This paper proposes to integrate reconfigurable intelligent surfaces (RISs) with unmanned aerial vehicles (UAVs) as a resilience mechanism to mitigate outages in UAV networks due to UAV and link failures. The inherent addition of RIS-aided links (UE-RIS-UAV links), combined with their reconfigurability, creates alternative paths for user equipment (UEs) to transmit signals to UAVs. The paper studies the problem of maximizing connectivity of UAV networks by jointly considering UE positioning, RIS-aided link selection, and phase shift design of RISs. To tackle it, we propose an efficient two-step solution. In the first step, we propose a supergradient method that locates the UEs in positions that improve their communication links until a certain connectivity threshold is satisfied. Given the optimized UE positioning, the second step jointly optimizes the RIS-aided link selection and RIS phase shift design using semidefinite programming (SDP). Through simulations, we illustrate the superiority of the proposed solution compared to the solutions available in the literature.

Reconfigurable intelligent surfaces (RISs) have drawn considerable attention due to their ability to introduce controllable phase-shifts onto impinging electromagnetic waves and impose link redundancy. Meanwhile, unmanned aerial vehicles (UAVs) are expected to make future 6G networks more connected, but they are prone to several failures, which cause network disintegration. To harness the benefits of both, we study their integration to improve connectivity of multi-RIS-assisted UAV networks. We first propose to define the criticality of nodes, which reflects the importance of some nodes over other nodes. We then employ the algebraic connectivity metric, which is adjusted by the reflected links of the RISs and their criticality weights, to formulate the problem of maximizing the network connectivity. Such problem is a computationally expensive combinatorial optimization. Using a relaxation method where the discrete scheduling constraint of the problem is relaxed to be continuous, we propose two efficient solutions, namely semi-definite programming (SDP) optimization and Laplacian matrix perturbation, which both solve the problem in polynomial time. We rigorously derive the lower and upper bounds of the algebraic connectivity obtained from the perturbation solution. Simulation results compare the performance of the proposed solutions with different schemes, including without RISs, unoptimized link scheduling and phase shifts, greedy search, and optimal. The results show that the proposed schemes achieve considerably improved performance with low computational complexity compared to other schemes.

It is anticipated that integrating unmanned aerial vehicles (UAVs) with reconfigurable intelligent surfaces (RISs), resulting in RIS-assisted UAV networks, will offer improved network connectivity against node failures for the beyond 5G networks. In this context, we utilize a RIS to provide path diversity and alternative connectivity options for information flow from user equipment (UE) to UAVs by adding more links to the network, thereby maximizing its connectivity. This paper employs the algebraic connectivity metric, which is adjusted by the reflected links of the RIS, to formulate the problem of maximizing the network connectivity in two cases. First, we consider formulating the problem for one UE, which is solved optimally using a linear search. Then, we consider the problem of a more general case of multiple UEs, which has high computational complexity. To tackle this problem, we formulate the problem of maximizing the network connectivity as a semi-definite programming (SDP) optimization problem that can be solved efficiently in polynomial time. In both cases, our proposed solutions find the best combination between UE(s) and UAVs through the RIS. As a result, it tunes the phase shifts of the RIS to direct the signals of the UEs to the appropriate UAVs, thus maximizing the network connectivity. Simulation results are conducted to assess the performance of the proposed solutions compared to the existing solutions.

This paper proposes a solution for energy-efficient communication in reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV) networks. The limited battery life of UAVs is a major concern for their sustainable operation, and RIS has emerged as a promising solution to reducing the energy consumption of communication systems. The paper formulates the problem of maximizing the energy efficiency of the network as a mixed integer nonlinear program, in which UAV placement, UAV beamforming, On-Off strategy of RIS elements, and phase shift of RIS elements are optimized. The proposed solution utilizes the block coordinate descent approach and a combination of continuous and binary genetic algorithms. Moreover, for optimizing the UAV placement, Adam optimizer is used. The simulation results show that the proposed solution outperforms the existing literature. Specifically, we compared the proposed method with the successive convex approximation (SCA) approach for optimizing the phase shift of RIS elements.

Networks are frequently changing due to new technologies. To increase the network performance, companies migrate their existing network to a network with a new technology. Finding an efficient optimization algorithm is an important challenge in the network migration. In this paper, the network migration problem is considered as a set of circuit migration problems in which multiple technicians simultaneously migrate the endpoints of circuits in order to minimize the average latency and average technician travel cost. While average latency indicates how fast the sites can be upgraded, average travel cost estimates the required cost for modernizing the network. First, We derive binary linear program and binary quadratic program formulations for average latency and average technician travel cost, respectively. Then we use the linear scalarization method to obtain a multi-objective optimization problem for simultaneously minimizing both costs. Our approach for solving the derived multi-objective optimization problem is based on converting it to a quadratic unconstrained binary optimization problem (QUBO) using the penalty method. Subsequently, we exploit the third generation of Fujitsu Digital Annealer which is a hybrid system of hardware and software to minimize the derived QUBO. To investigate the performance of our proposed method, we study extensive network migration instances on the 75-node CONUS network topology. Simulation results indicate that both costs can efficiently be optimized using our proposed method. We also directly solve the obtained multi-objective optimization problem with Gurobi solver. The comparison results show that our proposed method outperforms the Gurobi solver.

