Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

Zappone, Alessio; Björnson, Emil; Sanguinetti, Luca; Jorswieck, Eduard A.

doi:10.1109/tsp.2017.2673813

Cited by 234 publications

(229 citation statements)

References 51 publications

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“…We can solve the QCQP within a polynomial complex for a given N entry using standard convex optimization solvers such as CVX [38]. We can also solve (14a) via the sequential convex optimization of (15a) through iteratively updating the local point {q j [n], v j [n]} as in [28], [39], [40]. Note that the sequential convex optimization method has been proven to converge to at least one local optimal point [28].…”

Section: A Energy Minimization During Entry To the Target Boundarymentioning

confidence: 99%

A UAV-Mounted Free Space Optical Communication: Trajectory Optimization for Flight Time

Lee

Park

et al. 2020

IEEE Trans. Wireless Commun.

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In this work, we address the trajectory optimization of a fixed-wing unmanned aerial vehicle (UAV) using free space optical communication (FSOC). Here, we focus on maximizing the flight time of the UAV by considering practical constraints for wireless UAV communication, including limited propulsion energy and required data rates. We find optimized trajectories in various atmospheric environments (e.g., moderate-fog and heavy-fog conditions), while also considering the channel characteristics of FSOC. In addition to maximizing the flight time, we consider the energy efficiency maximization and operation-time minimization problem to find the suboptimal solutions required to meet those constraints.Furthermore, we introduce a low-complexity approach to the proposed framework. In order to address the optimization problem, we conduct a bisection method and sequential programming and introduce a new feasibility check algorithm. Although our design considers suboptimal solutions owing to the nonconvexity of the problems, our simulations indicate that the proposed scheme exhibits a gain of approximately 44.12% in terms of service time when compared to the conventional scheme. Free space optical communication (FSOC), wireless communications with an unmanned aerial vehicle (UAV), UAV-mounted FSOC, flight time maximization, trajectory design. I. INTRODUCTION Recently, unmanned aerial vehicles (UAVs) have attracted a great deal of attention in the area of wireless communication networks. Owing to their mobility and flexibility, UAVs can be dispatched as a mobile entity in a network. It can provide new opportunities in various communication applications and have the capability to complement conventional fixed networks. UAV-assisted communications can efficiently support already existing terrestrial communication infrastructure, including data offloading at a hot spot [1], [2]. For example, in case of a catastrophic event, UAV-mounted infrastructures can temporarily support service recovery initiatives and local interim communication facilities for potentially damaged infrastructures [3]. In addition, UAV-assisted relaying can help to extend base station connectivity, from one station to another, in situations where the nodes are widely scattered and/or obstacles such as hills or large buildings are present [4].Principally, we envision that most future networks will be highly user-centric, increasing the user demand information due to an exponential increase in the internet of things (IoT), fifth-generation (5G) mobile networks, and beyond-5G wireless networks. Accordingly, UAVmounted fronthaul and backhaul frameworks have been considered as a promising approach to handle unexpected or temporarily large amounts of information that is commonly required by user terminals [5], [6]. By leveraging the UAV-mounted fronthaul and backhaul links, large-scale projects are now underway in several research groups and information technology companies,including Global Access to the Internet for All (GAIA) [7], Project Loon by Google [8], and...

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Section: A Energy Minimization During Entry To the Target Boundarymentioning

confidence: 99%

A UAV-Mounted Free Space Optical Communication: Trajectory Optimization for Flight Time

Lee

Park

et al. 2020

IEEE Trans. Wireless Commun.

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“…Remark Algorithm 1 can be executed within polynomial‐time complexity and has a linear convergence rate . This is due to the fact that the update rule follows Newton's method, when applied to the auxiliary function F ( λ q , k , ς ) …”

Section: Max‐min Energy‐efficient Power Allocationmentioning

confidence: 99%

Joint relay selection and max‐min energy‐efficient power allocation in downlink multicell NOMA networks: A matching‐theoretic approach

