Search

In this work, we consider a Federated Edge Learning (FEEL) system where training data are randomly generated over time at a set of distributed edge devices with long-term energy constraints. Due to limited communication resources and latency requirements, only a subset of devices is scheduled for participating in the local training process in every iteration. We formulate a stochastic network optimization problem for designing a dynamic scheduling policy that maximizes the time-average data importance from scheduled user sets subject to energy consumption and latency constraints. Our proposed algorithm based on the Lyapunov optimization framework outperforms alternative methods without considering time-varying data importance, especially when the generation of training data shows strong temporal correlation., Funding Agencies|ELLIIT; Knut and Alice Wallenberg (KAW) Foundation; Zenith

The number of wireless devices is drastically increasing, resulting in many devices contending for radio resources. In this work, we present an algorithm to detect active devices for unsourced random access, i.e., the devices are uncoordinated. The devices use a unique, but non-orthogonal preamble, known to the network, prior to sending the payload data. They do not employ any carrier sensing technique and blindly transmit the preamble and data. To detect the active users, we exploit partial channel state information (CSI), which could have been obtained through a previous channel estimate. For static devices, e.g., Internet of Things nodes, it is shown that CSI is less time-variant than assumed in many theoretical works. The presented iterative algorithm uses a maximum likelihood approach to estimate both the activity and a potential phase offset of each known device. The convergence of the proposed algorithm is evaluated. The performance in terms of probability of miss detection and false alarm is assessed for different qualities of partial CSI and different signal-to-noise ratio., Funding Agencies|European Union [101013425]

Positioning with high precision and reliability can be provided by 5G cellular networks in environments where satellite positioning is not available or reliable. The accuracy that can be achieved by classical methods like triangulation and trilateration however degrades significantly under non line of sight (NLOS) conditions. The problem can be mitigated with increasingly dense deployments of network transmission and reception points (TRPs), but that is both impractical and costly. As an alternative, this study investigates if multipath propagation of radio signals can be exploited to improve positioning accuracy and reduce the necessary deployment density. With 3GPP Rel. 17 new signaling support has been introduced to report the propagation delay, corresponding to the length, of multiple paths between the user equipment (UE) and a network TRP. The length of a multipath can, in combination with a partially known map of the environment, give additional information about the UE position. In this study we develop multipath-assisted tracking algorithms and evaluate their performances in realistic simulations using 3GPP standardized positioning reference signals and measurements in an indoor factory environment. Our evaluations show that multipath-assisted algorithms can achieve an accuracy below 0.9 m in 90% of the cases, which is more than tenfold better than a conventional LOS based algorithm. Moreover, one algorithm variant also shows an ability to track a UE using very few TRPs.

It is well known that GNSS receivers are vulnerable to jamming and spoofing attacks, and numerous such incidents have been reported in the last decade all over the world. The notion of participatory sensing, or crowdsensing, is that a large ensemble of voluntary contributors provides measurements, rather than relying on a dedicated sensing infrastructure. The participatory sensing network under consideration in this work is based on GNSS receivers embedded in, for example, mobile phones. The provided measurements refer to the receiver-reported carrier-to-noise-density ratio (C/N-0) estimates or automatic gain control (AGC) values. In this work, we exploit C/N-0 measurements to locate a GNSS jammer, using multiple receivers in a crowdsourcing manner. We extend a previous jammer position estimator by only including data that is received during parts of the sensing period where jamming is detected by the sensor. In addition, we perform hardware testing for verification and evaluation of the proposed and compared state-of-the-art algorithms. Evaluations are performed using a Samsung S20+ mobile phone as participatory sensor and a Spirent GSS9000 GNSS simulator to generate GNSS and jamming signals. The proposed algorithm is shown to work well when using C/N-0 measurements and outperform the alternative algorithms in the evaluated scenarios, producing a median error of 50 meters when the pathloss exponent is 2. With higher pathloss exponents the error gets higher. The AGC output from the phone was too noisy and needs further processing to be useful for position estimation., Funding Agencies|Security Link; SSF-SURPRISE

Previous works on cell-free massive MIMO mostly consider physical-layer and fronthaul transport aspects. How to deploy cell-free massive MIMO functionality in a practical wireless system is an open problem. This paper proposes a new cell-free architecture that can be implemented on top of a virtualized cloud radio access network (V-CRAN). We aim to minimize the end-to-end power consumption by jointly considering the radio, optical fronthaul, virtualized cloud processing resources, and spectral efficiency requirements of the user equipments. The considered optimization problem is cast in a mixed binary second-order cone programming form and, thus, the global optimum can be found using a branch-and-bound algorithm. The optimal power-efficient solution of our proposed cell-free system is compared with conventional small-cell implemented using V-CRAN, to determine the benefits of cell-free networking. The numerical results demonstrate that cell-free massive MIMO increases the maximum rate substantially, which can be provided with almost the same energy per bit. We show that it is more power-efficient to activate cell-free massive MIMO already at low spectral efficiencies (above 1 bit/s/Hz)., Funding Agencies|CELTIC -NEXT project AI4Green; Vinnova, Swedish Innovation Agency

