Publications

Beamforming and Waveform Optimization for RF Wireless Power Transfer with Beyond Diagonal Reconfigurable Intelligent Surfaces

On the Effect of Self-Interference Cancelation in MultiHop Wireless Networks

In a wireless network, the problem of self-interference arises when a node transmits and receives simultaneously in the same frequency band. So far only two extreme approaches to circumvent this problem were thoroughly investigated in the literature. The first one prevents any node to transmit and receive simultaneously which may lead to a too conservative design. The second one assumes perfect self-interference cancelation which can be too optimistic since it ignores all possible technological limitations. To fill this gap, we provide a method based on complementary geometric programming for evaluating the gains achievable at the network layer when the network nodes employ self-interference cancelation techniques with different degrees of accuracy. The gains are evaluated in terms of average sum rate and average network congestion by using a network utility maximization framework. The method provides insights into the behavior of different network topologies when self-interference cancellation is employed in nodes. In addition, it can be used to assess the required degrees of accuracy of the self-interference in order to achieve substantial benefits. Thus, from a network design perspective, the proposed method is very beneficial. Numerical results suggest that the benefits from self-interference cancelation are more pronounced in tandem wireless network setups in which the network nodes are located in a linear grid.

Deep Contextual Bandits for Fast Initial Access in mmWave Based User-Centric Ultra-Dense Networks

Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high throughput requirements of future networks. Due to the high blockage susceptibility of mmWave, the connections may drop frequently. Hence efficient and fast beam management in initial access (IA) is essential. Current cellular systems use beam sweeping based IA mechanisms. UC UD concept requires all of its access points (APs) to perform IA. This leads to a shortage of orthogonal radio resources. Nonorthogonal resource allocation causes interference which leads to a higher misdetection probability. In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks. The DCB model uses one reference signal from the user to predict the IA beam. The reduced use of reference signals improves beam discovery delay and relaxes the requirement for radio resources. Ray-tracing and stochastic channel model-based simulations show that the suggested system outperforms its beam sweeping counterpart in terms of probability of beam misdetection and beam discovery delay in mmWave based UC UD networks.

An Interior-Point Method for Modified Total Variation Exploiting Transform-Domain Sparsity

The total variation (TV) minimization can be utilized in a compressive sensing framework to recover a signal from a small number of measurements by searching for a signal with a sparse gradient. However, many natural signals of interest, such as natural images, generally have sparse representations in known transforms. Hence, the performance of the signal reconstruction procedure can be improved by also taking into account this transform-domain sparsity of the signal. Thus, the TV minimization problem can be modified by introducing an $l_1$-norm penalty term. The $l_1$-regularized TV minimization problem searches for a signal with a sparse gradient and a sparse representation in the given transform. The main contribution of this paper is the derivation of a customized interior-point method for solving the $l_1$-regularized TV minimization problem that computes the search direction of the Newton method efficiently by exploiting the specific structure of the Hessian.

Performance Analysis of Passive/Active RIS Aided Wireless-Powered IoT Network With Nonlinear Energy Harvesting

No abstract is provided for this article.

Multicell MISO Downlink Weighted Sum-Rate Maximization: A Distributed Approach

We develop an easy to implement distributed method for weighted sum-rate maximization (WSRMax) problem in a multicell multiple antenna downlink system. Unlike the recently proposed minimum weighted mean-squared error based algorithms, where at each iteration all mobile terminals needs to estimate the covariance matrices of their received signals, compute and feedback over the air certain parameters to the base stations (BS), our algorithm operates without any user terminal assistance. It requires only BS to BS signalling via reliable backhaul links (e.g., fiber, microwave links) and all required computation is performed at the BSs. The algorithm is based on primal decomposition and subgradient methods, where the original nonconvex problem is split into a master problem and a number of subproblems (one for each BS). A novel sequential convex approximation strategy is proposed to address the nonconvex master problem. In the case of subproblems, we adopt an existing iterative approach based on second-order cone programming and geometric programming. The subproblems are coordinated to find a (possibly suboptimal) solution to the master problem. Subproblems can be solved by BSs in a fully asynchronous manner, though the coordination between subproblems should be synchronous. Numerical results are provided to see the behavior of the algorithm under different degrees of BS coordination. They show that the proposed algorithm yields a good tradeoff between the implementation-level simplicity and the performance.

