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See detailProof of the Equivalency of MSE-Constrained and Rate-Constrained Power Optimization Approaches to Distributed Bi-Directional Beamforming
Rahimi, Razgar; Shahbazpanahi, Shahram UL; Ottersten, Björn UL

in IEEE Transactions on Signal and Information Processing over Networks (2020), 6

Considered in this article is an asynchronous single-carrier two-way relay network, where two transceivers aim to communicate through a set of amplify-and-forward (AF) relays. This network is asynchronous ... [more ▼]

Considered in this article is an asynchronous single-carrier two-way relay network, where two transceivers aim to communicate through a set of amplify-and-forward (AF) relays. This network is asynchronous in the sense that the signal transmitted by any of the two transceivers arrives the other transceiver through different relaying paths, with possibly different propagation delays, thereby materializing a multipath channel. At sufficiently high data rates, this multi-path end-to-end channel causes inter-symbol-interference (ISI) in the received signals. Considering such a network, this paper presents two contributions. The first contribution is the rigorous characterization of the region of the mean-squared errors (MSEs) of the symbol estimates at the two transceivers under a total power budget, and when linear block post-channel equalization is used at the receiver front-end of the two transceivers. The importance of this MSE region characterization resides in the fact that knowing this region allows for characterization the region of un-coded probabilities of error at the two transceivers. Also, this MSE region characterization paves the way towards presenting the second contribution in this paper. Indeed, in the second contribution, this article relies on this MSE region characterization to rigorously prove that an MSE-constrained total power minimization approach and a rate-constrained total power minimization approach to design transceiver power allocation and distributed beamforming are equivalent, if the MSE thresholds in the former approach and the rate thresholds in the latter approach are properly chosen. The equivalence of these two approaches implies that the un-coded MSE performance of the network can be inferred from the rate-constrained problem, and conversely, the coded rate performance of the network can be inferred from the MSE-constrained total power minimization problem. [less ▲]

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See detailRobust SINR-Constrained Symbol-Level Multiuser Precoding With Imperfect Channel Knowledge
Haqiqatnejad, Alireza UL; Kayhan, Farbod UL; Ottersten, Björn UL

in IEEE Transactions on Signal Processing (2020), 68(1), 1837-1852

In this paper, we address robust design of symbol-level precoding (SLP) for the downlink of multiuser multiple-input single-output wireless channels, when imperfect channel state information (CSI) is ... [more ▼]

In this paper, we address robust design of symbol-level precoding (SLP) for the downlink of multiuser multiple-input single-output wireless channels, when imperfect channel state information (CSI) is available at the transmitter. In particular, we consider a well known model for the CSI imperfection, namely, stochastic Gaussian-distributed uncertainty. Our design objective is to minimize the total (per-symbol) transmission power subject to constructive interference (CI) constraints as well as the users’ quality-of-service requirements in terms of signal-to-interference-plus-noise ratio. Assuming stochastic channel uncertainties, we first define probabilistic CI constraints in order to achieve robustness to statistically known CSI errors. Since these probabilistic constraints are difficult to handle, we resort to their convex approximations in the form of tractable (deterministic) robust constraints. Three convex approximations are obtained based on different conservatism levels, among which one is introduced as a benchmark for comparison. We show that each of our proposed approximations is tighter than the other under specific robustness settings, while both of them always outperform the benchmark. Using the proposed CI constraints, we formulate the robust SLP optimization problem as a second-order cone program. Extensive simulation results are provided to validate our analytic discussions and to make comparisons with conventional block-level robust precoding schemes. We show that the robust design of symbol-level precoder leads to an improved performance in terms of energy efficiency at the cost of increasing the computational complexity by an order of the number of users in the large system limit, compared to its non-robust counterpart. [less ▲]

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See detailA RAN Resource Slicing Mechanism for Multiplexing of eMBB and URLLC Services in OFDMA based 5G Wireless Networks
Korrai, Praveenkumar UL; Lagunas, Eva UL; Sharma, Shree Krishna UL et al

in IEEE Access (2020)

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two ... [more ▼]

Enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communications (URLLC) are the two main expected services in the next generation of wireless networks. Accommodation of these two services on the same wireless infrastructure leads to a challenging resource allocation problem due to their heterogeneous specifications. To address this problem, slicing has emerged as an architecture that enables a logical network with specific radio access functionality to each of the supported services on the same network infrastructure. The allocation of radio resources to each slice according to their requirements is a fundamental part of the network slicing that is usually executed at the radio access network (RAN). In this work, we formulate the RAN resource allocation problem as a sum-rate maximization problem subject to the orthogonality constraint (i.e., service isolation), latency-related constraint and minimum rate constraint while maintaining the reliability constraint with the incorporation of adaptive modulation and coding (AMC). However, the formulated problem is not mathematically tractable due to the presence of a step-wise function associated with the AMC and a binary assignment variable. Therefore, to solve the proposed optimization problem, first, we relax the mathematical intractability of AMC by using an approximation of the non-linear AMC achievable throughput, and next, the binary constraint is relaxed to a box constraint by using the penalized reformulation of the problem. The result of the above two-step procedure provides a close-to-optimal solution to the original optimization problem. Furthermore, to ease the complexity of the optimization-based scheduling algorithm, a low-complexity heuristic scheduling scheme is proposed for the efficient multiplexing of URLLC and eMBB services. Finally, the effectiveness of the proposed optimization and heuristic schemes is illustrated through extensive numerical simulations. [less ▲]

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See detailStructured Compression of Deep Neural Networks with Debiased Elastic Group LASSO
Oyedotun, Oyebade UL; Aouada, Djamila UL; Ottersten, Björn UL

in IEEE 2020 Winter Conference on Applications of Computer Vision (WACV 20), Aspen, Colorado, US, March 2–5, 2020 (2020, March 01)

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See detailHybrid Analog-Digital Transceiver Designs for Multi-User MIMO mmWave Cognitive Radio Systems
Tsinos, Christos UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Transactions on Cognitive Communications and Networking (2020)

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See detailVertex Feature Encoding and Hierarchical Temporal Modeling in a Spatio-Temporal Graph Convolutional Network for Action Recognition
Papadopoulos, Konstantinos UL; Ghorbel, Enjie UL; Aouada, Djamila UL et al

in International Conference on Pattern Recognition, Milan 10-15 January 2021 (2020)

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See detailReceive Beamforming for Ultrareliable Random Access based SWIPT
Kisseleff, Steven UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in Proceedings of IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications (2020)

Ultrareliable uplink communication based on random access poses novel research challenges for the receiver design. Here, the uncertainty imposed by the random access and a large amount of interfering ... [more ▼]

Ultrareliable uplink communication based on random access poses novel research challenges for the receiver design. Here, the uncertainty imposed by the random access and a large amount of interfering transmissions is the limiting factor for the system performance. Recently, this type of communication has been addressed in context of simultaneous wireless information and power transfer (SWIPT). The need to adapt the power splitting to the signal states according to the underlying random access has been tackled by introducing a predictor, which determines the valid states of the received signal based on the long-term observation. Hence, the power splitting factor is scaled accordingly in order to guarantee ultrareliable communication and maximized harvested energy.In this work, we extend the considered SWIPT scenario by introducing multiple antennas at the receiver side. Through this, the received energy can be substantially increased, if the energy harvesting parameters and the spatial filter coefficients are jointly optimized. Hence, we propose an optimization procedure, which aims at maximizing the harvested energy under the ultrareliability constraint. The mentioned prediction method is then combined with the optimization solution and the resulting system performance is numerically evaluated. [less ▲]

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See detailRandom Access based Reliable Uplink Communication and Power Transfer using Dynamic Power Splitting
Kisseleff, Steven UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE Transactions on Wireless Communications (2020)

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to co-channel interference. One possibility to address this issue is the use of orthogonal multiple access (OMA ... [more ▼]

