References of "Ottersten, Björn 50002797"
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See detailJoint User Grouping and Power Allocation for MISO Systems: Learning to Schedule
Yuan, Yaxiong; Vu, Thang Xuan UL; Lei, Lei UL et al

in IEEE European Signal Processing Conference 2019 (2019, September)

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See detailARCHITECTURES AND SYNCHRONIZATION TECHNIQUES FOR COHERENT DISTRIBUTED REMOTE SENSING SYSTEMS
Merlano Duncan, Juan Carlos UL; Querol Borras, Jorge UL; Camps, Adriano et al

in 2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2019, August 31)

Phase, frequency and time synchronization is a crucial requirement for many applications as such as multi-static remote sensing and distributed beamforming for communications. The literature on the field ... [more ▼]

Phase, frequency and time synchronization is a crucial requirement for many applications as such as multi-static remote sensing and distributed beamforming for communications. The literature on the field is very wide, and in some cases, the requirements of the proposed synchronization solution may surpass the ones set by the application itself. Moreover, the synchronization solution becomes even more challenging when the nodes are flying or hovering on aerial or space platforms. In this work, we compare and classify the synchronization technologies available in the literature according to a common proposed framework, and we discuss the considerations of an implementation for distributed remote sensing applications. The general framework considered is based on a distributed collection of autonomous nodes that try to synchronize their clocks with a common reference. Moreover, they can be classified in non-overlapping, adjacent and overlapping frequency band scenarios [less ▲]

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See detailCalibrated Learning for Online Distributed Power Allocation in Small-Cell Networks
Zhang, Xinruo; Nakhai, Mohammad Reza; Zheng, Gan UL et al

in IEEE Transactions on Communications (2019), 67(11), 8124-8136

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station ... [more ▼]

This paper introduces a combined calibrated learning and bandit approach to online distributed power control in small cell networks operated under the same frequency bandwidth. Each small base station (SBS) is modelled as an intelligent agent who autonomously decides on its instantaneous transmit power level by predicting the transmitting policies of the other SBSs, namely the opponent SBSs, in the network, in real-time. The decision making process is based jointly on the past observations and the calibrated forecasts of the upcoming power allocation decisions of the opponent SBSs who inflict the dominant interferences on the agent. Furthermore, we integrate the proposed calibrated forecast process with a bandit policy to account for the wireless channel conditions unknown a priori , and develop an autonomous power allocation algorithm that is executable at individual SBSs to enhance the accuracy of the autonomous decision making. We evaluate the performance of the proposed algorithm in cases of maximizing the long-term sum-rate, the overall energy efficiency and the average minimum achievable data rate. Numerical simulation results demonstrate that the proposed design outperforms the benchmark scheme with limited amount of information exchange and rapidly approaches towards the optimal centralized solution for all case studies. [less ▲]

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See detailAn Approximate Solution for Symbol-Level Multiuser Precoding Using Support Recovery
Haqiqatnejad, Alireza UL; Kayhan, Farbod UL; Ottersten, Björn UL

in IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), Cannes 2-5 July 2019 (2019, August 29)

In this paper, we propose a low-complexity method to approximately solve the SINR-constrained optimization problem of symbol-level precoding (SLP). First, assuming a generic modulation scheme, the ... [more ▼]

In this paper, we propose a low-complexity method to approximately solve the SINR-constrained optimization problem of symbol-level precoding (SLP). First, assuming a generic modulation scheme, the precoding optimization problem is recast as a standard non-negative least squares (NNLS). Then, we improve an existing closed-form SLP (CF-SLP) scheme using the conditions for nearly perfect recovery of the optimal solution support, followed by solving a reduced system of linear equations. We show through simulation results that in comparison with the CF-SLP method, the improved approximate solution of this paper, referred to as ICF-SLP, significantly enhances the performance with a negligible increase in complexity. We also provide comparisons with a fast-converging iterative NNLS algorithm, where it is shown that the ICF-SLP method is comparable in performance to the iterative algorithm with a limited maximum number of iterations. Analytic discussions on the complexities of different methods are provided, verifying the computational efficiency of the proposed method. Our results further indicate that the ICF-SLP scheme performs quite close to the optimal SLP, particularly in the large system regime. [less ▲]