Networks are frequently changing due to new technologies. The growing demand for bandwidth is forcing many carriers to migrate their existing network to a network with a new technology in order to increase network performance. Telecommunication companies are looking for optimization algorithms to efficiently manage their network migration. In this paper, the network migration problem is considered as a set of circuit migration problems in which two technicians simultaneously migrate the two ends of a circuit in order to minimize the total accumulated sites in-service and total technician travels. While total accumulated sites in-service indicates how fast the sites can be upgraded during the migration process, total technician travels estimates the required cost. We first formulate our target problem as a constrained binary quadratic program which is NP-hard in general. Our approach for solving the derived optimization problem is based on converting it to a quadratic unconstrained binary optimization problem (QUBO) using the penalty method. Subsequently, we exploit Digital Annealer which is a massively parallel hardware architecture to minimize the derived QUBO. To evaluate our proposed method, we study extensive network migration instances on the 75-node CONUS network topology.

Resampling increasing the variance of the tracking algorithm in Particle Filtering (PF). Instead of utilizing resampling procedures that rely on asymptotic convergence properties, we show that intelligently selecting and replicating a set of samples can better represent the posterior approximation and improve the overall performance of the PF. To this end, we formulate the resampling procedure as an integer program that minimizes an upper bound on the Kullback-Leibler divergence (KLD) between the resampled distribution and the posterior approximation. We then transform the problem into an Ising energy minimization problem, which we are able to efficiently solve. Applying our novel paradigm to a challenging sequential importance resampling (SIR) simulation shows faster convergence over the number of resampled particles and a 35% improvement in the median KLD for a fixed number of particles.

Macro base stations are densely overlaid by small cells to satisfy the demands of user equipment in heterogeneous networks. Due to their dense deployment, some small cells are not directly connected to macro base stations and thus backhaul connections are required to connect small cells to macro base stations. Millimeter wave backhauls which have high bandwidths are preferred for small cell backhaul communication, since they can increase the capacity of network considerably. In this context, association of user equipment to base stations becomes challenging due to the backhaul architecture. Considering environmental concerns, energy efficiency is a vital criterion in designing user association algorithms. In this paper, we study the user association problem aiming at the maximization of energy efficiency. We develop centralized and distributed user association algorithms based on sequentially minimizing the power consumption. We evaluate the performance of the proposed algorithms under two scenarios and show that they achieve higher energy efficiency compared to the existing algorithms in the literature, while maintaining high spectral efficiency and backhaul load balancing.

Macro base stations are densely overlaid by small cells to satisfy the demands of user equipments in heterogeneous networks. Due to their dense deployment, some small cells are not directly connected to macro base stations and thus backhaul connections are required to connect small cells to macro base stations. Millimeter wave backhauls which have high bandwidths are preferred for small cell backhaul communication, since they can increase the capacity of network considerably. In this context, association of user equipments to base stations becomes challenging due to the backhaul architecture. Considering environmental concerns, energy efficiency is a vital criterion in designing the user association algorithms. In this paper, we study the user association problem aiming at the maximization of energy efficiency given a specific spectral efficiency target. Firstly, we derive a quadratic upper bound on the total power consumption in heterogeneous network (access network and backhaul). Then we develop an energy and spectrum efficient user association method based on minimizing the derived quadratic upper bound, which indeed turns our target problem into a generalized quadratic assignment problem. Since this problem is NP-hard in general, we propose a heuristic algorithm for solving it. Extensive simulations show that our approach provides an enhanced level of energy efficiency compared to other well-known alternatives in the literature.

The tight lower bound on the redundancy of optimal prefix-free codes in terms of a given symbol probability (not necessarily the largest or the smallest one) has been derived in the literature. The first goal of this paper is to derive the tight lower bounds in terms of j (j > 1) known symbol probabilities of the source using some properties of Kullback-Leibler distance. Since fix-free codes are special prefix-free codes (for which no codeword is a suffix of the other codewords), it is clear that all lower bounds on the redundancy of optimal prefix-free codes are also valid for fix-free ones. Accordingly, the second question of the paper is on the tightness of the derived lower bounds for optimal fix-free codes. It is proven that these lower bounds are tight for optimal fix-free codes if j ≤ 3, and are not tight for j ≥ 4. Also, it is shown that the tight lower bound in terms of the probability of the most likely symbol is the same for optimal prefix-free and optimal fix-free codes.

Cyber attacks on the power systems can mislead the control center to produce incorrect state and topology estimate. State and topology attacks can have harmful impacts on the operation of a power system. The problem of placing secure phasor measurement units (PMUs) to detect these attacks has been studied in the literature. Specifically, it has been shown that placing secure PMUs to disable undetectable state and topology attacks can enhance the security of the system against cyber attacks. Placing secure PMUs is indeed the minimum vertex cover problem. Since the cost of deploying PMUs is high, it is important to place the secure PMUs efficiently in order to maximize the ability of detecting cyber attacks while reducing the costs. In this paper, we use Digital Annealer to solve the vertex cover problem. Digital Annealer is a hardware architecture for solving combinatorial optimization problems. We have performed numerous numerical experiments and noticed that our approach has an enhanced level of optimality compared to other well-known alternatives in the literature.

Cellular V2X, the “Vehicle to Everything” standard, defines a framework for practically feasible information exchange among vehicles and other network entities. This interaction is proved to bring in substantial economic and ecological benefits. LTE V2X uses a portion of uplink frame as a resource pool and in the main mode, relies on a central scheduler for allocating these resources to the users. As an important resource management problem, besides the optimal resource scheduling, finding a proper lower bound for required resources in this mode is NP-hard. Network management entities and service providers require this lower bound to determine the minimum size of the uplink frame slice to be allocated to the V2X resource pool. In this paper, we take advantage of Digital Annealer potentials to target this problem at a new size range with considerably enhanced level of optimality that has not been achievable so far due to computational complexities. The Digital Annealer enables us to find the minimum required size of the V2X slice in the uplink frame with high speed through our maximum clique and minimum cover graph theoretic formulations of this problem. Our approach considerably enhances performance compared to its closest alternatives in the literature.