Backscatter communication (BSC) is a promising solution for Internet-of-Things (IoT) connections due to its lowcomplexity, low-cost, and energy-efficient solution for sensors. There are several network infrastructure setups that can be used for BSC with IoT nodes/passive devices. One of them is a bistatic setup where there is a need for high dynamic range and high-resolution analog-to-digital converters at the reader side. In this paper, we investigate a bistatic BSC setup with multiple antennas. We propose a novel algorithm to suppress direct link interference between the carrier emitter (CE) and the reader using beamforming into the nullspace of the CEreader direct link to decrease the dynamic range of the system and increase the detection performance of the backscatter device (BSD). Further, we derive a Neyman-Pearson (NP) test and an exact closed-form expression for its performance in the detection of the BSD. Finally, simulation results show that the dynamic range of the system is significantly decreased and the detection performance of the BSD is increased by the proposed algorithm compared to a system not using beamforming in the CE, which could then be used in a host of different practical fields such as agriculture, transportation, factories, hospitals, smart cities, and smart homes., Funding Agencies|European Union [101013425]; ELLIIT; KAW foundation

An Intelligent Reflecting Surface (IRS) is an emerging technology for improving the data rate over wireless channels by controlling the underlying channel. In this paper, we describe a novel solution for IRS configuration to maximize the data rate over wideband channels. The optimization is obtained by online training of a deep generative neural network. Inspired by related works in image processing, this network is randomly initialized and acts as a regularization term for the optimization process since the structure of the generator is sufficient to capture a great deal of IRS statistics prior to any learning. In contrast to recent deep learning techniques for IRS configuration, the proposed technique does not require an offline training stage and can adapt quickly to any environment. Compared to the previous state-of-the-art algorithm, the proposed method is significantly faster and obtains IRS configurations that achieve higher data transmission rates.

Radio frequency (RF) wireless power transfer (WPT) is a promising technology for 6G use cases. It enables a massive yet sustainable deployment of batteryless energy neutral (EN) devices at an unprecedented scale. Recent research on 6G is exploring high operating frequencies up to the THz spectrum, where antenna arrays with large apertures are capable of forming narrow, "laser-like" beams. At sub-10 GHz frequencies, physically large antenna arrays are considered that are operating in the array near field. Transmitting spherical wavefronts, power can be focused to a focal point rather than a beam, which allows for efficient and radiation-safe WPT. We formulate a multipath channel model comprising specular components and diffuse scattering to find the WPT power budget in a realistic indoor scenario. Specular components can be predicted by means of a geometric model. This is used to transmit power via multiple beams simultaneously, increasing the available power budget and expanding the initial access distance. We show that exploiting this "beam diversity" reduces the required fading margin for the initial access to EN devices., Funding Agencies|European Unions Horizon 2020 research and innovation programme [101013425]

In this paper, we examine the energy consumption of a user equipment (UE) when it transmits a finite-sized data packet. The receiving base station (BS) controls a reconfigurable intelligent surface (RIS) that can be utilized to improve the channel conditions, if additional pilot signals are transmitted to configure the RIS. We derive a formula for the energy consumption taking both the pilot and data transmission powers into account. By dividing the RIS into subarrays consisting of multiple RIS elements using the same reflection coefficient, the pilot overhead can be tuned to minimize the energy consumption while maintaining parts of the aperture gain. Our analytical results show that there exists an energy-minimizing subarray size. For small data blocks and when the channel conditions between the BS and UE are favorable compared to the path to the RIS, the energy consumption is minimized using large subarrays. When the channel conditions to the RIS are better and the data blocks are large, it is preferable to use fewer elements per subarray and potentially configure the elements individually., Funding Agencies|Swedish Foundation for Strategic Research [FFL18-0277]

In this paper, we consider privacy aspects of wireless federated learning (FL) with Over-the-Air (OtA) transmission of gradient updates from multiple users/agents to an edge server. OtA FL enables the users to transmit their updates simultaneously with linear processing techniques, which improves resource efficiency. However, this setting is vulnerable to privacy leakage since an adversary node can hear directly the uncoded message. Traditional perturbation-based methods provide privacy protection while sacrificing the training accuracy due to the reduced signal-to-noise ratio. In this work, we aim at minimizing privacy leakage to the adversary and the degradation of model accuracy at the edge server at the same time. More explicitly, spatially correlated perturbations are added to the gradient vectors at the users before transmission. Using the zero-sum property of the correlated perturbations, the side effect of the added perturbation on the aggregated gradients at the edge server can be minimized In the meanwhile, the added perturbation will not be canceled out at the adversary, which prevents privacy leakage. Theoretical analysis of the perturbation covariance matrix, differential privacy, and model convergence is provided, based on which an optimization problem is formulated to jointly design the covariance matrix and the power scaling factor to balance between privacy protection and convergence performance. Simulation results validate the correlated perturbation approach can provide strong defense ability while guaranteeing high learning accuracy., Funding Agencies|Security Link; ELLIIT; KAW foundation

GNSS receivers are vulnerable to jamming and spoofing attacks, and numerous such incidents have been reported worldwide in the last decade. It is important to detect attacks fast and localize attackers, which can be hard if not impossible without dedicated sensing infrastructure. The notion of participatory sensing, or crowdsensing, is that a large ensemble of voluntary contributors provides the measurements, rather than relying on dedicated sensing infrastructure. This work considers embedded GNSS receivers to provide measurements for participatory jamming detection and localization. Specifically, this work proposes a novel jamming localization algorithm, based on participatory sensing, that exploits AGC and C/N-0 estimates from commercial GNSS receivers. The proposed algorithm does not require knowledge of the jamming power nor of the channels, but automatically estimates all parameters. The algorithm is shown to outperform similar state-of-the-art localization algorithms in relevant scenarios., Funding Agencies|Security Link