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See detailM-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form Solution
Krivochiza, Jevgenij UL; Merlano Duncan, Juan Carlos UL; Chatzinotas, Symeon UL et al

Scientific Conference (2019, August)

In this paper, we derive a closed-form algorithm of the computationally efficient Symbol-Level Precoding (SLP) for power efficient communications when using M-QAM modulated waveforms. The channel state ... [more ▼]

In this paper, we derive a closed-form algorithm of the computationally efficient Symbol-Level Precoding (SLP) for power efficient communications when using M-QAM modulated waveforms. The channel state information (CSI) based and data-aided SLP technique optimizes power efficiency by solving a non-negative convex quadratic optimization problem per time frame of transmitted symbols. The optimization combines constructive inter-user interference to minimize the sum power of precoded symbols at the transmitter side under constraints for minimum SNR at the receiver side. The SLP implementation incurs extra computational complexity of the transmitter. We propose a convex quadratic optimization problem for M-QAM constellations and derive a closed-form algorithm with a fixed number of iterations to solve the problem. [less ▲]

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See detailLearning-Based Resource Allocation: Efficient Content Delivery Enabled by Convolutional Neural Network
Lei, Lei UL; Yaxiong, Yuan UL; Vu, Thang Xuan UL et al

in IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC) 2019 (2019, July)

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See detailMachine Learning based Antenna Selection and Power Allocation in Multi-user MISO Systems
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in 2019 IEEE International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt) (2019, June)

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See detailOn the Successful Delivery Probability of Full-Duplex-Enabled Mobile Edge Caching
Vu, Thang Xuan UL; Lei, Lei UL; Chatzinotas, Symeon UL et al

in IEEE Communications Letters (2019)

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See detailInterference Localization On-Board the Satellite Using Drift Induced Virtual Array
Arora, Aakash UL; Maleki, Sina; Shankar, Bhavani UL et al

in Proc. 2018 International Conference on Signal Processing and Communications (SPCOM) (2019, May 30)

Herein, we investigate the interference received from other wireless networks into a satellite communication (SATCOM) link, and review approaches to identify the interference location using on-board ... [more ▼]

Herein, we investigate the interference received from other wireless networks into a satellite communication (SATCOM) link, and review approaches to identify the interference location using on-board satellite processing. Interference is an increasing problem for satellite communication links, and while receiving signals from gateways or user terminals, the uplink is prone to disturbance by interference due to jammers or unintentional transmissions. In this paper, our aim is to localize unknown interference sources present on the ground by estimating direction of arrival (DOA) information using onboard processing (OBP) in the satellite, and the satellite drift inducing a virtual array. In this work, the signal sampled by the drifting single antenna feed is modeled as using an arbitrary array. Building on this model, we perform the 2-D DOA (azimuth and elevation) estimation. The key challenges in such a design include single snapshot based DOA estimation with low complexity and robustness, arising out of limited on-board computational complexity as well as uncertainty in parameters like the drift speed. Employing realistic satellite drift patterns, the paper illustrates the performance of the proposed technique highlighting the accuracy in localization under adverse environments. We provide numerical simulations to show the effectiveness of our methodology. [less ▲]

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See detailUltrareliable SWIPT using Unscheduled Short Packet Transmissions
Kisseleff, Steven UL; Chatzinotas, Symeon UL; Ottersten, Björn UL

in IEEE International Conference on Communications (ICC), B5G-URLLC Workshop, Shanghai, May 2019 (2019, May 20)

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to the co-channel interference from simultaneous transmissions. In the recent time, this problem has been ... [more ▼]

