arXiv subjects

Ivan Stupia

Publications and source records attributed to Ivan Stupia.

12 recordsLinked to original sources

arXiv2022

Real-Time CRLB based Antenna Selection in Planar Antenna Arrays

Estimation of User Terminals' (UTs') Angle of Arrival (AoA) plays a significant role in the next generation of wireless systems. Due to high demands, energy efficiency concerns, and scarcity of available resources, it is pivotal how these resources are used. Installed antennas and their corresponding hardware at the Base Station (BS) are of these resources. In this paper, we address the problem of antenna selection to minimize Cramer-Rao Lower Bound (CRLB) of a planar antenna array when fewer antennas than total available antennas have to be used for a UT. First, the optimal antenna selection strategy to minimize the expected CRLB is proposed. Then, using this strategy as a preliminary step, we present a two-stage greedy antenna selection method whose goal is to minimize the instantaneous CRLB. The optimal start point of the greedy algorithm is presented alongside some methods to reduce the algorithm's computational complexity. Numerical results confirm the accuracy of proposed solutions. They demonstrate that the proposed antenna selection method only requires a small proportion of the total available antennas to accomplish a significant amount of the total performance, enhancing hardware utilization efficiency. Also, it is shown that the presented algorithm has a high error tolerance.

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arXiv2021

Energy Efficient Competitive Resource Allocation in MIMO networks

This paper considers the competitive resource allocation problem in Multiple-Input Multiple-Output (MIMO) interfering channels, when users maximize their energy efficiency. Considering each transmitter-receiver pair as a selfish player, conditions on the existence and uniqueness of the Nash equilibrium of the underlying noncooperative game are obtained. A decentralized asynchronous algorithm is proven to converge towards this equilibrium under the same conditions. Two frameworks are considered for the analysis of this game. On the one hand, the game is rephrased as a Quasi-Variational Inequality (QVI). On the other hand, the best response of the players is analyzed in light of the contraction mappings. For this problem, the contraction approach is shown to lead to tighter results than the QVI one. When specializing the obtained results to OFDM networks, the obtained conditions appear to significantly outperform state-of-the-art works, and to lead to much simpler decentralized algorithms. Numerical results finally assess the obtained conditions in different settings.

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arXiv2021

Localization Efficiency in Massive MIMO Systems

In the next generation of wireless systems, Massive MIMO offers high angular resolution for localization. By virtue of large number of antennas, the Angle of Arrival (AoA) of User Terminals (UTs) can be estimated with high accuracy. According to Dense Multipath Component (DMC) channel model, local scatters around UTs can create different multipath signals for each antenna at the Base Station (BS). We obtain a deterministic form for the Cramer-Rao Lower Bound (CRLB) in a multi-user scenario when the contribution of the multipath signals is considered. We do this when the multipath signals are independent and identically distributed (i.i.d) with arbitrary distribution. Then, we redefine a localization efficiency function for a multi-user scenario and numerically optimize it with respect to (w.r.t) the number of antennas. We prove when only a subset of the available antennas is used, CRLB can be minimized w.r.t which set of antennas is used. Then, an antenna selection strategy that minimizes CRLB is proposed. As a benchmark, we apply the proposed antenna selection scheme to the MUltiple SIgnal Classification (MUSIC) algorithm and study its efficiency. Numerical results validate the accuracy of our analysis and show significant improvement in efficiency when the proposed antenna selection strategy is employed.

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arXiv2020

Leveraging User-Diversity in Energy-Efficient Edge-Facilitated Collaborative Fog Computing

Motivated by applications such as on-device collaborative neural network inference, this work investigates edge-facilitated collaborative fog computing - in which edge-devices collaborate with each other and with the edge of the network to complete a processing task - to augment the computing capabilities of individual edge-devices while optimizing the collaboration for energy-efficiency. Collaborative computing is modeled using the Map-Reduce distributed computing framework, consisting in two rounds of computations separated by a communication phase. The computing load is optimally distributed among the edge-devices, taking into account their diversity in term of computing and communications capabilities. In addition, edge-devices local parameters such as CPU clock frequency and RF transmit power are also optimized for energy-efficiency. The corresponding optimization problem can be shown to be convex and optimality conditions can be obtained through Lagrange duality theory. A waterfilling-like interpretation for the size of the computing load assigned to each edge-device is given. Numerical experiments demonstrate the benefits of the proposed optimal collaborative-computing scheme over various other schemes in several respects. Most notably, the proposed scheme exhibits increased probability of successfully dealing with heavier computations and/or smaller latency along with energy-efficiency gains of up to two orders of magnitude. Both improvements come from the scheme ability to optimally leverage edge-devices diversity.

