arXiv subjects

Erik G. Larsson

Publications and source records attributed to Erik G. Larsson.

At least 163 records · Page 9Linked to original sources

arXiv2019

Intelligent Reflecting Surface-Assisted Cognitive Radio System

Cognitive radio (CR) is an effective solution to improve the spectral efficiency (SE) of wireless communications by allowing the secondary users (SUs) to share spectrum with primary users. Meanwhile, intelligent reflecting surface (IRS), also known as reconfigurable intelligent surface (RIS), has been recently proposed as a promising approach to enhance energy efficiency (EE) of wireless communication systems through intelligently reconfiguring the channel environment. To improve both SE and EE, in this paper, we introduce multiple IRSs to a downlink multiple-input single-output (MISO) CR system, in which a single SU coexists with a primary network with multiple primary user receivers (PU-RXs). Our design objective is to maximize the achievable rate of SU subject to a total transmit power constraint on the SU transmitter (SU-TX) and interference temperature constraints on the PU-RXs, by jointly optimizing the beamforming at SU-TX and the reflecting coefficients at each IRS. Both perfect and imperfect channel state information (CSI) cases are considered in the optimization. Numerical results demonstrate that the introduction of IRS can significantly improve the achievable rate of SU under both perfect and imperfect CSI cases.

eess.SP↗

arXiv2019

Intelligent Reflecting Surfaces: Physics, Propagation, and Pathloss Modeling

Intelligent reflecting surfaces can improve the communication between a source and a destination. The surface contains metamaterial that is configured to "reflect" the incident wave from the source towards the destination. Two incompatible pathloss models have been used in prior work. In this letter, we derive the far-field pathloss using physical optics techniques and explain why the surface consists of many elements that individually act as diffuse scatterers but can jointly beamform the signal in a desired direction with a certain beamwidth. We disprove one of the previously conjectured pathloss models.

physics.app-ph↗

arXiv2019

Opinion Dynamics with Random Actions and a Stubborn Agent

We study opinion dynamics in a social network with stubborn agents who influence their neighbors but who themselves always stick to their initial opinion. We consider first the well-known DeGroot model. While it is known in the literature that this model can lead to consensus even in the presence of a stubborn agent, we show that the same result holds under weaker assumptions than has been previously reported. We then consider a recent extension of the DeGroot model in which the opinion of each agent is a random Bernoulli distributed variable, and by leveraging on the first result we establish that this model also leads to consensus, in the sense of convergence in probability, in the presence of a stubborn agent. Moreover, all agents' opinions converge to that of the stubborn agent.

cs.SI↗

arXiv2019

Optimal Design of Energy-Efficient Cell-Free Massive MIMO: Joint Power Allocation and Load Balancing

A large-scale distributed antenna system that serves the users by coherent joint transmission is called Cell-free Massive MIMO (multiple input multiple output). For a given user set, only a subset of the access points (APs) is likely needed to satisfy the users' performance demands. To find a flexible and energy-efficient implementation, we minimize the total power consumption at the APs in the downlink, considering both the hardware and transmit powers, where APs can be turned off. Even though this is a non-convex optimization problem, a globally optimal solution is obtained by solving a mixed-integer second-order cone program. We also propose a low-complexity algorithm that exploits group-sparsity in the problem formulation. Numerical results manifest that our optimization framework can greatly reduce the power consumption compared to keeping all APs turned on and only minimizing the transmit powers.

cs.IT↗

arXiv2019

Dynamic Resource Allocation in Co-Located and Cell-Free Massive MIMO

In this paper, we study joint power control and scheduling in uplink massive multiple-input multiple-output (MIMO) systems with randomly arriving data traffic. We consider both co-located and Cell-Free (CF) Massive MIMO, where the difference lies in whether the antennas are co-located at the base station or spread over a wide network area. The data is generated at each user according to an individual stochastic process. Using Lyapunov optimization techniques, we develop a dynamic scheduling algorithm (DSA), which decides at each time slot the amount of data to admit to the transmission queues and the transmission rates over the wireless channel. The proposed algorithm optimizes the long-term user throughput under various fairness policies while keeping the transmission queues stable. Simulation results show that the state-of-the-art power control schemes developed for Massive MIMO with infinite backlogs can fail to stabilize the system even when the data arrival rates are within the network capacity region. Our proposed DSA shows advantage in providing finite delay with performance optimization whenever the network can be stabilized.

