Using Information Theoretic Techniques for Sinusoidal Signal Resolution

The objective is to develop information theoT"€tic criteria for detection of sinusoidal signals. The minimum description length (MDL) and the predictive stochastic complexity (PSC) have been formulated for harmonic resolution. MDL and PSC QI" € the codelength for data and model. The proposed techniques are based on decomposing the observation vector into its components in the signal and noise subspaces. Each component is encoded separately and the results are added to form the total codelength. The codelength is minimized over different models to select the best model. Sinusoidal signal detection is applied in various fields ranging from telecommunications to arra

Effectiveness of Reconfigurable Intelligent Surfaces to Enhance Connectivity in UAV Networks

Reconfigurable intelligent surfaces (RISs) are expected to make future 6G networks more connected and resilient against node failures, due to their ability to introduce controllable phase-shifts onto impinging electromagnetic waves and impose link redundancy. Meanwhile, unmanned aerial vehicles (UAVs) are prone to failure due to limited energy, random failures, or targeted failures, which causes network disintegration that results in information delivery loss. In this paper, we show that the integration between UAVs and RISs for improving network connectivity is crucial. We utilize RISs to provide path diversity and alternative connectivity options for information flow from user equipments (UEs) to less critical UAVs by adding more links to the network, thereby making the network more resilient and connected. To that end, we first define the criticality of UAV 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. To tackle this problem, we propose a relaxation method such that the discrete scheduling constraint of the problem is relaxed and becomes continuous. Leveraging this, we propose two efficient solutions, namely semi-definite programming (SDP) optimization and perturbation heuristic, which both solve the problem in polynomial time. For the perturbation heuristic, we derive the lower and upper bounds of the algebraic connectivity obtained by adding new links to the network. Finally, we corroborate the effectiveness of the proposed solutions through extensive simulation experiments.Comment: 14 pages, 8 figures, journal paper. arXiv admin note: text overlap with arXiv:2308.0467

Energy Efficient Communications in RIS-assisted UAV Networks Based on Genetic Algorithm

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 non-linear 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

Cross-layer modelling for efficient transmission of non-realtime data traffic over downlink DS-CDMA heterogenous networks

In this paper, we develop a cross-layer model for downlink interference in heterogenous DS-CDMA wireless cellular networks. In this model, interference is described as a function of application layer parameters (traffic characteristics) and physical layer variations (channel characteristics). We show that for a heterogenous service DS-CDMA network, downlink interference is a second-order self-similar process and thus has long-range dependence. We then use the predictive structure of total downlink interference to maximize non-realtime data throughput. We use fractional Gaussian noise (fGn) to model the self-similarity of downlink interference. In the proposed method, the base-station uses an optimal linear predictor, based on the fGn model, to estimate the level of interference. The estimated interference is then used to allocate power to users. To maximize data throughput, we use time domain scheduling. The simulation studies confirm the self-similarity of downlink interference and validate the fGn model. The simulation results also show a substantial performance improvement using the proposed predictive-adaptive scheme and confirm that the interference model is still valid after applying the proposed method