RadioWeaves, in which distributed antennas with integrated radio and compute resources serve a large number of users, is envisioned to provide high data rates in next-generation wireless systems. In this paper, we develop a physical layer abstraction model to evaluate the performance of different RadioWeaves deployment scenarios. This model helps speed up system-level simulators of the RadioWeaves and is made up of two blocks. The first block generates a vector of signalto-interference-plus-noise ratios (SINRs) corresponding to each coherence block, and the second block predicts the packet error rate corresponding to the SINRs generated. The vector of SINRs generated depends on different parameters such as the number of users, user locations, antenna configurations, and precoders. We have also considered different antenna gain patterns, such as omni-directional and directional microstrip patch antennas. Our model exploits the benefits of exponential effective SINR mapping (EESM). We study the robustness and accuracy of the EESM for RadioWeaves., Funding Agencies|European Union [101013425]

Base stations in 5G and beyond use advanced antenna systems (AASs), where multiple passive antenna elements and radio units are integrated into a single box. A critical bottleneck of such a system is the digital fronthaul between the AAS and baseband unit (BBU), which has limited capacity. In this paper, we study an AAS used for precoded downlink transmission over a multi-user multiple-input multiple-output (MU-MIMO) channel. First, we present the baseline quantization-unaware precoding scheme created when a precoder is computed at the BBU and then quantized to be sent over the fronthaul. We propose a new precoding design that is aware of the fronthaul quantization. We formulate an optimization problem to minimize the mean squared error at the receiver side. We rewrite the problem to utilize mixed-integer programming to solve it. The numerical results manifest that our proposed precoding greatly outperforms quantization-unaware precoding in terms of sum rate., Funding Agencies|Knut and Alice Wallenberg Foundation

Wireless power transfer (WPT) is an alternative technology to conventional batteries for powering Internet of things (IoT) devices. WPT is especially beneficial in situations when battery replacement is infeasible or expensive. It can also reduce battery-related e-waste. In this paper, we analyze the limits of adopting WPT technology for remote powering of IoT devices. We assume that an IoT device periodically harvests energy from a base station (BS) and transmits a data packet related to the sensor measurement under shadow fading channel conditions. Our goal is to characterize the epsilon-coverage range, where epsilon is the probability of the coverage. Our analysis shows a tradeoff between the coverage range and the rate of sensor measurements, where the maximal epsilon-coverage range is achieved as the sensor measurement rate approaches zero. We demonstrate that the weighted sum of the sleep power consumption and the harvesting sensitivity power of an IoT device limits the maximal e-coverage range. Beyond that range, the IoT device cannot harvest enough energy to operate. The desired rate of the sensor measurements also significantly impacts the epsilon-coverage range. Our results suggest that for an IoT device designed using current technology, the maximal 0.95-coverage range is in the order of 120 m. When high measurement rates are required, the coverage range drops to 50-100 m. Compared to battery-powered IoT devices, WPT is well-suited for medium-range applications plus when battery replacement is costly.

This paper introduces realizations of a reconfigurable finite-impulse-response (FIR) filter for simultaneous equalization and lowpass filtering. The proposed structures employ properties of both a variable bandwidth (VBW) filter and a variable equalizer (VE) with an adjustable coefficient using the Farrow structure, therefore they consist of weighted combinations of fixed subfilters. Design procedures using minimax optimization technique are provided. The paper includes a design example and complexity comparisons between the proposed structures of the reconfigurable lowpass equalizer (RLPE) and a common approach of using a regular FIR equalization filter requiring online redesign.

We discuss possible definitions of structural balance conditions in a network with preference orderings as node attributes. The main result is that for the case with three alternatives (A, B, C) we reduce the (3!)(3) = 216 possible configurations of triangles to 10 equivalence classes, and use these as measures of balance of a triangle towards possible extensions of structural balance theory. Moreover, we derive a general formula for the number of equivalent classes for preferences on n alternatives. Finally, we analyze a real-world data set and compare its empirical distribution of triangle equivalence classes to a null hypothesis in which preferences are randomly assigned to the nodes., Funding Agencies|ELLIIT; KAW foundation

Deep learning (DL) is a powerful technique for many real-time applications, but it is vulnerable to adversarial attacks. Herein, we consider DL-based modulation classification, with the objective to create DL models that are robust against attacks. Specifically, we introduce three defense techniques: i) randomized smoothing, ii) hybrid projected gradient descent adversarial training, and iii) fast adversarial training, and evaluate them under both white-box (WB) and black-box (BB) attacks. We show that the proposed fast adversarial training is more robust and computationally efficient than the other techniques, and can create models that are extremely robust to practical (BB) attacks., Funding Agencies|Security-Link; Start-Up Research Grant of IIT Guwahati

How to manage the interference introduced by the enormous wireless devices is a crucial issue to address in the prospective sixth-generation (6G) communications. The treating interference as noise (TIN) optimality conditions are commonly used for interference management and thus attract significant interest in existing wireless systems. Cell-free massive multiple-input multiple-output (CF mMIMO) is a promising technology in 6G that exhibits high system throughput and excellent interference management by exploiting a large number of access points (APs) to serve the users collaboratively. In this paper, we take the first step on studying TIN in CF mMIMO systems from a stochastic geometry perspective by investigating the probability that the TIN conditions hold with spatially distributed network nodes. We propose a novel analytical framework for TIN in a CF mMIMO system with both Binomial Point Process (BPP) and Poisson Point Process (PPP) approximations. We derive the probability that the TIN conditions hold in close form using the PPP approximation. Numerical results validate our derived expressions and illustrate the impact of various system parameters on the probability that the TIN conditions hold., Funding Agencies|Fundamental Research Funds for the Central Universities [2021YJS001]; National Key R&D Program of China [2020YFB1807201]; National Natural Science Foundation of China [61971027, 61961130391]; Beijing Natural Science Foundation [L202013]; Natural Science Foundation of Jiangsu Province [BK20212002]; Royal Society Newton Advanced Fellowship [NA191006]; Frontiers Science Center for Smart Highspeed Railway System; Center for Industrial Information Technology (CENIIT); Excellence Center at Linkoping-Lund in Information Technology (ELLIIT)