Statistical Tools and Methodologies for Ultrareliable Low-Latency Communication - A Tutorial

Ultra-reliable low-latency communication (URLLC) constitutes a key service class of the fifth generation and beyond cellular networks.Notably, designing and supporting URLLC poses a herculean task due to the fundamental need of identifying and accurately characterizing the underlying statistical models in which the system operates, e.g., interference statistics, channel conditions, and the behavior of protocols.In general, multi-layer end-to-end approaches considering all the potential delay and error sources and proper statistical tools and methodologies are inevitably required for providing strong reliability and latency guarantees.This paper contributes to the body of knowledge in the latter aspect by providing a tutorial on several statistical tools and methodologies that are useful for designing and analyzing URLLC systems.Specifically, we overview the frameworks related to i) reliability theory, ii) short packet communications, iii) inequalities, distribution bounds, tail approximations, and risk-assessment tools, iv) rare events simulation, v) large-scale tools such as stochastic geometry, clustering, compressed sensing, and mean-field games, vi) queuing theory and information freshness, and vii) machine learning.Throughout the paper, we briefly review the stateof-the-art works using the addressed tools and methodologies, and their link to URLLC systems.Moreover, we discuss novel application examples focused on physical and medium access control layers.Finally, key research challenges and directions are highlighted to elucidate how URLLC analysis/design research may evolve in the coming years.

Mission Effective Capacity—A Novel Dependability Metric: A Study Case of Multiconnectivity-Enabled URLLC for IIoT

Various industrial Internet of Things applications demand execution periods throughout which no communication failure is tolerated. However, the classical understanding of reliability in the context of ultra-reliable low-latency communication (URLLC) does not reflect on the time-varying characteristics of the wireless channel. In this article, we introduce a novel mission reliability and mission effective capacity metric that takes these phenomena medium into account, while specifically studying multiconnectivity (MC)-enabled industrial radio systems. We assume uplink short packet transmission with no channel state information at URLLC user (the transmitter) and sporadic traffic arrival. Moreover, we leverage the existing framework of dependability theory and provide closed-form expressions (CFEs) for the mission reliability of the MC system using the maximal-ratio combining scheme. We do so by utilizing the mean time to first failure, which is the expected time of failure occurring for the first time. Moreover, we also derive exact CFEs for second-order statistics, such as level crossing rate and average fade duration, showing how fades are distributed in fading channels with respect to time. Furthermore, the design throughput maximization problem under the mission reliability constraint is solved numerically through the cross-entropy method.

Weighted sum-rate maximization for MISO downlink cellular networks via branch and bound

The problem of weighted sum-rate maximization (WSRMax) in multicell downlink multi-input single-output (MISO) systems is considered. The problem is known to be NP-hard. We propose a solution method, based on branch and bound technique, which solves globally the nonconvex WSRMax problem with an optimality certificate. Specifically, the algorithm computes a sequence of asymptotically tight upper and lower bounds and it terminates when the difference between the upper and lower bound is smaller than a pre-specified tolerance. Novel bounding techniques via conic optimization are introduced and their efficiency is demonstrated via numerical simulations. The proposed method can be used to provide performance benchmarks by back-substituting it into many existing network design problems which relies on solving WSRMax problem. The method proposed here is not restricted to WSRMax; it can also be used to maximize any system performance metric that can be expressed as a Lipschitz continuous and increasing function of signal-to-interference-plus-noise ratio.

Energy efficient power control and beamforming in multi-antenna enabled femtocells

Energy efficient beamforming and power control problem is considered for a MISO (multiple input single output) network. We consider an active femtocell within the coverage area of a macrocell. The femto base station is equipped with multi antennas and the users are considered to be single antenna users. A beamforming and power control problem is formulated in order to minimize the energy consumption per bit in the downlink transmission in the femtocell. The objective function is introduced as "sum power/sum rate" which has the unit J/bit to measure the energy efficiency of the network. The problem is non-convex. We introduce a novel method to solve this problem with an approximation. We show that the problem can be solved with convex optimization techniques which has more practical interest even though the solution is suboptimal. In order to measure the energy efficiency we apply an existing power model which considers the total energy consumption of a base station. Thus we expect that our solution indicates realistic energy consumption measurements. Then we introduce a beamforming and power control algorithm which minimizes the energy consumption per bit transmission. Finally, the behavior of the objective function is observed in different antenna configurations by varying the user density in different channel environments.

Joint Load Balancing and Interference Mitigation in 5G Heterogeneous Networks

We study the problem of joint load balancing and interference mitigation in heterogeneous networks (HetNets) in which massive multiple-input multiple-output (MIMO) macro cell base station (BS) equipped with a large number of antennas, overlaid with wireless self-backhauled small cells (SCs) are assumed. Self-backhauled SC BSs with full-duplex communication employing regular antenna arrays serve both macro users and SC users by using the wireless backhaul from macro BS in the same frequency band. We formulate the joint load balancing and interference mitigation problem as a network utility maximization subject to wireless backhaul constraints. Subsequently, leveraging the framework of stochastic optimization, the problem is decoupled into dynamic scheduling of macro cell users, backhaul provisioning of SCs, and offloading macro cell users to SCs as a function of interference and backhaul links. Via numerical results, we show the performance gains of our proposed framework under the impact of small cells density, number of base station antennas, and transmit power levels at low and high frequency bands. We further provide insights into the performance analysis and convergence of the proposed framework. The numerical results show that the proposed user association algorithm outperforms other baselines. Interestingly, we find that even at lower frequency band the performance of open access small cell is close to that of closed access at some operating points, the open access full- duplex small cell still yields higher gain as compared to the closed access at higher frequency bands. With increasing the small cell density or the wireless backhaul quality, the open access full- duplex small cells outperform and achieve a 5.6x gain in terms of cell-edge performance as compared to the closed access ones in ultra-dense networks with 350 small cell base stations per km2 .