Large communication networks, e.g. Internet of Things (IoT), are known to be vulnerable to the co-channel interference from simultaneous transmissions. In the recent time, this problem has been extensively studied in various contexts. Due to a potentially very long duty cycle, orthogonal multiple access techniques are not well suited for such schemes. Instead, random medium access (RMA) seems promising, since it guarantees a lower bound for the network throughput even in presence of an infinite number of simultaneous transmissions while reducing the average length of the duty cycle. Such an RMA scheme is based on transmission of short data packets with unknown scheduling. Of course, a reliable symbol detection for this type of communication is very challenging not only due to a large amount of interference from the adjacent nodes, but also because of the uncertainty related to the presence or absence of overlapping packets. Interestingly, with increasing number of network nodes also the amount of energy, which can be harvested from the received signal, increases. This is especially beneficial for powering of a relay device, which may utilize the energy for further information processing and retransmission. In this paper, we address the design of a simultaneous information and power transfer scheme based on unscheduled short packet transmissions for ultrareliable communication. [less ▲]

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See detailVIEW-INVARIANT ACTION RECOGNITION FROM RGB DATA VIA 3D POSE ESTIMATION
Baptista, Renato UL; Ghorbel, Enjie UL; Papadopoulos, Konstantinos UL et al

in IEEE International Conference on Acoustics, Speech and Signal Processing, Brighton, UK, 12–17 May 2019 (2019, May)

In this paper, we propose a novel view-invariant action recognition method using a single monocular RGB camera. View-invariance remains a very challenging topic in 2D action recognition due to the lack of ... [more ▼]

In this paper, we propose a novel view-invariant action recognition method using a single monocular RGB camera. View-invariance remains a very challenging topic in 2D action recognition due to the lack of 3D information in RGB images. Most successful approaches make use of the concept of knowledge transfer by projecting 3D synthetic data to multiple viewpoints. Instead of relying on knowledge transfer, we propose to augment the RGB data by a third dimension by means of 3D skeleton estimation from 2D images using a CNN-based pose estimator. In order to ensure view-invariance, a pre-processing for alignment is applied followed by data expansion as a way for denoising. Finally, a Long-Short Term Memory (LSTM) architecture is used to model the temporal dependency between skeletons. The proposed network is trained to directly recognize actions from aligned 3D skeletons. The experiments performed on the challenging Northwestern-UCLA dataset show the superiority of our approach as compared to state-of-the-art ones. [less ▲]

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See detailInexact Block Coordinate Descent Algorithms for Nonsmooth Nonconvex Optimization
Yang, Yang UL; Pesavento, Marius; Luo, Zhi-Quan et al

E-print/Working paper (2019)

In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is selected and updated by ... [more ▼]

In this paper, we propose an inexact block coordinate descent algorithm for large-scale nonsmooth nonconvex optimization problems. At each iteration, a particular block variable is selected and updated by solving the original optimization problem with respect to that block variable inexactly. More precisely, a local approximation of the original optimization problem is solved. The proposed algorithm has several attractive features, namely, i) high flexibility, as the approximation function only needs to be strictly convex and it does not have to be a global upper bound of the original function; ii) fast convergence, as the approximation function can be designed to exploit the problem structure at hand and the stepsize is calculated by the line search; iii) low complexity, as the approximation subproblems are much easier to solve and the line search scheme is carried out over a properly constructed differentiable function; iv) guaranteed convergence to a stationary point, even when the objective function does not have a Lipschitz continuous gradient. Interestingly, when the approximation subproblem is solved by a descent algorithm, convergence to a stationary point is still guaranteed even if the approximation subproblem is solved inexactly by terminating the descent algorithm after a finite number of iterations. These features make the proposed algorithm suitable for large-scale problems where the dimension exceeds the memory and/or the processing capability of the existing hardware. These features are also illustrated by several applications in signal processing and machine learning, for instance, network anomaly detection and phase retrieval. [less ▲]

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See detailParallel coordinate descent algorithms for sparse phase retrieval
Yang, Yang UL; Pesavento, Marius; Eldar, Yonina C. et al

in Proc. 2019 IEEE International Conference on Acoustics, Speech and Signal (ICASSP) (2019, May)