In this paper, we consider a cellular-based Internet of things (IoT) network consisting of IoT devices that can communicate directly with each other in a device-to-device (D2D) fashion as well as send real-time status updates about some underlying physical processes observed by them. We assume that such real-time applications are supported by cellular networks where cellular base stations (BSs) collect status updates over time from a subset of the IoT devices in their vicinity. We characterize two performance metrics: i) the network throughput which quantifies the performance of D2D communications, and ii) the Age of Information which quantifies the performance of the real-time IoT-enabled applications. Concrete analytical results are derived using stochastic geometry by modeling the locations of IoT devices as a bipolar Poisson Point Process (PPP) and that of the BSs as another Independent PPP. Our results provide useful design guidelines on the efficient deployment of future IoT networks that will jointly support D2D communications and several cellular network-enabled real-time applications., Funding Agencies|U.S. NSF [CPS-1739642]

Deep learning (DL) is becoming popular as a new tool for many applications in wireless communication systems. However, for many classification tasks (e.g., modulation classification) it has been shown that DL-based wireless systems are susceptible to adversarial examples; adversarial examples are well-crafted malicious inputs to the neural network (NN) with the objective to cause erroneous outputs. In this paper, we extend this to regression problems and show that adversarial attacks can break DL-based power allocation in the downlink of a massive multiple-input-multiple-output (maMIMO) network. Specifically, we extend the fast gradient sign method (FGSM), momentum iterative FGSM, and projected gradient descent adversarial attacks in the context of power allocation in a maMIMO system. We benchmark the performance of these attacks and show that with a small perturbation in the input of the NN, the white-box attacks can result in infeasible solutions up to 86%. Furthermore, we investigate the performance of black-box attacks. All the evaluations conducted in this work are based on an open dataset and NN models, which are publicly available.

The knowledge of channel covariance matrices is of paramount importance to the estimation of instantaneous channels and the design of beamforming vectors in multi-antenna systems. In practice, an abrupt change in channel covariance matrices may occur due to the change in the environment and the user location. Although several works have proposed efficient algorithms to estimate the channel covariance matrices after any change occurs, how to detect such a change accurately and quickly is still an open problem in the literature. In this paper, we focus on channel covariance change detection between a multiantenna base station (BS) and a single-antenna user equipment (UE). To provide theoretical performance limit, we first propose a genie-aided change detector based on the log-likelihood ratio (LLR) test assuming the channel covariance matrix after change is known, and characterize the corresponding missed detection and false alarm probabilities. Then, this paper considers the practical case where the channel covariance matrix after change is unknown. The maximum likelihood (ML) estimation technique is used to predict the covariance matrix based on the received pilot signals over a certain number of coherence blocks, building upon which the LLR-based change detector is employed. Numerical results show that our proposed scheme can detect the change with low error probability even when the number of channel samples is small such that the estimation of the covariance matrix is not that accurate. This result verifies the possibility to detect the channel covariance change both accurately and quickly in practice.

With its privacy preservation and communication efficiency, federated learning (FL) has emerged as a learning framework that suits beyond SG and towards 6G systems. This work looks into a future scenario in which there are multiple groups with different learning purposes and participating in different FL processes. We give energy-efficient solutions to demonstrate that this scenario can be realistic. First, to ensure a stable operation of multiple FL processes over wireless channels, we propose to use a massive multiple-input multiple-output network to support the local and global FL training updates, and let the iterations of these FL processes be executed within the same large-scale coherence time. Then, we develop asynchronous and synchronous transmission protocols where these iterations are asynchronously and synchronously executed, respectively, using the downlink unicasting and conventional uplink transmission schemes. Zero-forcing processing is utilized for both uplink and downlink transmissions. Finally, we propose an algorithm that optimally allocates power and computation resources to save energy at both base station and user sides, while guaranteeing a given maximum execution time threshold of each FL iteration. Compared to the baseline schemes, the proposed algorithm significantly reduces the energy consumption, especially when the number of base station antennas is large., Funding Agencies|U. K. Research and Innovation Future Leaders Fellowships [MR/S017666/1]; ELLIIT; Knut and Alice Wallenberg Foundation; Federation University Australia [RGS21-8]

Reconfigurable intelligent surfaces (RISs) have recently received widespread attention in the field of wireless communication. An RIS can be controlled to reflect incident waves from the transmitter towards the receiver; a feature that is believed to fundamentally contribute to beyond SG wireless technology. The typical RIS consists of entirely passive elements, which requires the high-dimensional channel estimation to be done elsewhere. Therefore, in this paper, we present a semi-passive large-scale RIS architecture equipped with only a small fraction of simplified receiver units with only 1-bit quantization. Based on this architecture, we first propose an alternating direction method of multipliers (ADMM)-based approach to recover the training signals at the passive RIS elements, We then obtain the global channel by combining a channel sparsification step with the generalized approximate message passing (GAMP) algorithm. Our proposed scheme exploits both the sparsity and low-rankness properties of the channel in the joint spatial-frequency domain of a wideband mmWave multiple-input-multiple-output (MIMO) communication system. Simulation results show that the proposed algorithm can significantly reduce the pilot signaling needed for accurate channel estimation and outperform previous methods, even with fewer receiver units.

