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.

The evolution of communication networks towards multi-functionality has paved the way for integrated sensing, communication, and power transfer (ISCAPT) systems, enabling efficient data transmission, environmental sensing, and wireless energy transfer. However, conventional ISCAPT architectures face inherent trade-offs between high-rate communication, precise sensing, and efficient energy transfer, exacerbated by interference and dynamic channel conditions. To address these limitations, rate-splitting multiple access (RSMA) and reconfigurable intelligent surfaces (RIS) are integrated into ISCAPT systems. Hence, in this paper, we consider active RIS-aided RSMA ISCAPT to maximize the sum communication rate while ensuring the sensing performance, harvesting adequate energy, and satisfying the transmit power budgets of the BS and RIS. To achieve this goal, the transmit beamforming matrix, RIS reflection matrix, power splitting factor, radar receive beamforming, and common rate allocation are jointly designed. However, the formulated maximization problem is challenging due to its non-convex nature and the coupling among the optimization variables. To efficiently tackle this issue, the formulated problem is decomposed into three sub-problems, which are reformulated into quadratic-constrained-quadratic programs (QCQPs). Then, an alternating optimization (AO)-aided majorization-minimization (MM) and successive convex approximation (SCA) algorithm is proposed to iteratively optimize these sub-problems. Simulation studies are conducted to demonstrate the effectiveness of the proposed framework and illustrate trade-offs compared to established benchmarks.

Ambient backscatter communication (AmBC) has attracted significant attention as a viable paradigm for energy efficient communication in the next generation of Internet-of-Things (IoT). Channel estimation, a vital task in AmBC receiver design, becomes more challenging when accounting for more practical scenarios such as frequency selective fading and the presence of hardware impairments such as I/Q imbalance. Full-duplex communication further complicates channel estimation, due to the presence of self-interference. In this work, we consider OFDM-based full-duplex AmBC systems affected by I/Q imbalance, and propose two methods for the joint estimation of the channel and I/Q imbalance. The first algorithm is a pilot-based estimator, while the second is a semi-blind estimator based on the concept of decision-directed (DD) estimation. In addition, we propose a novel design of appropriate pilot sequences to optimize the performance of the proposed methods. As benchmarks on estimation performance, we obtain analytical expressions for the pilot-based and semi-blind Cramer-Rao bounds (CRBs). Our simulations demonstrate that both proposed estimators converge to their respective bounds. The proposed methods offer different tradeoffs between accuracy and computational complexity, which are suitable for different use cases.

Ambient backscatter communication (AmBC) has attracted attention as an enabling technology for green device-to-device (D2D) communication for the next generation of Internet-of-Things (IoT) networks. Most existing works assume that the signals of the source and the backscattering device (BD) are perfectly synchronized at the reader. Perfect synchronization is not feasible in practice, and the timing offset between the two signals can significantly degrade the performance of the reader’s receiver. In this work, we consider an asynchronous AmBC system and solve the problem of joint timing offset and channel estimation at the reader. Assuming generic Nyquist pulse-shaping filters, we develop a pilot-based maximum likelihood (ML) estimator, as well as a semi-blind estimator using the expectation maximization (EM) algorithm. For the special case of the rectangular pulse, we also derive the corresponding ML and EM estimators, and an approximate low-complexity EM (LCEM) algorithm. As theoretical benchmarks, we obtain the Cramer-Rao-bound (CRB) for pilot-based estimation, while for semi-blind estimation the modified CRB (MCRB) is obtained. The performance of the proposed algorithms is investigated using simulations, showing that both the ML and EM approach their corresponding CRBs, and that the EM-type algorithms provide superior estimation and detection accuracy, at the expense of higher complexity.

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.

Abstract With the advent of fifth generation (5G) systems and the Internet‐of‐Things (IoT), the number of interconnected wireless devices is increasing significantly. Protocols that allow these deceives to interconnect peer‐to‐peer through wireless links are becoming of interest. The major challenge is the inevitable interference among the simultaneously activated wireless links. Given a set of wireless links, this article addresses the non‐deterministic polynomial‐time (NP) hard problem of selecting the maximum subset of links that can be simultaneously activated at their respective signal‐to‐interference‐plus‐noise‐ratio (SINR) targets. The contribution of this article is two‐fold. First, we introduce a new genetic algorithm (GA) constraint‐handling mechanism, and prove analytically that finding optimal link schedules is guaranteed. Second, we develop a novel parallelized GA to solve the problem. Through serial algorithm analysis, we utilize data decomposition as well as exploratory decomposition in order to achieve significant running time speedup, which scales well with problem size. Our numerical results for openMP parallelization illustrate 6.5 and 5.4 reduction in computation time as compared to the serial versions of the GA and hybrid genetic algorithm (HGA), respectively. Moreover, the parallelization of the GA and HGA result in a speedup of 10.4 and 5.4 , respectively, using master‐slave multithreading.