cs.IT↗

arXiv2019

Joint Unicast and Multi-group Multicast Transmission in Massive MIMO Systems

We study the joint unicast and multi-group multicast transmission in massive multiple-input-multiple-output (MIMO) systems. We consider a system model that accounts for channel estimation and pilot contamination, and derive achievable spectral efficiencies (SEs) for unicast and multicast user terminals (UTs), under maximum ratio transmission and zero-forcing precoding. For unicast transmission, our objective is to maximize the weighted sum SE of the unicast UTs, and for the multicast transmission, our objective is to maximize the minimum SE of the multicast UTs. These two objectives are coupled in a conflicting manner, due to their shared power resource. Therefore, we formulate a multiobjective optimization problem (MOOP) for the two conflicting objectives. We derive the Pareto boundary of the MOOP analytically. As each Pareto optimal point describes a particular efficient trade-off between the two objectives of the system, we determine the values of the system parameters (uplink training powers, downlink transmission powers, etc.) to achieve any desired Pareto optimal point. Moreover, we prove that the Pareto region is convex, hence the system should serve the unicast and multicast UTs at the same time-frequency resource. Finally, we validate our results using numerical simulations.

cs.IT↗

arXiv2019

Downlink Spectral Efficiency of Cell-Free Massive MIMO with Full-Pilot Zero-Forcing

Cell-free Massive multiple-input multiple-output (MIMO) ensures ubiquitous communication at high spectral efficiency (SE) thanks to increased macro-diversity as compared cellular communications. However, system scalability and performance are limited by fronthauling traffic and interference. Unlike conventional precoding schemes that only suppress intra-cell interference, full-pilot zero-forcing (fpZF), introduced in [1], actively suppresses also inter-cell interference, without sharing channel state information (CSI) among the access points (APs). In this study, we derive a new closed-form expression for the downlink (DL) SE of a cell-free Massive MIMO system with multi-antenna APs and fpZF precoding, under imperfect CSI and pilot contamination. The analysis also includes max-min fairness DL power optimization. Numerical results show that fpZF significantly outperforms maximum ratio transmission scheme, without increasing the fronthauling overhead, as long as the system is sufficiently distributed.

cs.IT↗

arXiv2019

Scalability Aspects of Cell-Free Massive MIMO

Ubiquitous cell-free massive MIMO (multiple-input multiple-output) combines massive MIMO technology and user-centric transmission in a distributed architecture. All the access points (APs) in the network cooperate to jointly and coherently serve a smaller number of users in the same time-frequency resource. However, this coordination needs significant amounts of control signalling which introduces additional overhead, while data co-processing increases the back/front-haul requirements. Hence, the notion that the "whole world" could constitute one network, and that all APs would act as a single base station, is not scalable. In this study, we address some system scalability aspects of cell-free massive MIMO that have been neglected in literature until now. In particular, we propose and evaluate a solution related to data processing, network topology and power control. Results indicate that our proposed framework achieves full scalability at the cost of a modest performance loss compared to the canonical form of cell-free massive MIMO.

cs.IT↗

arXiv2019

Downlink Training in Cell-Free Massive MIMO: A Blessing in Disguise

Cell-free Massive MIMO (multiple-input multiple-output) refers to a distributed Massive MIMO system where all the access points (APs) cooperate to coherently serve all the user equipments (UEs), suppress inter-cell interference and mitigate the multiuser interference. Recent works demonstrated that, unlike co-located Massive MIMO, the \textit{channel hardening} is, in general, less pronounced in cell-free Massive MIMO, thus there is much to benefit from estimating the downlink channel. In this study, we investigate the gain introduced by the downlink beamforming training, extending the previously proposed analysis to non-orthogonal uplink and downlink pilots. Assuming single-antenna APs, conjugate beamforming and independent Rayleigh fading channel, we derive a closed-form expression for the per-user achievable downlink rate that addresses channel estimation errors and pilot contamination both at the AP and UE side. The performance evaluation includes max-min fairness power control, greedy pilot assignment methods, and a comparison between achievable rates obtained from different capacity-bounding techniques. Numerical results show that downlink beamforming training, although increases pilot overhead and introduces additional pilot contamination, improves significantly the achievable downlink rate. Even for large number of APs, it is not fully efficient for the UE relying on the statistical channel state information for data decoding.