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to co-channel interference. One possibility to address this issue is the use of orthogonal multiple access (OMA) techniques. However, due to a potentially very long duty cycle, OMA is not well suited for such schemes. Instead, random medium access (RMA) appears more promising. An RMA scheme is based on transmission of short data packets with random scheduling, which is typically unknown to the receiver. The received signal, which consists of the overlapping packets, can be used for energy harvesting and powering of a relay device. Such an energy harvesting relay may utilize the energy for further information processing and uplink transmission. In this paper, we address the design of a simultaneous information and power transfer scheme based on randomly scheduled packet transmissions and reliable symbol detection. We formulate a prediction problem with the goal to maximize the harvested power for an RMA scenario. In order to solve this problem, we propose a new prediction method, which shows a significant performance improvement compared to the straightforward baseline scheme. Furthermore, we investigate the complexity of the proposed method and its vulnerability to imperfect channel state information. [less ▲]

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See detailFast Adaptive Reparametrization (FAR) with Application to Human Action Recognition
Ghorbel, Enjie UL; Demisse, Girum UL; Aouada, Djamila UL et al

in IEEE Signal Processing Letters (2020)

In this paper, a fast approach for curve reparametrization, called Fast Adaptive Reparamterization (FAR), is introduced. Instead of computing an optimal matching between two curves such as Dynamic Time ... [more ▼]

In this paper, a fast approach for curve reparametrization, called Fast Adaptive Reparamterization (FAR), is introduced. Instead of computing an optimal matching between two curves such as Dynamic Time Warping (DTW) and elastic distance-based approaches, our method is applied to each curve independently, leading to linear computational complexity. It is based on a simple replacement of the curve parameter by a variable invariant under specific variations of reparametrization. The choice of this variable is heuristically made according to the application of interest. In addition to being fast, the proposed reparametrization can be applied not only to curves observed in Euclidean spaces but also to feature curves living in Riemannian spaces. To validate our approach, we apply it to the scenario of human action recognition using curves living in the Riemannian product Special Euclidean space SE(3) n. The obtained results on three benchmarks for human action recognition (MSRAction3D, Florence3D, and UTKinect) show that our approach competes with state-of-the-art methods in terms of accuracy and computational cost. [less ▲]

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See detailConstant-Envelope Precoding for Satellite Systems
Tsinos, Christos; Arora, Aakash UL; Ottersten, Björn UL

in Proc. 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2020)

In this paper, Constant-Envelope Precoding techniques are presented for satellite-based communication systems. In the developed transmission technique the signals of the antennas are designed to be of ... [more ▼]

In this paper, Constant-Envelope Precoding techniques are presented for satellite-based communication systems. In the developed transmission technique the signals of the antennas are designed to be of constant amplitude, improving the robustness of the latter to the non-linear distortions on satellite systems, introduced by the employed on-board Traveling-Wave-Tube-Amplifiers. We consider the forward link of a multi-beam broadband satellite system where the aim is to design the signals at the gateway such that the desired symbols are transmitted to the intended user terminals and the transmitted signals from the satellite terminal are of constant amplitude. At first, the gateway signals are designed given that a fixed on-board beamformer is applied to the satellite terminal. Then, the case of an adaptive on-board beamformer is considered which is designed jointly with the gateway signals. The design of the gateway signals and the adaptive on-board beamformer, in the second case, requires solving difficult nonconvex problems. Efficient algorithmic solutions are developed based on the saddle point method. The effectiveness of the proposed approaches is verified via numerical results. [less ▲]

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See detailCramer-Rao Bound on DOA Estimation of Finite Bandwidth Signals Using a Moving Sensor
Arora, Aakash UL; Mysore Rama Rao, Bhavani Shankar UL; Ottersten, Björn UL

in Proc. 2020 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2020)

In this paper, we provide a framework for the direction of arrival (DOA) estimation using a single moving sensor and evaluate performance bounds on estimation. We introduce a signal model which captures ... [more ▼]

In this paper, we provide a framework for the direction of arrival (DOA) estimation using a single moving sensor and evaluate performance bounds on estimation. We introduce a signal model which captures spatio-temporal incoherency in the received signal due to sensor motion in space and finite bandwidth of the signal, hitherto not considered. We show that in such a scenario, the source signal covariance matrix becomes a function of the source DOA, which is usually not the case. Due to this unknown dependency, traditional subspace techniques cannot be applied and conditions on source covariance needs to imposed to ensure identifiability. This motivates us to investigate the performance bounds through the Cramer-Rao Lower Bounds (CRLBs) to set benchmark performance for future estimators. This paper exploits the signal model to derive an appropriate CRLB, which is shown to be better than those in relevant literature. [less ▲]