Classification between different activities in an indoor environment using wireless signals is an emerging technology for various applications, including intrusion detection, patient care, and smart home. Researchers have shown different methods to classify activities and their potential benefits by utilizing WiFi signals. In this paper, we analyze classification of moving objects by employing machine learning on real data from a massive multi-input-multi-output (MIMO) system in an indoor environment. We conduct measurements for different activities in both line-of-sight and non line-of-sight scenarios with a massive MIMO testbed operating at 3.7 GHz. We propose algorithms to exploit amplitude and phase-based features classification task. For the considered setup, we benchmark the classification performance and show that we can achieve up to 98% accuracy using real massive MIMO data, even with a small number of experiments. Furthermore, we demonstrate the gain in performance results with a massive MIMO system as compared with that of a limited number of antennas such as in WiFi devices., Funding Agencies|ELLIIT, Security-Link; Ericsson ABEricsson

Massive MIMO (Multiple Input Multiple Output) has demonstrated as a potential candidate for 5G-and-beyond wireless networks. Instead of using Gaussian signals as most of the previous works, this paper makes a novel contribution by investigating the transmission quality of image data by utilizing the Massive MIMO technology. We first construct a framework to decode the image signal from the noisy received data in the uplink Massive MIMO transmission by utilizing the alternating direction method of multipliers (ADMM) approach. Then, a low-pass filter is exploited to enhance the efficiency of the remaining noise and artifacts reduction in the recovered image. Numerical results demonstrate the necessity of a post-filtering process in enhancing the quality of image recovery., Funding Agencies|Vietnam National Foundation for Science and Technology Development (NAFOSTED)National Foundation for Science & Technology Development (NAFOSTED) [102.01-2019.07]

The performance of both primary and secondary networks in underlay cognitive radio networks (CRNs) with the help of incremental amplify and forward (IAF) relaying under imperfect channel state information (CSI) are investigated. Particularly, the interference probability at primary networks denoted by IP as well as the outage probability at secondary networks denoted by OP, are computed in the closed-form expressions. Our findings show that the impact of the imperfect CSI on the performance of IP is non-negligible. To tackle the high value of IP, reducing the transmit power at the secondary transmitters is a proper solution, it, however, will also increase the OP at the secondary networks. Thus, a simple power control coefficient is proposed to compromise the performance between two networks. Finally, Monte-Carlo simulations are provided to verify the accuracy of the proposed mathematical frameworks., Funding Agencies|Vietnam National Foundation for Science and Technology Development (NAFOSTED)National Foundation for Science & Technology Development (NAFOSTED) [102.01-2019.07]

Reconfigurable intelligent surfaces (RISs) have attracted great attention as a potential beyond SG technology. These surfaces consist of many passive elements of metamaterials whose impedance can be controllable to change the phase, amplitude, or other characteristics of wireless signals impinging on them. Channel estimation is a critical task when it comes to the control of a large RIS when having a channel with a large number of multipath components. In this paper, we propose a novel channel estimation scheme that exploits spatial correlation characteristics at both the massive multiple-input multiple-output (MIMO) base station and the planar RISs, and other statistical characteristics of multi-specular fading in a mobile environment. Moreover, a novel heuristic for phase-shift selection at the RISs is developed, inspired by signal processing methods that are effective in conventional massive MIMO. Simulation results demonstrate that the proposed uplink RIS-aided framework improves the spectral efficiency of the cell-edge mobile users substantially in comparison to a conventional single-cell massive MIMO system., Funding Agencies|Swedish Foundation for Strategic Research [FFL18-0277]

Caching popular contents at a large number of access points and edge-clouds is a promising solution to alleviate the increasing backhaul congestion in beyond fifth-generation (B5G) networks. By integrating with cell-free massive multiple-input multiple-output (CF mMIMO), wireless caching can harness their combined virtues, i.e., almost uniform service quality, strong macro-diversity, and reduction of the data traffic from the core network. In this paper, we consider an offline cache-aided scenario with two caching strategies to minimize the total energy consumption (TEC), which are evaluated from the cache hit probability (CHP). The TEC minimization is showed to be NP-complete and, hence, dealt with a proposed greedy algorithm. An adaptive power control policy is proposed to reduce the TEC. We compare CF mMIMO with small cells in terms of the successful content delivery probability (SCDP) and TEC, respectively. The numerical results show that CF mMIMO can offer a much more uniform service, significantly higher SCDP, and lower average TEC when compared to than SC.

This paper considers signal detection in massive multiple-input multiple-output (MIMO) systems with general additive hardware impairments and one-bit quantization. First, we present the quantization-unaware and Bussgang decomposition-based linear receivers by generalizing them for the considered hardware impairment model. We propose an optimization problem to estimate the uplink data signals by choosing a suitable cost function that treats the unquantized received signal at the base station as the variable. We exploit the additional structure of the one-bit quantization and signal modulation by including proper constraints. To solve the non-convex quadratically-constrained quadratic programming (QCQP) problem, we propose an ADMM-based algorithm with closed-form update equations. Then, we replace the harsh projectors in the updates with their soft versions to improve the detection performance. We show that the proposed ADMM-based algorithm outperforms the state-of-the-art linear receivers significantly in terms of bit error rate (BER) and the performance gain increases with the number of antennas and users., Funding Agencies|ELLIIT; Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