cs.IT↗

arXiv2019

Ubiquitous Cell-Free Massive MIMO Communications

Since the first cellular networks were trialled in the 1970s, we have witnessed an incredible wireless revolution. From 1G to 4G, the massive traffic growth has been managed by a combination of wider bandwidths, refined radio interfaces, and network densification, namely increasing the number of antennas per site. Due its cost-efficiency, the latter has contributed the most. Massive MIMO (multiple-input multiple-output) is a key 5G technology that uses massive antenna arrays to provide a very high beamforming gain and spatially multiplexing of users, and hence, increases the spectral and energy efficiency. It constitutes a centralized solution to densify a network, and its performance is limited by the inter-cell interference inherent in its cell-centric design. Conversely, ubiquitous cell-free Massive MIMO refers to a distributed Massive MIMO system implementing coherent user-centric transmission to overcome the inter-cell interference limitation in cellular networks and provide additional macro-diversity. These features, combined with the system scalability inherent in the Massive MIMO design, distinguishes ubiquitous cell-free Massive MIMO from prior coordinated distributed wireless systems. In this article, we investigate the enormous potential of this promising technology while addressing practical deployment issues to deal with the increased back/front-hauling overhead deriving from the signal co-processing.

cs.IT↗

arXiv2019

MmWave Hybrid Array with More Users than RF Chains

In millimeter wave communications, hybrid analog-digital arrays consisting of only a few radiofrequency (RF) chains offer an attractive alternative to costly digital arrays. A limitation to hybrid arrays is that the number of streams that can be transmitted simultaneously cannot exceed the number of RF chains. We demonstrate that if the states of the analog components and the symbols passed to the RF chains are jointly optimized on a symbol-by-symbol (SbS) basis, it is possible to achieve the same degrees of freedom as a digital array with linear precoding, thus effectively enabling the transmission of a large number of streams. To this end, an algorithm for SbS hybrid precoding is proposed based on a variation of orthogonal matching pursuit, particularly suited to the structure of our problem, which makes it fast and precise.

cs.IT↗

arXiv2019

Multi-Tag Backscattering to MIMO Reader: Channel Estimation and Throughput Fairness

Green low power networking with the least requirement of dedicated radio resources is need of the hour which has led to the upsurge of backscatter communication (BSC) technology. However, this inherent potential of BSC is challenged by hardware constraints of the underlying tags. We address this timely concern by investigating the practical efficacy of multiple-input-multiple-output (MIMO) technology in overcoming the fundamental limitations of BSC. Specifically, we first introduce a novel least-squares based channel estimation (CE) protocol for multi-tag BSC settings that takes care of both the unintended ambient reflections and the inability of tags in performing estimation by themselves. Then using it, a nontrivial low-complexity algorithm is proposed to obtain the optimal transceiver designs for the multiantenna reader to maximize the minimum value of the lower-bounded backscattered throughput among the single-antenna semi-passive tags. Additional analytical insights on both individually and jointly-optimal precoding vector and detector matrix at the reader are provided by exploring the asymptotically-optimal transceiver designs. Lastly detailed numerical investigation is carried out to validate the theoretical results and quantify the practically realizable throughput fairness. Specifically, more than seven-fold increase in the common-backscattered-throughput among tags as achieved by the proposed designs over the relevant benchmarks corroborates their practical significance.

cs.IT↗

arXiv2019

Massive MIMO for Drone Communications: Case Studies and Future Directions

Unmanned aerial vehicles (UAVs), also known as drones, are proliferating. Applications, such as surveillance, disaster management, and drone racing, place high requirements on the communication with the drones in terms of throughput, reliability, and latency. The existing wireless technologies, notably Wi-Fi, that are currently used for drone connectivity are limited to short ranges and low-mobility situations. New, scalable technology is needed to meet future demands on long connectivity ranges, support for fast-moving drones, and the possibility to simultaneously communicate with entire swarms of drones. Massive multiple-input and multiple-output (MIMO), the main technology component of emerging 5G standards, has the potential to meet these requirements.