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See detailA Worst-Case Performance Optimization Based Design Approach to Robust Symbol-Level Precoding for Downlink MU-MIMO
Haqiqatnejad, Alireza UL; Shahbazpanahi, Shahram UL; Ottersten, Björn UL

in IEEE Global Conference on Signal and Information Processing (GlobalSIP), Ottawa 11-14 November 2019 (2020)

This paper addresses the optimization problem of symbol-level precoding (SLP) in the downlink of a multiuser multiple-input multiple-output (MU-MIMO) wireless system while the precoder's output is subject ... [more ▼]

This paper addresses the optimization problem of symbol-level precoding (SLP) in the downlink of a multiuser multiple-input multiple-output (MU-MIMO) wireless system while the precoder's output is subject to partially-known distortions. In particular, we assume a linear distortion model with bounded additive noise. The original signal-to-interference- plus-noise ratio (SINR) -constrained SLP problem minimizing the total transmit power is first reformulated as a penalized unconstrained problem, which is referred to as the relaxed robust formulation. We then adopt a worst-case design approach to protect the users' intended symbols and the targeted constructive interference with a desired level of confidence. Due to the non-convexity of the relaxed robust formulation, we propose an iterative algorithm based on the block coordinate ascent-descent method. We show through simulation results that the proposed robust design is flexible in the sense that the CI constraints can be relaxed so as to keep a desirable balance between achievable rate and power consumption. Remarkably, the new formulation yields more energy-efficient solutions for appropriate choices of the relaxation parameter, compared to the original problem. [less ▲]

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See detailOnline Spatiotemporal Popularity Learning via Variational Bayes for Cooperative Caching
Mehrizi Rahmat Abadi, Sajad UL; Chaterjee, Saikat; Chatzinotas, Symeon UL et al

in IEEE Transactions on Communications (2020)

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See detailJoint Forecasting and Interpolation of Time-Varying Graph Signals Using Deep Learning
Lewenfus, Gabriela; Alves Martins, Wallace UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Signal and Information Processing over Networks (2020)

We tackle the problem of forecasting network-signal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate time-series ... [more ▼]

We tackle the problem of forecasting network-signal snapshots using past signal measurements acquired by a subset of network nodes. This task can be seen as a combination of multivariate time-series forecasting (temporal prediction) and graph signal interpolation (spatial prediction). This is a fundamental problem for many applications wherein deploying a high granularity network is impractical. Our solution combines recurrent neural networks with frequency-analysis tools from graph signal processing, and assumes that data is sufficiently smooth with respect to the underlying graph. The proposed learning model outperforms state-of-the-art deep learning techniques, especially when predictions are made using a small subset of network nodes, considering two distinct real world datasets: temperatures in the US and speed flow in Seattle. The results also indicate that our method can handle noisy signals and missing data, making it suitable to many practical applications. [less ▲]

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See detailDeepVI: A Novel Framework for Learning Deep View-Invariant Human Action Representations using a Single RGB Camera
Papadopoulos, Konstantinos UL; Ghorbel, Enjie UL; Oyedotun, Oyebade UL et al

in IEEE International Conference on Automatic Face and Gesture Recognition, Buenos Aires 18-22 May 2020 (2020)

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See detailJoint Power Allocation and Access Point Selection for Cell-free Massive MIMO
Vu, Thang Xuan UL; Chatzinotas, Symeon UL; ShahbazPanahi, Shahram et al

in IEEE International Conference on Communications (2020)

Cell-free massive multiple-input multiple-output (CF-MIMO) is a promising technological enabler for fifth generation (5G) networks in which a large number of access points (APs) jointly serve the users ... [more ▼]