In this work, we consider a system where external requests arrive for status updates of a remote source, which is monitored by an energy harvesting (EH) sensor. The requests are placed in an aggregator that has direct communication with the sensor and is also equipped with storage space to cache a previously generated status update. A probabilistic model is considered to determine whether a new request will be served with a fresh update from the EH sensor or with a cached update from the aggregator. In the first case, the fresh update will replace the cached one in the aggregator. Assuming that the energy arrivals at the sensor can be modeled by a Bernoulli process, we characterize the average Age of Information (AoI) of the source seen at the aggregator as a function of the external request probability, the battery charging probability, and the probability that a fresh update will be generated by the EH sensor. Our numerical results reveal some insights about the role of caching in EH-based status updating systems., Funding Agencies|Center for Industrial Information Technology (CENIIT); ELLIIT; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research; Swedish Research Council (VR)Swedish Research Council; Infotech Oulu; Academy of FinlandAcademy of FinlandEuropean Commission [323698]; Academy of Finland 6Genesis Flagship [318927]

Cell-free Massive MIMO (mMIMO) is envisaged to be a next-generation technology beyond 5G with its high spectral efficiency and superior spatial diversity as compared to that of conventional MIMO technology. The main principle is that many distributed access points (APs) cooperate to simultaneously serve all the users within the network without creating cell boundaries. This paper considers the uplink of a cell-free mMIMO system utilizing the radio stripe network architecture. We propose a novel sequential processing algorithm with normalized linear minimum mean square error (N-LMMSE) combining at every AP. This algorithm enables interference suppression in cell-free mMIMO while keeping the cost and front-haul requirements low. The spectral efficiency of the proposed algorithm is computed and analyzed. We conclude that it provides an attractive trade-off between low front-haul requirements and high spectral efficiency., Funding Agencies|Swedish Research Council (VR)Swedish Research Council; ELLIIT

n/a

Intelligent reflecting surfaces (IRSs), consisting of reconfigurable metamaterials, have recently attracted attention as a promising cost-effective technology that can bring new features to wireless communications. These surfaces can be used to partially control the propagation environment and can potentially provide a power gain that is proportional to the square of the number of IRS elements when configured in a proper way. However, the configuration of the local phase matrix at the IRSs can be quite a challenging task since they are purposely designed to not have any active components, therefore, they are not able to process any pilot signal. In addition, a large number of elements at the IRS may create a huge training overhead. In this paper, we present a deep learning (DL) approach for phase reconfiguration at an IRS in order to learn and make use of the local propagation environment. The proposed method uses the received pilot signals reflected through the IRS to train the deep feedforward network. The performance of the proposed approach is evaluated and the numerical results are presented.

Cognitive radio (CR) is an effective solution to increase the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share the spectrum with primary users (PUs). On the other hand, intelligent reflecting surface (IRS) is a promising approach to enhance the energy efficiency (EE) of wireless communication systems through passively reconfiguring the channel environments. In this paper, we propose an IRS enhanced downlink multiple-input single-output (MISO) CR systems to improve both SE and EE, where a single SU coexists with a primary network with multiple primary user receivers (PU-RXs). Specifically, for the MISO-CR system, we maximize the achievable rate of SU subject to a total power constraint on an SU transmitter (SU-TX) and an interference temperature (IT) constraint on PU-RXs, by jointly optimizing the beamforming vector at SU-TX and the phase shifts at the IRS. Furthermore, both perfect channel state information (CSI) and imperfect CSI are considered in the optimization. Numerical results demonstrate that the IRS can significantly improve the achievable rate of SU-RX under both the perfect and imperfect CSI conditions., Funding Agencies|National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61631005, U1801261]; National Key RD Program of China [2018YFB1801105]; 111 ProjectMinistry of Education, China - 111 Project [B20064]; NRF - Korea government (MSIT) [2018R1A4A1023826]; Central Universities Funding [ZYGX2019Z022]

This paper considers the large-scale fading precoding design for mitigating the pilot contamination in the downlink of multi-cell massive MIMO (multiple-input multiple-output) systems. Rician fading with spatially correlated channels are considered where the line-of-sight (LOS) components of the channels are randomly phase-shifted in each coherence block. The large-scale fading precoding weights are designed based on maximizing the product of the signal-to-interference-plus-noise ratios (SINRs) of the users, which provides a good balance between max-min fairness and sum rate maximization. The spectral efficiency (SE) is derived based on the scaled least squares (LS) estimates of the channels, which only utilize the despreaded pilot signals without any matrix inversion. Simulation results show that the two-layer large-scale fading precoding improves the SE of almost all users compared to the conventional single-layer precoding., Funding Agencies|ELLIIT; Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

This paper studies the uplink spectral efficiency (SE) achieved by two single-antenna user equipments (UEs) communicating with a Large Intelligent Surface (LIS), defined as a planar array consisting of N antennas that each has area A. The analysis is carried out with a deterministic line-of-sight propagation channel model that captures key fundamental aspects of the so-called geometric near-field of the array. Maximum ratio (MR) and minimum mean squared error (MMSE) combining schemes are considered. With both schemes, the signal and interference terms are numerically analyzed as a function of the position of the transmitting devices when the width/height L = root NA of the square-shaped array grows large. The results show that an exact near-field channel model is needed to evaluate the SE whenever the distance of transmitting UEs is comparable with the LIS dimensions. It is shown that, if L grows, the UEs are eventually in the geometric near-field and the interference does not vanish. MMSE outperforms MR for an LIS of practically large size.