cs.IT↗

arXiv2019

On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

cs.IT↗

arXiv2019

Massive MIMO with Spatially Correlated Rician Fading Channels

This paper considers multi-cell Massive MIMO (multiple-input multiple-output) systems where the channels are spatially correlated Rician fading. The channel model is composed of a deterministic line-of-sight (LoS) path and a stochastic non-line-of-sight (NLoS) component describing a practical spatially correlated multipath environment. We derive the statistical properties of the minimum mean squared error (MMSE), element-wise MMSE (EW-MMSE), and least-square (LS) channel estimates for this model. Using these estimates for maximum ratio (MR) combining and precoding, rigorous closed-form uplink (UL) and downlink (DL) spectral efficiency (SE) expressions are derived and analyzed. The asymptotic SE behavior when using the different channel estimators are also analyzed. Numerical results show that the SE is higher when using the MMSE estimator than the other estimators, and the performance gap increases with the number of antennas.

cs.IT↗

arXiv2019

Weighted Sum-Rate Optimization for Intelligent Reflecting Surface Enhanced Wireless Networks

Intelligent reflecting surface (IRS) is a promising solution to build a programmable wireless environment for future communication systems. In practice, an IRS consists of massive low-cost elements, which can steer the incident signal in fully customizable ways by passive beamforming. In this paper, we consider an IRS-aided multiuser multiple-input single-output (MISO) downlink communication system. In particular, the weighted sum-rate of all users is maximized by joint optimizing the active beamforming at the base-station (BS) and the passive beamforming at the IRS. In addition, we consider a practical IRS assumption, in which the passive elements can only shift the incident signal to discrete phase levels. This non-convex problem is firstly decoupled via Lagrangian dual transform, and then the active and passive beamforming can be optimized alternatingly. The active beamforming at BS is optimized based on the fractional programming method. Then, three efficient algorithms with closed-form expressions are proposed for the passive beamforming at IRS. Simulation results have verified the effectiveness of the proposed algorithms as compared to different benchmark schemes.

eess.SP↗

arXiv2019

Massive MIMO for Internet of Things (IoT) Connectivity

Massive MIMO is considered to be one of the key technologies in the emerging 5G systems, but also a concept applicable to other wireless systems. Exploiting the large number of degrees of freedom (DoFs) of massive MIMO essential for achieving high spectral efficiency, high data rates and extreme spatial multiplexing of densely distributed users. On the one hand, the benefits of applying massive MIMO for broadband communication are well known and there has been a large body of research on designing communication schemes to support high rates. On the other hand, using massive MIMO for Internet-of-Things (IoT) is still a developing topic, as IoT connectivity has requirements and constraints that are significantly different from the broadband connections. In this paper we investigate the applicability of massive MIMO to IoT connectivity. Specifically, we treat the two generic types of IoT connections envisioned in 5G: massive machine-type communication (mMTC) and ultra-reliable low-latency communication (URLLC). This paper fills this important gap by identifying the opportunities and challenges in exploiting massive MIMO for IoT connectivity. We provide insights into the trade-offs that emerge when massive MIMO is applied to mMTC or URLLC and present a number of suitable communication schemes. The discussion continues to the questions of network slicing of the wireless resources and the use of massive MIMO to simultaneously support IoT connections with very heterogeneous requirements. The main conclusion is that massive MIMO can bring benefits to the scenarios with IoT connectivity, but it requires tight integration of the physical-layer techniques with the protocol design.

cs.IT↗

arXiv2019

Sum Throughput Maximization in Multi-Tag Backscattering to Multiantenna Reader

Backscatter communication (BSC) is being realized as the core technology for pervasive sustainable Internet-of-Things applications. However, owing to the resource-limitations of passive tags, the efficient usage of multiple antennas at the reader is essential for both downlink excitation and uplink detection. This work targets at maximizing the achievable sum-backscattered-throughput by jointly optimizing the transceiver (TRX) design at the reader and backscattering coefficients (BC) at the tags. Since, this joint problem is nonconvex, we first present individually-optimal designs for the TRX and BC. We show that with precoder and {combiner} designs at the reader respectively targeting downlink energy beamforming and uplink Wiener filtering operations, the BC optimization at tags can be reduced to a binary power control problem. Next, the asymptotically-optimal joint-TRX-BC designs are proposed for both low and high signal-to-noise-ratio regimes. Based on these developments, an iterative low-complexity algorithm is proposed to yield an efficient jointly-suboptimal design. Thereafter, we discuss the practical utility of the proposed designs to other application settings like wireless powered communication networks and BSC with imperfect channel state information. Lastly, selected numerical results, validating the analysis and shedding novel insights, demonstrate that the proposed designs can yield significant enhancement in the sum-backscattered throughput over existing benchmarks.

cs.IT↗