Baidas

Bahbahani

El-Sharkawi

et al. 2019

Trans Emerging Tel Tech

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In this paper, the problem of joint relay selection and max‐min energy‐efficient power allocation (J‐RS‐MMEE‐PA) in downlink multicell nonorthogonal multiple access (NOMA) networks is studied. In particular, the goal is to perform relay selection for each cellular user within each cell, so as to achieve max‐min energy efficiency while satisfying quality‐of‐service (QoS) constraints. However, the formulated J‐RS‐MMEE‐PA problem happens to be nonconvex (ie, computationally intensive). In turn, a solution procedure for max‐min energy‐efficient power allocation is devised to determine the energy efficiency of each user per potential relay while meeting the target minimum rate per user. After that, the relay selection problem is modeled as a student‐project allocation with preferences over projects matching problem. A polynomial‐time complexity stable matching (SM) algorithm is proposed, which takes into account the maximum number of users that can select a relay as well as the number of users that can be associated with a base station. However, the proposed SM algorithm can only guarantee an SM size that is at least half the size of the maximum “optimal” cardinality SM. Thus, the improved SM (I‐SM) algorithm with polynomial‐time complexity is devised, so as to guarantee an SM size that is at least two‐thirds of that of the optimal matching. Lastly, simulation results are presented to compare the proposed matching algorithms to the J‐RS‐MMEE‐PA scheme, where it has been shown that the I‐SM algorithm is superior to its SM counterpart and yields comparable energy efficiency per cellular user to the J‐RS‐MMEE‐PA scheme while satisfying QoS constraints.

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“…In order to obtain such global solutions, researchers have applied methods from the field of monotonic optimization (e.g., [25]- [28]) to optimization problems in various communication systems. For instance, monotonic programming was applied in interference channels [7], [29]- [36], in broadcast channels with linear transceivers [11], [37]- [40], in interfering broadcast channels [41], in relaying scenarios [23], [34], and in satellite systems [42], with the aim of maximizing weighted sum rates [7], [29]- [31], [33], fairness-based performance metrics [32], [33], [36]- [38], [41], or the energy efficiency [23], [34], [35], [40] as well as minimizing the required sum transmit power [11], [39], [42]. Some of these applications include solutions for multiantenna systems [7], [11], [29], [30], [36]- [39], [41], allow to average data rates over several time slots [32], [33], [36], [37], [39], and/or incorporate additional robustness considerations [41].…”

Section: Introductionmentioning

confidence: 99%

Mixed Monotonic Programming for Fast Global Optimization

Matthiesen

Jorswieck

Utschick

2020

IEEE Trans. Signal Process.

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While globally optimal solutions to convex programs can be computed efficiently in polynomial time, this is, in general, not possible for nonconvex optimization problems. Therefore, locally optimal approaches or other efficient suboptimal heuristics are usually applied for practical implementations. However, there is also a strong interest in computing globally optimal solutions of nonconvex problems in offline simulations in order to benchmark the faster suboptimal algorithms. Global solutions often rely on monotonicity properties. A common approach is to reformulate problems into a canonical form of a monotonic optimization problem, where the monotonicity becomes evident, but this often comes at the cost of nested optimizations, increased numbers of variables, and/or slow convergence. The framework of mixed monotonic programming (MMP) proposed in this paper is a more direct approach that exploits hidden monotonicity properties without performance-deteriorating reformulations. By means of a wide range of application examples from the area of signal processing for communications (including energy efficiency for green communications, resource allocation in interference networks, scheduling for fairness and quality of service, as well as beamformer design in multiantenna systems), we demonstrate that the novel MMP approach leads to tremendous complexity reductions compared to state-of-the-art methods for global optimization.Index Terms-Resource allocation, global optimization, interference networks, monotonic optimization, branch-and-bound 1 E.g., in multiple-input/multiple-output (MIMO) broadcast channels with dirty paper coding [6], or for rate balancing problems in multiple-input/single-output (MISO) interference channels with interference treated as noise [7].

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Globally Optimal Energy-Efficient Power Control and Receiver Design in Wireless Networks

Cited by 234 publications

References 51 publications

A UAV-Mounted Free Space Optical Communication: Trajectory Optimization for Flight Time

A UAV-Mounted Free Space Optical Communication: Trajectory Optimization for Flight Time

Joint relay selection and max‐min energy‐efficient power allocation in downlink multicell NOMA networks: A matching‐theoretic approach

Mixed Monotonic Programming for Fast Global Optimization

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