that continues developing features to support XR services.

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arXiv2024

Revisiting Data Recovery Loops in 6G Networks

Mechanisms for data recovery and packet reliability are essential components of the upcoming 6th generation (6G) communication system. In this paper, we evaluate the interaction between a fast hybrid automatic repeat request (HARQ) scheme, present in the physical and medium access control layers, and a higher layer automatic repeat request (ARQ) scheme which may be present in the radio link control layer. Through extensive system-level simulations, we show that despite its higher complexity, a fast HARQ scheme yields > 66 % downlink average user throughput gains over simpler solutions without energy combining gains and orders of magnitude larger gains for users in challenging radio conditions. We present results for the design trade-off between HARQ and higher-layer data recovery mechanisms in the presence of realistic control and data channel errors, network delays, and transport protocols. We derive that, with a suitable design of 6G control and data channels reaching residual errors at the medium access control layer of 5 E-5 or better, a higher layer data recovery mechanism can be disabled. We then derive design targets for 6G control channel design, as well as promising enhancements to 6G higher layer data recovery to extend support for latency-intolerant services.

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arXiv2023

PDU-set Scheduling Algorithm for XR Traffic in Multi-Service 5G-Advanced Networks

In this paper, we investigate a dynamic packet scheduling algorithm designed to enhance the eXtended Reality (XR) capacity of fifth-generation (5G)-Advanced networks with multiple cells, multiple users, and multiple services. The scheduler exploits the newly defined protocol data unit (PDU)-set information for XR traffic flows to enhance its quality-of-service awareness. To evaluate the performance of the proposed solution, advanced dynamic system-level simulations are conducted. The findings reveal that the proposed scheduler offers a notable improvement in increasing XR capacity up to 45%, while keeping the same enhanced mobile broadband (eMBB) cell throughput as compared to the well-known baseline schedulers.

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arXiv2023

Performance of Joint XR and Best Effort eMBB Traffic in 5G-Advanced Networks

In this paper, we address the joint performance of eXtended reality (XR) and best effort enhanced mobile broadband (eMBB) traffic for a 5G-Advanced system. Although XR users require stringent throughput and latency performance, operators do not lose significant additional network capacity when adding XR users to an eMBB dominated network. For instance, adding an XR service at 45 Mbps with 10 ms packet delay budget, yields close to a 45 Mbps drop in eMBB capacity. In an XR only network layer, we show how the capacity in number of supported XR users depends significantly on the rate but also the latency budget. We show also how the XR service capacity is significantly reduced in the mixed service setting as the system goes into full load and other-cell interference becomes significant. The presented results can be used by cellular service providers to assess their networks performance of XR traffic based on their current eMBB performance, or as input to dimensioning to be able to serve certain XR traffic loads.

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arXiv2022

Extended Reality over 3GPP 5G-Advanced New Radio: Link Adaptation Enhancements

One of the rapidly emerging services for fifth-generation (5G)-Advanced is eXtended Reality (XR) which combines several immersive experiences and cloud gaming services. Those services are demanding as they call for relatively high data rates under tight latency constraints, sometimes also referred to as dependable real-time applications. Supporting as many XR users per cell requires highly efficient radio solutions. In this paper, we propose an enhanced channel quality indicator (CQI) that results in a better link adaptation to unleash the full performance potential of code block group (CBG) based transmissions for XR cases. We present both an analytical analysis of the related problems and solutions, as well as an extensive dynamic system-level performance assessment in line with the 3rd generation partnership project (3GPP)-defined advanced simulation methodologies. Our results show an increased XR system capacity of 17% to 33% as compared to what can be supported by current 5G systems with baseline CQI schemes. We also present enhanced CQI complexity-reducing techniques based on derived closed-form expressions that are attractive to the user equipment (UE) implementation.

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arXiv2022

Enhanced Link Adaptation for Extended Reality Code Block Group based HARQ Transmission

Extended Reality (XR) is one of the most important media applications in 5\textsuperscript{th} Generation (5G) and 5G-Advanced. XR traffic is characterized by high data rates with bounded latency constraints, which is challenging for bandwidth-constrained wireless systems. In this paper, we propose two new low-complexity enhanced Outer Loop Link Adaptation (eOLLA) algorithms that significantly improve the downlink system capacity in terms of satisfied XR users. The algorithms exploit the Code Block Group (CBG)-based Hybrid Automatic Repeat reQuest (HARQ) multi-bit feedback for minimizing the radio resource utilization in retransmissions by controlling the first and second block error operation points. Evaluation by means of both analytical assessment and realistic system-level simulations verifies that the proposed eOLLA algorithms increase system capacity by up to \SI{67}{\percent} compared to known OLLA algorithms with traditional transport block based HARQ.