Cell-free massive multiple-input multiple-output (CF-MIMO) is a promising technological enabler for fifth generation (5G) networks in which a large number of access points (APs) jointly serve the users. Each AP applies conjugate beamforming to precode data, which is based only on the AP's local channel state information. However, by having the nature of a (very) large number of APs, the operation of CF-MIMO can be energy-inefficient. In this paper, we investigate the energy efficiency performance of CF-MIMO by considering a practical energy consumption model which includes both the signal transmit energy as well as the static energy consumed by hardware components. In particular, a joint power allocation and AP selection design is proposed to minimize the total energy consumption subject to given quality of service (QoS) constraints. In order to deal with the combinatorial complexity of the formulated problem, we employ norm $l_{2,1}$-based block-sparsity and successive convex optimization to leverage the AP selection process. Numerical results show significant energy savings obtained by the proposed design, compared to all-active APs scheme and the large-scale based AP selection. [less ▲]

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See detailReconfigurable Intelligent Surfaces for Smart Cities: Research Challenges and Opportunities
Kisseleff, Steven UL; Alves Martins, Wallace UL; Al-Hraishawi, Hayder UL et al

in IEEE Open Journal of the Communications Society (2020)

The concept of Smart Cities has been introduced as a way to benefit from the digitization of various ecosystems at a city level. To support this concept, future communication networks need to be carefully ... [more ▼]

The concept of Smart Cities has been introduced as a way to benefit from the digitization of various ecosystems at a city level. To support this concept, future communication networks need to be carefully designed with respect to the city infrastructure and utilization of resources. Recently, the idea of 'smart' environment, which takes advantage of the infrastructure in order to enable better performance of wireless networks, has been proposed. This idea is aligned with the recent advances in design of reconfigurable intelligent surfaces (RISs), which are planar structures with the capability to reflect impinging electromagnetic waves toward preferred directions. Thus, RISs are expected to provide the necessary flexibility for the design of the ‘smart’ communication environment, which can be optimally shaped to enable cost- and energy-efficient signal transmissions where needed. Upon deployment of RISs, the ecosystem of the Smart Cities would become even more controllable and adaptable, which would subsequently ease the implementation of future communication networks in urban areas and boost the interconnection among private households and public services. In this article, we provide our vision on RIS integration into future Smart Cities by pointing out some forward looking new application scenarios and use cases and by highlighting the potential advantages of RIS deployment. To this end, we identify the most promising research directions and opportunities. The respective design problems are formulated mathematically. Moreover, we focus the discussion on the key enabling aspects for RIS-assisted Smart Cities, which require substantial research efforts such as pilot decontamination, precoding for large multiuser networks, distributed operation and control of RISs. These contributions pave the road to a systematic design of RIS-assisted communication networks for Smart Cities in the years to come. [less ▲]

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See detailHybrid Transceivers Design for Large-Scale Antenna Arrays Using Majorization-Minimization Algorithms
Arora, Aakash UL; Tsinos, Christos UL; Shankar, Bhavani UL et al

in IEEE Transactions on Signal Processing (2020), 68

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See detailOne-bitDoA estimation via Sparse Linear Arrays
Sedighi, Saeid UL; Mysore Rama Rao, Bhavani Shankar UL; Soltanalian, Mojtaba et al

in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2020)

Parameter estimation from noisy and one-bit quantized data has become an important topic in signal processing, as it offers low cost and low complexity in the implementation. On the other hand, Direction ... [more ▼]

Parameter estimation from noisy and one-bit quantized data has become an important topic in signal processing, as it offers low cost and low complexity in the implementation. On the other hand, Direction-of-Arrival (DoA) estimation using Sparse Linear Arrays (SLAs) has recently gained considerable interest in array processing due to their attractive capability of providing enhanced degrees of freedom. In this paper, the problem of DoA estimation from one-bit measurements received by an SLA is considered and a novel framework for solving this problem is proposed. The proposed approach first provides an estimate of the received signal covariance matrix through minimization of a constrained weighted least-squares criterion. Then, MUSIC is applied to the spatially smoothed version of the estimated covariance matrix to find the DoAs of interest. Several numerical results are provided to demonstrate the superiority of the proposed approach over its counterpart already propounded in the literature. [less ▲]

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