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arXiv2020

Performance Analysis of Uplink Adaptive NOMA Depending on Channel Knowledge

Non Orthogonal Multiple Access (NOMA) is a key technique to satisfy large users densities in future wireless networks. However, NOMA may provide poor performance compared to Orthogonal Multiple Access (OMA) due to inter-user interference. In this paper, we obtain closed-form expressions of the uplink NOMA and OMA throughputs when no Channel State Information at Transmitter (CSIT) is available, and of the average data rates assuming that instantaneous rates should be larger than a minimum threshold when full CSIT is available. Analytical comparisons of OMA and NOMA prove that there is no global dominant strategy valid in all situations. Based on this conclusion, we propose a new multiple-access (MA) strategy called NOMA-Adaptive (NOMA-A) that selects the best MA technique between OMA and NOMA. NOMA-A aims at maximizing the sum throughput in the no CSIT case, and the probability that both users are active in the full CSIT case. NOMA-A is shown to outperform the other strategies in terms of sum throughput and rate.

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arXiv2019

Energy-Efficient Edge-Facilitated Wireless Collaborative Computing using Map-Reduce

In this work, a heterogeneous set of wireless devices sharing a common access point collaborates to perform a set of tasks. Using the Map-Reduce distributed computing framework, the tasks are optimally distributed amongst the nodes with the objective of minimizing the total energy consumption of the nodes while satisfying a latency constraint. The derived optimal collaborative-computing scheme takes into account both the computing capabilities of the nodes and the strength of their communication links. Numerical simulations illustrate the benefits of the proposed optimal collaborative-computing scheme over a blind collaborative-computing scheme and the non-collaborative scenario, both in term of energy savings and achievable latency. The proposed optimal scheme also exhibits the interesting feature of allowing to trade energy for latency, and vice versa.

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arXiv2018

SWIPT-based Real-Time Mobile Computing Systems: A Stochastic Geometry Perspective

Driven by the Internet of Things vision, recent years have seen the rise of new horizons for the wireless ecosystem in which a very large number of mobile low power devices interact to run sophisticated applications. The main hindrance to the massive deployment of low power nodes is most probably the prohibitive maintenance cost of battery replacement and the ecotoxicity of the battery production/end-of-life. An emerging research direction to avoid battery replacement is the combination of radio frequency energy harvesting and mobile computing (MC). In this paper, we propose the use of simultaneous information and power transfer (SWIPT) to control the distributed computation process while delivering power to perform the computation tasks requested. A real-time MC system is considered, meaning that the trade-off between the information rate and the energy harvested must be carefully chosen to guarantee that the CPU may perform tasks of given complexity before receiving a new control signal. In order to provide a system-level perspective on the performance of SWIPT-MC networks, we propose a mathematical framework based on stochastic geometry to characterise the rate-energy trade-off of the system. The resulting achievable performance region is then put in relation with the CPU energy consumption to investigate the operating conditions of real-time computing systems. Finally, numerical results illustrate the joint effect of the network densification and the propagation environment on the optimisation of the CPU usage.