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arXiv2022

QoS-Aware User Scheduling in Crowded XL-MIMO Systems Under Non-Stationary Multi-State LoS/NLoS Channels

Providing minimum quality-of-service (QoS) in crowded wireless communications systems, with high user density, is challenging due to the network structure with limited transmit power budget and resource blocks. Smart resource allocation methods, such as user scheduling, power allocation, and modulation and coding scheme selection, must be implemented to cope with the challenge. Aiming to enhance the number of served users with minimum QoS in the downlink (DL) channel of crowded extra-large scale massive multiple-input multiple-output (XL-MIMO) systems, in this paper we propose a QoS-aware joint user scheduling and power allocation technique. The proposed technique is constituted by two sequential procedures: the clique search-based scheduling (CBS) algorithm for user scheduling followed by optimal power allocation with transmit power budget and minimum achievable rate per user constraints. To accurately evaluate the proposed technique in the XL-MIMO scenario, we propose a generalized non-stationary multi-state channel model based on spherical-wave propagation assuming that users under LoS and NLoS transmission coexist in the same communication cell. Such model considers that users under different channel states experience different propagation aspects both in the multipath fading model and the path loss rule. Numerical results on the achievable sum-rate, number of scheduled users, and distribution of the scheduled users reveal that the proposed CBS algorithm provides a fair coverage over the whole cell area, achieving remarkable numbers of scheduled users when users under the LoS and NLoS channel states coexist in the communication cell.

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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

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

Uncoordinated and Decentralized Processing in Extra-Large MIMO Arrays

We propose a decentralized receiver for extra-large multiple-input multiple-output (XL-MIMO) arrays. Our method operates with no central processing unit (CPU) and all the signal detection tasks are done in distributed nodes. We exploit a combined message-passing framework to design an uncoordinated detection scheme that overcomes three major challenges in the XL-MIMO systems: computational complexity, scalability and non-stationarities in user energy distribution. Our numerical evaluations show a significant performance improvement compared to benchmark distributed methods while operating very close to the centralized receivers.

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arXiv2021

Quasi-Distributed Antenna Selection for Spectral Efficiency Maximization in Subarray Switching XL-MIMO Systems

In this paper, we consider the downlink (DL) of a zero-forcing (ZF) precoded extra-large scale massive MIMO (XL-MIMO) system. The base-station (BS) operates with limited number of radio-frequency (RF) transceivers due to high cost, power consumption and interconnection bandwidth associated to the fully digital implementation. The BS, which is implemented with a subarray switching architecture, selects groups of active antennas inside each subarray to transmit the DL signal. This work proposes efficient resource allocation (RA) procedures to perform joint antenna selection (AS) and power allocation (PA) to maximize the DL spectral efficiency (SE) of an XL-MIMO system operating under different loading settings. Two metaheuristic RA procedures based on the genetic algorithm (GA) are assessed and compared in terms of performance, coordination data size and computational complexity. One algorithm is based on a quasi-distributed methodology while the other is based on the conventional centralized processing. Numerical results demonstrate that the quasi-distributed GA-based procedure results in a suitable trade-off between performance, complexity and exchanged coordination data. At the same time, it outperforms the centralized procedures with appropriate system operation settings.

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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

Distributed Receivers for Extra-Large Scale MIMO Arrays: A Message Passing Approach

We study the design of receivers in extra-large scale MIMO (XL-MIMO) systems, i.e. systems in which the base station is equipped with an antenna array of extremely large dimensions. While XL-MIMO can significantly increase the system's spectral efficiency, they present two important challenges. One is the increased computational cost of the multi-antenna processing. The second is the variations of user energy distribution over the antenna elements and therefore spatial non-stationarities in these energy distributions. Such non-stationarities limit the performance of the system. In this paper, we propose a distributed receiver for such an XL-MIMO system that can address both challenges. Based on variational message passing (VMP), We propose a set of receiver options providing a range of complexity-performance characteristics to adapt to different requirements. Furthermore, we distribute the processing into local processing units (LPU), that can perform most of the complex processing in parallel, before sharing their outcome with a central processing unit (CPU). Our designs are specifically tailored to exploit the spatial non-stationarities and require lower computations than linear receivers such as zero-forcing. Our simulation study, performed with a channel model accounting for the special characteristics of XL-MIMO channels, confirms the superior performance of our proposals compared to the state of the art methods.

