Recognition of the modulation scheme is the intermediate step between signal detection and demodulation of the received signal in communication networks. Automatic modulation recognition (AMR) plays a central role in many applications, especially in the military and security sectors. In general, several properties of the received signal are extracted and employed for AMR. Selecting the appropriate features has a significant impact on increasing the efficiency of AMR. In this paper, we implement and compare digital modulation recognition via multi-layer perceptrons (MLP), radial basis function (RBF), adaptive neuro-fuzzy inference system (ANFIS), decision tree (DT), and naïve Bayes (NB) algorithms. In addition, the optimal parameters of each model are obtained by utilizing a genetic algorithm (GA). A series of studies are conducted in this work in order to determine the efficiency of different algorithms in identifying modulated signals with commonly used digital modulations. Numerous computer simulations are performed in the presence of additive white Gaussian noise (AWGN) with a signal-to-noise ratio (SNR) ranging from -10 dB to 30 dB. The simulation results and comparisons with previous studies demonstrate that applying the proposed algorithms along with the selected features leads to a significant enhancement in the accuracy and speed of the automatic determination of the digital modulation types at low SNRs. In addition, the convergence rates of the models are enhanced.

Integrated sensing and communications (ISAC) has emerged as a promising solution for addressing spectrum congestion in sixth-generation communication systems. In this work, we consider the deployment of ISAC in a full-duplex cell-free (FD-CF) multi-input multi-output (MIMO) system that is aided by a reconfigurable intelligent surface (RIS). To overcome the performance limitations of a single ISAC base station (BS), we consider multiple FD access points (APs) that simultaneously perform target detection and multi-user uplink (UL) communication, assisted by a RIS. We aim to maximize the weighted sum of the output radar and communication signal-to-interference-plus-noise ratios (SINRs) by jointly designing the radar and communication receive beamformers, UL transmission powers, joint downlink (DL) sensing beamformers, and RIS reflection coefficients. The total UL, DL power budgets, and the RIS phase shift unit-modulus constraints are considered to guarantee the balance between sensing and communication requirements. The resulting problem is non-convex and rather formidable to solve. Nonetheless, an efficient solution to this problem is developed based on alternating optimization, which utilizes majorization-minimization (MM), fractional programming (FP), and the penalty method. Simulations demonstrate the effectiveness of the proposed solution and the advantages of deploying RIS to assist integrated cooperative sensing and communication (ICSAC) in FD-CF MIMO systems.

Given a multihop wireless network and a source-destination pair of nodes, this paper addresses the problem of jointly selecting a communication route and allocating transmit power levels, so that the end-to-end spectral efficiency of the route exceeds a desired threshold. Spectral-efficient routing has been subject to interest in the recent literature. The transmit power level, however, has been assumed to be known, and route selection was considered in isolation. This paper presents the first rigourously proven optimal, polynomial-time algorithms for two versions of the joint spectral-efficient routing and power allocation problem: sum-power minimization and maximum power minimization. The proposed algorithms rely on the divide-and-conquer principle and the Bellman-Ford algorithm for shortest (or widest) path computation. Our computational results further illustrate the efficiency of the proposed approach.

This paper addresses the problem of finding the route with maximum end-to-end spectral efficiency, under the constraint of equal bandwidth sharing, in multihop wireless networks. This problem has been addressed recently in the literature, and only exhaustive search with exponential computational complexity or suboptimal heuristics are known. This paper closes the algorithmic gap by introducing two algorithms that provide provably optimal solutions to the problem in polynomial-time. The proposed algorithms rely on the iterative use of a shortest path procedure. Our computational results further illustrate the efficiency of the proposed approach.

Ambient backscatter communication (AmBC) is a highly promising technology that enables the ubiquitous deployment of low-cost, low-power devices to support the next generation of Internet-of-Things (IoT) applications. This paper addresses channel estimation for full-duplex multi-antenna AmBC systems. This is highly challenging due to the large number of channel parameters resulting from the use of multiple antennas, the presence of self-interference, and the dependence of the backscattering channel on the state of the backscattering device. Considering both pilot-based and semi-blind estimation strategies, we propose three solutions for this problem. The first is the pilot-based maximum-likelihood (ML) estimator. The second is a semi-blind estimator based on the expectation maximization (EM) framework, which provides higher accuracy than the ML, at the cost of higher computational complexity. The third is a semi-blind estimator based on the decision-directed (DD) strategy, which provides a tradeoff between the ML and the EM. Additionally, we derive the exact Cramer-Rao bound (CRB) for pilot-based estimation and the modified CRB for semi-blind estimation. Simulations show that the ML and the EM perform very close to their respective CRBs, and that the semi-blind estimators offer significantly higher estimation accuracy, as well as superior symbol-error-rate performance, compared to the ML estimator.

In a wavelength division multiplexing (WDM)-based network, lightpaths are established between router pairs to form a virtual topology residing on top of the underlying physical topology. The ability to reconfigure its virtual topology upon dynamically changing traffic patterns has been identified as one of the most important features of WDM-based networks. Given a multifiber WDM network with limited fiber and wavelength resources, an existing virtual topology, and a new set of traffic demands, this paper addresses the problem of finding the new virtual topology that maximizes the carried traffic of connections, while absolutely guaranteeing that ongoing connections are not disrupted. We introduce conditions under which the new virtual topology has the intrinsic property of no service disruption. Then, we use these conditions to formulate the reconfiguration problem as an integer linear program (ILP). We also present a heuristic reconfiguration algorithm that is based on partitioning the traffic demands, so as to maintain wavelength loads as balanced as possible, followed by solving a sequence of single-wavelength problems. We theoretically verify the correctness of the algorithm, and illustrate its efficiency in terms of solution quality and computational cost via numerical experiments.