In this paper, two first-order compensation strategies for timing mismatch in two-channel time-interleaved analog-to-digital converters (TIADCs) are analyzed, and expressions for the spurious-free dynamic range (SFDR) after compensation are derived. The derived expressions reveal that the strategy where both channels are compensated to match each other, using half the value of the mismatch with different signs, achieves a substantially greater SFDR than the strategy where only one channel is compensated to match the other (reference) channel. This is because, after compensation, the remaining aliasing distortion is shown to be of third order in the former strategy whereas it is of second order in the latter. Simulations included demonstrate the validity of the derived expressions., Funding: National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61701509]; Science and Technology on Analog Integrated Circuit Laboratory [JCKY2019210C027]

In this paper, we analyze the uplink spectral efficiency (SE) of an arbitrary user in a single-cell massive multiple-input-multiple-output (MIMO) system in which there are jammers randomly distributed. We compare this SE with that of a user in a single-input-multiple-output (SIMO) system. We utilize two types of receivers that are widely used in massive MIMO literature: Maximum-ratio-combining (MRC) and zero-forcing (ZF). The jammers attack the base station (BS) during the training and data transmission phases. In order to estimate the channel vectors of the legitimate users, the BS uses either the linear minimum mean square error (LMMSE) estimator which requires information about the jamming power or the least squares (LS) estimator which does not require any knowledge about the jamming signals. We show that ZF gives higher SE than MRC, but interestingly the performance is unaffected by the choice of the estimators. Moreover, we derive the closed form signal-to-interference-noise ratio (SINR) for the MRC receiver when the jammers attack to the BS and based on this SINR expression we utilize power control algorithms that achieve max-min fairness and proportional fairness., Funding Agencies|ELLIIT; SURPRISE project - Swedish Foundation for Strategic Research (SSF); Security-Link

A potential showstopper for reciprocity-based beamforming is that the uplink SNR often is much smaller than the downlink SNR, making it hard to estimate channels on the uplink. We analyze this problem by considering a "grid-of-beams world" with a finite number of possible channel realizations. We assume that the terminal can accurately detect the channel and we propose a method of improving the channel detection from uplink pilots by designing a mapping between the channel and the pilots. We find a simple metric that is to be minimized to maximize performance. Further, we propose an algorithm that draws pilot sequences from a distribution aimed to minimize the metric. We see that we can come close to optimal performance, which requires long sequences, with significantly shorter sequences.

Despite the deleterious effect of hardware impairments (HWIs) on wireless systems, most prior works in cell-free (CF) massive multiple-input-multiple-output (mMIMO) systems have not accounted for their impact. In particular, the effect of phase noise (PN) has not been investigated at all in CF systems. Moreover, there is no work investigating HWIs in scalable CF (SCF) mMIMO systems, encountering the prohibitively demanding fronthaul requirements of large networks with many users. Hence, we derive the uplink spectral efficiency (SE) under HWIs with minimum mean-squared error (MMSE) combining in closed-form by means of the deterministic equivalent (DE) analysis. Notably, previous works, accounted for MMSE decoding, studied the corresponding SE only by means of simulations. Numerical results illustrate the performance loss due to HWIs and result in insightful conclusions.

The precoding in cell-free massive multiple-input multiple-output (MIMO) technology relies on accurate knowledge of channel responses between users (UEs) and access points (APs). Obtaining high-quality channel estimates in turn requires the path losses between pairs of UEs and APs to be known. These path losses may change rapidly especially in line-of-sight environments with moving blocking objects. A difficulty in the estimation of path losses is pilot contamination, that is, simultaneously transmitted pilots from different UEs that may add up destructively or constructively by chance, seriously affecting the estimation quality (and hence the eventual performance). A method for estimation of path losses, along with an accompanying pilot transmission scheme, is proposed that works for both Rayleigh fading and line-of-sight channels and that significantly improves performance over baseline state-of-the-art. The salient feature of the pilot transmission scheme is that pilots are structurally phase-rotated over different coherence blocks (according to a pre-determined function known to all parties), in order to create an effective statistical distribution of the received pilot signal that can be efficiently exploited by the proposed estimation algorithm., Funding Agencies|Swedish Research Council (VR)Swedish Research Council; ELLIIT

One main goal of 5G-and-beyond systems is to simultaneously serve many users, each having a requested spectral efficiency (SE), in an energy-efficient way. The network capacity cannot always satisfy all the SE requirements, for example, when some users have bad channel conditions, especially happening in a cellular topology, and therefore congestion can happen. By considering both the pilot and data powers in the uplink transmission as optimization variables, this paper formulates and solves an energy-efficiency problem for cellular Massive MIMO (Multiple Input Multiple Output) systems that can handle the congestion issue. New algorithms based on the alternating optimization approach are proposed to obtain a fixed-point solution. Numerical results manifest that the proposed algorithms can provide the demanded SEs to many users even when the congestion happens., Funding Agencies|ELLIIT; CENIIT; UK Research and Innovation Future Leaders Fellowships [MR/S017666/1]

In this work, we consider the uplink of a scalable cell-free massive MIMO system where the users are served only by a subset of access points (APs) in the network. The APs are physically grouped into predetermined "cell-centric clusters", which are connected to different cooperative central processing units (CPUs). Given the cooperative nature of the considered communications network, we assume that each user is associated with a "virtual cluster", that, in general, involves some APs belonging to different cell-centric clusters. Assuming the maximum-ratio-combining at the APs, we propose a user-association procedure aimed at the maximization of the sum-rate of the users in the system. The proposed procedure is based on the Hungarian Algorithm and exploits only the knowledge of the position of the APs in the network. Numerical results reveal that the performance of the proposed approach is not always better than the alternatives but it offers a considerably lower backhaul load with a negligible performance loss compared to full-cell free approaches., Funding Agencies|MIURMinistry of Education, Universities and Research (MIUR); MIUR PRIN 2017 ProjectMinistry of Education, Universities and Research (MIUR); Swedish research council (VR)Swedish Research Council; ELLIIT