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See detailClosed Form Discrete Unimodular MIMO Waveform Design Using Block Circulant Decomposition
Hammes, Christian UL; Shankar, Bhavani UL; Ottersten, Björn UL

Poster (2019, April 22)

This paper deals with the waveform design under the constraint of discrete multiphase unimodular sequences. It relies on Block Circulant decomposition of the slow-time transmitted waveform. The presented ... [more ▼]

This paper deals with the waveform design under the constraint of discrete multiphase unimodular sequences. It relies on Block Circulant decomposition of the slow-time transmitted waveform. The presented closed-form solution is capable of designing orthogonal signals, such that the virtual MIMO paradigm is enabled leading to enhanced angular resolution. On the other hand, the proposed method is also capable of approximating any desired radiation pattern within the physical limits of the transmitted array size. Simulation results prove the effectiveness in terms computational complexity, orthogonal signal design and the transmit beam pattern design under constant modulus constraint. [less ▲]

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See detailA Calibrated Learning Approach to Distributed Power Allocation in Small Cell Networks
Zhang, Xinruo; Nakhai; Zheng, Gan UL et al

in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2019, April 17)

This paper studies the problem of max-min fairness power allocation in distributed small cell networks operated under the same frequency bandwidth. We introduce a calibrated learning enhanced time ... [more ▼]

This paper studies the problem of max-min fairness power allocation in distributed small cell networks operated under the same frequency bandwidth. We introduce a calibrated learning enhanced time division multiple access scheme to optimize the transmit power decisions at the small base stations (SBSs) and achieve max-min user fairness in the long run. Provided that the SBSs are autonomous decision makers, the aim of the proposed algorithm is to allow SBSs to gradually improve their forecast of the possible transmit power levels of the other SBSs and react with the best response based on the predicted results at individual time slots. Simulation results validate that in terms of achieving max-min signal-to-interference-plus-noise ratio, the proposed distributed design outperforms two benchmark schemes and achieves a similar performance as compared to the optimal centralized design. [less ▲]

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See detailRobust Precoding Techniques for Multibeam Mobile Satellite Systems
Joroughi, Vahid UL; Shankar, Bhavani UL; Maleki, Sina UL et al

in 2019 IEEE Wireless Communications and Networking Conference (WCNC) (2019, April)

This paper presents designing precoding technique at the gateway of a multibeam mobile satellite systems, enabling full frequency reuse pattern among the beams. Such a system brings in two critical ... [more ▼]

This paper presents designing precoding technique at the gateway of a multibeam mobile satellite systems, enabling full frequency reuse pattern among the beams. Such a system brings in two critical challenges to overcome. The inter-beam interference makes applying interference mitigation techniques necessary. Further, when the user terminals are mobile the Channel State Information (CSI) becomes time-varying which is another challenge to overcome. Therefore, the gateway has only access to an outdated CSI, which can eventually limit the precoding gains. In this way, employing a proper CSI estimation mechanism at the gateway can improve the performance of the precoding scheme. In this context, the objectives of this paper are two folds. First, we present different CSI feedback mechanisms which aim at preserving a lower CSI variations at the gateway. Then, we develop the corresponding precoding schemes which are adapted with the proposed CSI feedback mechanisms. To keep the complexity of the proposed precoding schemes affordable, we consider a maritime communication scenario so that the signals received by mobile user terminals suffer from a lower pathloss compared to the Land Mobile communication. Finally, we provide several simulations results in order to evaluate the performance of the proposed precoding techniques. [less ▲]

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See detailPricing Perspective for SWIPT in OFDM-based Multi-User Wireless Cooperative Systems
Gautam, Sumit UL; Lagunas, Eva UL; Vuppala, Satyanarayana UL et al

Scientific Conference (2019, April)

We propose a novel formulation for joint maximization of total weighted sum-spectral efficiency and weighted sum-harvested energy to study Simultaneous Wireless Information and Power Transfer (SWIPT) from ... [more ▼]