Deep Neural Network-Based Blind Multiple User Detection for Grant-free Multi-User Shared Access

Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden nodes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable of distinguishing the active devices of the received signal without any a priori knowledge of the device sparsity level and the channel state information. Our numerical evaluation shows that with a higher percentage of active devices, the DNN-MUD achieves a significantly increased probability of detection compared to the conventional approaches.

On the Joint Impact of Beamwidth and Orientation Error on Throughput in Wireless Directional Poisson Networks.

Beyond Diagonal Reconfigurable Intelligent Surfaces for Multi-Carrier RF Wireless Power Transfer

Traffic aware pilot de-contamination for multi-cell MIMO systems

Traffic aware precoder/decoder design in multi-cell multi-user multiple-input multiple-output systems is considered with the objective of weighted queue minimization, where the original non-convex optimization problem is solved via successive convex approximation. Centralized pilot reuse algorithms for mitigating the pilot contamination are investigated to reflect the traffic aware optimization objective. Distinctive feature of the proposed pilot reuse algorithms is to utilize the user buffer state information jointly with the traditional large scale fading values when allocating the limited pilot resources among the served users. Numerical examples compare the performance of the proposed pilot reuse algorithms for varying number of available pilots and different traffic arrival models. The results demonstrate that significant performance gains are available when the pilot allocation strategy is designed to reflect closely the overall system optimization objective.

Ultra-wide band sensor networks in oil and gas explorations

Seismic exploration and monitoring for oil and gas reservoirs is a peculiar application that requires a large number (1000-2000 nodes/sqkm) of geophone sensors deployed outdoors over large areas (≥ 40 sqkm) to measure backscattered wave fields from artificial sources. A storage/processing unit or sink node collects the measurements from all the geophones to obtain an image of the sub-surface. The existing cabling system to connect sensors is known to cause inefficiencies, large logistic and weight costs, as well as insufficient flexibility in survey design. Oil companies are therefore expecting that wireless connectivity will provide the enabling technology for future seismic explorations. This application represents a new challenging research area for the wireless community. Early results suggest that current off-the-shelf radio solutions do not guarantee the minimum requirements in terms of system usability and energy consumption, not even for current deployment size. This article presents a tutorial view to introduce the basic principles of seismic acquisition systems that are necessary to define the wireless geophone network specifications. Strict sampling synchronization constraint over large geographic areas, high precision sensor localization, and high data rate are all requirements calling for a scalable network system where Ultra-Wide Band radio transmissions play a key role as the only viable technology.

Spatial transmit diversity techniques for MC-CDMA systems

Transmit diversity is an effective technique to improve the performance of wireless communication systems. We investigate different transmit diversity schemes for multicarrier code-division multiple-access (MC-CDMA) systems. More specifically we compare the performance of different transmit diversity methods, including delay diversity, phase diversity, code diversity, scrambling code diversity and space-time block coding diversity for MC-CDMA. All the signal processing is carried out in the frequency domain. The impacts of the system load on the bit error rate performance is presented in correlated fading channels. The performance of multiple transmit antennas and multiple receive antennas (MIMO) MC-CDMA is also considered. Based on the simulation results, the space-time block coding method achieves the best performance. All transmit diversity schemes perform worse in the heavily loaded system than no transmit diversity system. Based on this, we propose a switched transmit diversity method in which the transmit diversity methods are changed according to the system load.

Key drivers and research challenges for 6G ubiquitous wireless intelligence

As fifth generation (5G) research is maturing towards a global standard, the research community has started to focus on the development of beyond-5G solutions and the 2030 era, i.e. 6G. In the future, our society will be increasingly digitised, hyper-connected and globally data driven. Many widely anticipated future services will be critically dependent on instant, virtually unlimited wireless connectivity. Mobile communication technologies are expected to progress far beyond anything seen so far in wireless-enabled applications, making everyday lives smoother and safer while dramatically improving the efficiency of businesses. 6G is not only about moving data around — it will become a framework of services, including communication services where all user-specific computation and intelligence may move to the edge cloud. The white paper presents key drivers, research requirements, challenges and essential research questions related to 6G. The focus is on societal and business drivers; use cases and new device forms; spectrum and key performance indicator targets; radio hardware progress and challenges; physical layer; networking; and new service enablers. Societal megatrends, United Nations’ sustainability goals, lowering carbon dioxide emissions, emerging new technical enablers as well as ever increasing productivity demands are introduced as critical drivers towards 2030 solutions. This white paper is the first in a series of 6G Research Visions based on the views that 70 invited experts shared during a special workshop at the first 6G Wireless Summit in Finnish Lapland in March 2019.