This paper studies the transmit power optimization in a multi-cell massive multiple-input multiple-output (MIMO) system. To overcome the scalability issue of network-wide max-min fairness (NW-MMF), we propose a novel power control (PC) scheme. This scheme maximizes the geometric mean (GM) of the per-cell max-min spectral efficiency (SE). To solve this new optimization problem, we prove that it can be rewritten in a convex form and then solved using standard tools. To provide a fair comparison with the available utility functions in the literature, we solve the network-wide proportional fairness (NW-PE) PC as well. The NW-PE focuses on maximizing the sum SE, thereby ignoring fairness, but gives some extra attention to the weakest users. The simulation results highlight the benefits of our model which is balancing between NW-PE and NW-MMF., Funding Agencies|ELLIIT; European Unions Horizon 2020 research and innovation programme [641985]

Age of Information (AoI) is a newly appeared concept and metric to characterize the freshness of data. In this work, we study the delay and AoI in a multiple access channel (MAC) with two source nodes transmitting different types of data to a common destination. The first node is grid-connected and its data packets arrive in a bursty manner, and at each time slot it transmits one packet with some probability. Another energy harvesting (EH) sensor node generates a new status update with a certain probability whenever it is charged. We derive the delay of the grid-connected node and the AoI of the EH sensor as functions of different parameters in the system. The results show that the mutual interference has a non-trivial impact on the delay and age performance of the two nodes., Funding Agencies|ELLIIT; CENIIT; Swedish Foundation for Strategic ResearchSwedish Foundation for Strategic Research

In this work, a processing architecture for grant-free machine type communication based on compressive sensing is proposed. The architecture can be adapted for a number of parameters. An instantiation for 128 terminals and 96 antennas is implemented. Without memories it consumes 1.52 W and occupies and area of 5.1 mm(2) in a 28 nm SOI CMOS process. The implemented instance can process about 10k messages per second, each containing four bits.

Current outdoor mobile network infrastructure cannot support uplink intensive mobile applications such as connected vehicles that collect and upload large amount of real time data. Our investigation reveals that with maximum-ratio (MR) decoding, it is theoretically impossible to support such applications with cell-free Massive MIMO, and it requires a very large number of service antennas in single cell configuration, making it practically infeasible; but with zero-forcing (ZF) decoding, such applications can be easily supported by cell-free Massive MIMO with very moderate number of access points (APs), and it requires a lot more service antennas in single cell configuration. Via the newly derived SINR expressions for cell-free Massive MIMO with ZF decoding we show that uplink power control is unnecessary, and that with 10 MHz effective bandwidth for uplink data transmission, in urban and suburban morphologies, on the 2 GHz band, 90/km(2) and 32/km(2) single antenna APs are enough to support 18 autonomous vehicles respectively. In rural morphology, using 450 MHz band, only 2/km(2) single antenna APs is enough., Funding Agencies|Swedish Research Council (VR)Swedish Research Council; ELLIIT

Communication by joint signal processing from many distributed access points, called Cell-free Massive MIMO, is a potential beyond-5G network infrastructure. The aim of this paper is to provide the first comprehensive comparison with Cellular Massive MIMO. The uplink spectral efficiencies of four different cell-free implementations are analyzed, with spatially correlated fading and arbitrary processing. It turns out that it is possible to outperform cellular networks by a wide margin, but only using the right signal processing. A centralized implementation with optimal processing maximizes performance and, surprisingly, also reduces the fronthaul signaling., Funding Agencies|ELLIIT; Wallenberg AI, Autonomous Systems and Software Program (WASP); University of Pisa under the PRA 2018-2019 Research Project CONCEPT

Cell-free Massive MIMO systems consist of a large number of geographically distributed access points (APs) that serve users by coherent joint transmission. Downlink power allocation is important in these systems, to determine which APs should transmit to which users and with what power. If the system is implemented correctly, it can deliver a more uniform user performance than conventional cellular networks. To this end, previous works have shown how to perform system-wide max-min fairness power allocation when using maximum ratio precoding. In this paper, we first generalize this method to arbitrary precoding, and then train a neural network to perform approximately the same power allocation but with reduced computational complexity. Finally, we train one neural network per AP to mimic system-wide max-min fairness power allocation, but using only local information. By learning the structure of the local propagation environment, this method outperforms the state-of-the-art distributed power allocation method from the Cell-free Massive MIMO literature., Funding Agencies|Wallenberg AI, Autonomous Systems and Software Program (WASP) - Knut and Alice Wallenberg Foundation

Usage of passive intelligent surface (PIS) is emerging as a low-cost green alternative to massive antenna systems for realizing high energy beamforming (EB) gains. To maximize its realistic utility, we present a novel channel estimation (CE) protocol for PIS-assisted energy transfer (PET) from a multiantenna power beacon (PB) to a single-antenna energy harvesting (EH) user. Noting the practical limitations of PIS and EH user, all computations are carried out at PB having required active components and radio resources. Using these estimates, near-optimal analytical active and passive EB designs are respectively derived for PB and PIS, that enable efficient PET over a longer duration of coherence block. Nontrivial design insights on relative significance of array size at PIS and PB are also provided. Numerical results validating theoretical claims against the existing benchmarks demonstrate that with sufficient passive elements at PIS, we can achieve desired EB gain with reduced active array size at PB., Funding Agencies|ELLIIT