We propose a novel formulation for joint maximization of total weighted sum-spectral efficiency and weighted sum-harvested energy to study Simultaneous Wireless Information and Power Transfer (SWIPT) from a pricing perspective. Specifically, we consider that a transmit source communicates with multiple destinations using Orthogonal Frequency Division Multiplexing (OFDM) system within a dual-hop relay-assisted network, where the destination nodes are capable of jointly decoding information and harvesting energy from the same radio-frequency (RF) signal using either the time-switching (TS) or power-splitting (PS) based SWIPT receiver architectures. Computation of the optimal solution for the aforementioned problem is an extremely challenging task as joint optimization of several network resources introduce intractability at high numeric values of relays, destination nodes and OFDM sub-carriers. Therefore, we present a suitable algorithm with sub-optimal results and good performance to compute the performance of joint data processing and harvesting energy under fixed pricing methods by adjusting the respective weight factors, motivated by practical statistics. Furthermore, by exploiting the binary options of the weights, we show that the proposed formulation can be regulated purely as a sum-spectral efficiency maximization or solely as a sum-harvested energy maximization problem. Numerical results illustrate the benefits of the proposed design under several operating conditions and parameter values. [less ▲]

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See detailRelay Selection and Resource Allocation for SWIPT in Multi-User OFDMA Systems
Gautam, Sumit UL; Lagunas, Eva UL; Chatzinotas, Symeon UL et al

in IEEE Transactions on Wireless Communications (2019)

We investigate the resource allocation and relay selection in a two-hop relay-assisted multi-user Orthogonal Frequency Division Multiple Access (OFDMA) network, where the end-nodes support Simultaneous ... [more ▼]

We investigate the resource allocation and relay selection in a two-hop relay-assisted multi-user Orthogonal Frequency Division Multiple Access (OFDMA) network, where the end-nodes support Simultaneous Wireless Information and Power Transfer (SWIPT) employing a Power Splitting (PS) technique. Our goal is to optimize the end-nodes’ power splitting ratios as well as the relay, carrier and power assignment so that the sum-rate of the system is maximized subject to harvested energy and transmitted power constraints. Such joint optimization with mixed integer non-linear programming structure is combinatorial in nature. Due to the complexity of this problem, we propose to solve its dual problem which guarantees asymptotic optimality and less execution time compared to a highly-complex exhaustive search approach. Furthermore, we also present a heuristic method to solve this problem with lower computational complexity. Simulation results reveal that the proposed algorithms provide significant performance gains compared to a semi-random resource allocation and relay selection approach and close to the optimal solution when the number of OFDMA sub-carriers is sufficiently large. [less ▲]

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See detailRobust Design of Power Minimizing Symbol-Level Precoder under Channel Uncertainty
Haqiqatnejad, Alireza UL; Kayhan, Farbod UL; Ottersten, Björn UL

in IEEE Global Communications Conference (GLOBECOM), Abu Dhabi 9-13 December 2018 (2019, February 21)

In this paper, we investigate the downlink transmission of a multiuser multiple-input single-output (MISO) channel under a symbol-level precoding (SLP) scheme, having imperfect channel knowledge at the ... [more ▼]

In this paper, we investigate the downlink transmission of a multiuser multiple-input single-output (MISO) channel under a symbol-level precoding (SLP) scheme, having imperfect channel knowledge at the transmitter. In defining the SLP design problem, a general category of constructive interference regions (CIR) called distance preserving CIR (DPCIR) is adopted. In particular, we are interested in a robust SLP design minimizing the total transmit power subject to individual quality-of-service (QoS) requirements. We consider two common models for the channel uncertainty region, namely, spherical (norm-bounded) and stochastic. For the spherical uncertainty model, a worst-case robust precoder is proposed, while for the stochastically known uncertainties, we derive a convex optimization problem with probabilistic constraints. We simulate the performance of the proposed robust approaches, and compare them with the existing methods. Through the simulation results, we also show that there is an essential trade-off between the two robust approaches. [less ▲]

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