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arXiv2018

On the effect of blockage objects in dense MIMO SWIPT networks

Simultaneous information and power transfer (SWIPT) is characterised by the ambiguous role of multi-user interference. In short, the beneficial effect of multi-user interference on RF energy harvesting is obtained at the price of a reduced link capacity, thus originating nontrivial trade-offs between the achievable information rate and the harvestable energy. Arguably, in indoor environments, this trade-off might be affected by the propagation loss due to blockage objects like walls. Hence, a couple of fundamental questions arise. How much must the network elements be densified to counteract the blockage attenuation? Is blockage always detrimental on the achievable rate-energy trade-off? In this paper, we analyse the performance of an indoor multiple-input multiple-output (MIMO) SWIPT-enabled network in the attempt to shed a light of those questions. The effects of the obstacles are examined with the help of a stochastic approach in which energy transmitters (also referred to as power heads) are located by using a Poisson Point Process and walls are generated through a Manhattan Poisson Line Process. The stochastic behaviour of the signal attenuation and the multi-user interference is studied to obtain the Joint Complementary Cumulative Distribution Function (J-CCDF) of information rate and harvested power. Theoretical results are validated through Monte Carlo simulations. Eventually, the rate-energy trade-off is presented as a function of the frequency of walls to emphasise the cross-dependences between the deployment of the network elements and the topology of the venue.

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arXiv2017

Optimal Resource Allocation in Ultra-low Power Fog-computing SWIPT-based Networks

In this paper, we consider a fog computing system consisting of a multi-antenna access point (AP), an ultra-low power (ULP) single antenna device and a fog server. The ULP device is assumed to be capable of both energy harvesting (EH) and information decoding (ID) using a time-switching simultaneous wireless information and power transfer (SWIPT) scheme. The ULP device deploys the harvested energy for ID and either local computing or offloading the computations to the fog server depending on which strategy is most energy efficient. In this scenario, we optimize the time slots devoted to EH, ID and local computation as well as the time slot and power required for the offloading to minimize the energy cost of the ULP device. Numerical results are provided to study the effectiveness of the optimized fog computing system and the relevant challenges.

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arXiv2016

Joint MOO of Transmit Precoding and Receiver Design in a Downlink Time Switching MISO SWIPT System

In this paper, we consider a time-switching (TS) co-located simultaneous wireless information and power transfer (SWIPT) system consisting of multiple multi-antenna access points which serve multiple single antenna users. In this scenario, we propose a multi-objective optimization (MOO) framework to design jointly the Pareto optimal beamforming vector and the TS ratio for each receiver. The objective is to maximize the utility vector including the achieved data rates and the harvested energies of all users simultaneously. This problem is a non-convex rank-constrained MOO problem which is relaxed and transformed into a non-convex semidefinite program (SDP) based on the weighted Chebycheff method. The majorization-minimization algorithm is utilized to solve the nonconvex SDP and the optimal solution is proved to satisfy the rank constraint. We also study the problem of optimizing the beamforming vectors in a fixed TS ratio scenario with the same approach. Numerical results are provided for two coordinated access points with MISO configuration. The results illustrate the trade-off between harvested energy and information data rate objectives and show the effect of optimizing the precoding strategy and TS ratio on this trade-off.

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arXiv2015

Power Control in Networks With Heterogeneous Users: A Quasi-Variational Inequality Approach

This work deals with the power allocation problem in a multipoint-to-multipoint network, which is heterogenous in the sense that each transmit and receiver pair can arbitrarily choose whether to selfishly maximize its own rate or energy efficiency. This is achieved by modeling the transmit and receiver pairs as rational players that engage in a non-cooperative game in which the utility function changes according to each player's nature. The underlying game is reformulated as a quasi variational inequality (QVI) problem using convex fractional program theory. The equivalence between the QVI and the non-cooperative game provides us with all the mathematical tools to study the uniqueness of its Nash equilibrium (NE) points and to derive novel algorithms that allow the network to converge to these points in an iterative manner both with and without the need for a centralized processing. A small-cell network is considered as a possible case study of this heterogeneous scenario. Numerical results are used to validate the proposed solutions in different operating conditions.

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arXiv2015

Energy Efficient Precoder Design for MIMO-OFDM with Rate-dependent Circuit Power

This paper studies an energy efficient design of precoders for point-to-point multiple-input-multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. Differently from traditional approaches, the optimal power allocation strategy is studied by modelling the circuit power as a rate-dependent function. We show that if the circuit power is a constant plus an increasing and convex function of the transmission rate, the problem of minimizing the consumed energy per bit received can be reformulated as a convex fractional program and solved by means of a bisection algorithm. The impact of the some system parameters is investigated either analytically or by means of computational results.

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