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arXiv2020

Deep Learning Based Spatial User Mapping on Extra Large MIMO Arrays

In an extra-large scale MIMO (XL-MIMO) system, the antenna arrays have a large physical size that goes beyond the dimensions in traditional MIMO systems. Because of this large dimensionality, the optimization of an XL-MIMO system leads to solutions with prohibitive complexity when relying on conventional optimization tools. In this paper, we propose a design based on machine learning for the downlink of a multi-user setting with linear pre-processing, where the goal is to select a limited mapping area per user, i.e. a small portion of the array that contains the beamforming energy to the user. We refer to this selection as spatial user mapping (SUM). Our solution relies on learning using deep convolutional neural networks with a distributed architecture that is built to manage the large system dimension. This architecture contains one network per user where all the networks work in parallel and exploit specific non-stationary properties of the channels along the array. Our results show that, once the parallel networks are trained, they provide the optimal SUM solution in more than $80\%$ of the instances, resulting in a negligible sum-rate loss compared to a system using the optimal SUM solution while providing an insightful approach to rethink these kinds of problems that have no closed-form solution.

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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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arXiv2019

A Message Passing Based Receiver for Extra-Large Scale MIMO

We consider a massive MIMO system where the array at the access point reaches a dimension that is much larger than the array in current systems. Transitioning to an extremely large dimension and hence large number of antennas implies a need to scale up the multi-antenna processing while maintaining a reasonable computational complexity. In this paper, we study the receiver of such an extra-large scale MIMO (XL-MIMO) system. We propose to base the reception on Variational Message Passing (VMP). The motivation is that the complexity of VMP scales (almost) linearly with the number of antennas and number of users, hence enabling low-complexity reception in crowd scenarios. Furthermore, VMP adapts to the non-stationarities of the MIMO channel that appear due to the large dimension of the array. Through numerical results, we show significant performance improvement and computational complexity reduction compared to a zero-forcing receiver.

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arXiv2019

Non-Stationarities in Extra-Large Scale Massive MIMO

Massive MIMO, a key technology for increasing area spectral efficiency in cellular systems, was developed assuming moderately sized apertures. In this paper, we argue that massive MIMO systems behave differently in large-scale regimes due to spatial non-stationarity. In the large-scale regime, with arrays of around fifty wavelengths, the terminals see the whole array but non-stationarities occur because different regions of the array see different propagation paths. At even larger dimensions, which we call the extra-large scale regime, terminals see a portion of the array and inside the first type of non-stationarities might occur. We show that the non-stationarity properties of the massive MIMO channel changes several important MIMO design aspects. In simulations, we demonstrate how non-stationarity is a curse when neglected but a blessing when embraced in terms of computational load and multi-user transceiver design.

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arXiv2018

Extremely Large Aperture Massive MIMO: Low Complexity Receiver Architectures

This paper focuses on new communication paradigms arising in massive multiple-input-multiple-output systems where the antenna array at the base station is of extremely large dimension (xMaMIMO). Due to the extreme dimension of the array, xMaMIMO is characterized by spatial non-stationary field properties along the array; this calls for a multi-antenna transceiver design that is adapted to the array dimension but also its non-stationary properties. We address implementation aspects of xMaMIMO, with computational efficiency as our primary objective. To reduce the computational burden of centralized schemes, we distribute the processing into smaller, disjoint subarrays. Then, we consider several low-complexity data detection algorithms as candidates for uplink communication in crowded xMaMIMO systems. Drawing inspiration from coded random access, one of the main contributions of the paper is the design of low complexity scheme that exploits the non-stationary nature of xMaMIMO systems and where the data processing is decentralized. We evaluate the bit-error-rate performance of the transceivers in crowded xMaMIMO scenarios. The results confirm their practical potential.

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