Abstract This paper considers the problem of joint timing‐offset and channel estimation for physical‐layer network coding systems operating in frequency‐selective environments. Three different algorithms are investigated for the joint estimation of the channel coefficients and the fractional timing offset. The first algorithm is based on the maximum‐likelihood (ML) criterion assuming baud‐rate (BR) sampling. The second algorithm also assumes BR sampling and is based on the special properties of Zadoff‐Chu training sequences. In the third algorithm, oversampling at double the baud‐rate (DBR) is used and the least‐squares (LS) estimation criterion applied. While the above algorithms assume that the integer timing offset is known, three generalized‐likelihood‐ratio tests (GLRTs) are also considered for integer offset error correction that integrate very well with the proposed estimation algorithms. Our simulation studies show that the DBR‐LS estimator provides the highest estimation accuracy, significantly outperforming both BR estimators and performing very close to the corresponding Cramer–Rao bound. A gain of 4 dB is observed in symbol‐error‐rate performance using the DBR‐LS algorithm. The DBR‐GLRT also provides substantially higher probability of error correction.

In cellular networks, user density and activity across network area vary from cell to another, and even at the same area over the day hours. This results in an irregular utilization of the network resources (i.e., resource blocks "RBs") across the network cells. One of the solutions to improve network utilization is the coordinated multi-point (CoMP) approach which coordinates signal transmission and reception for a user to and from multiple base stations (BSs). However, providing CoMP to all network users could waste RBs. To solve this problem, a novel clustering technique is proposed in this paper to guarantee a wise CoMP service to users based on their area density state (i.e., overloaded or under-loaded) and across different day periods. Through simulations, the proposed dynamic clustering algorithm outperforms other clustering algorithms in improving overall user throughput while maintaining the total consumed energy to acceptable levels.

In this paper, we consider the problem of joint timing-offset and channel estimation for amplify-and-forward (AF) two-way relay networks (TWRNs). This problem is solved for generic pulse-shaping filters, taking into account the filter truncation in practical communication and considering both pilot-based and semi-blind estimation strategies. Beginning with pilot-based estimation, we propose a novel Maximum-likelihood joint timing-offset and channel estimator, as well as an alternative estimator based on the special properties of Zadoff-Chu sequences. The first algorithm offers high accuracy, almost overlapping with the Cramer-Rao bound (CRB), while the second offers very low computational complexity. We then develop a semi-blind estimator based on the expectation maximization (EM) framework, exploiting the underlying Hidden Markov Model to apply Baum-Welch forward-backward recursion. The semi-blind CRB is also obtained as an indicator of the best achievable performance. Using simulations, we show that the semi-blind algorithm yields superior accuracy to pilot-based estimation, as well as improved symbol-error-rates and performs very close to the semi-blind CRB. Additionally, a low-complexity approximate EM algorithm is proposed for the case of rectangular pulses. Finally, we consider the possibility of errors in integer-offset estimation and propose pilot-based and semi-blind generalized likelihood ratio test (GLRT) schemes for correcting such errors.

Finding the route with maximum end‐to‐end spectral efficiency in multihop wireless networks has been subject to interest in the recent literature. All previous studies, however, focused on finding one route from a given source to a given destination under the constraint of equal bandwidth sharing. To the best of our knowledge, for the first time, this paper provides extensions to the multicommodity flow case, i.e., the case of multiple simultaneous source‐destination (s‐d) pairs. In particular, given an arbitrary number of s‐d pairs, we address the problem of finding a route for every s‐d pair such that the minimum spectral efficiency across all routes is maximized. We provide two alternative approaches, where one is based on fixed‐sized time slots and the other is based on variable‐sized time slots. For each approach, we derive the provably optimal routing algorithm. We also shed the light on the arising tradeoff between the complexity of network‐layer route computation and the complexity of medium access control (MAC) layer scheduling of time slots, as well as the amenability to distributed implementation of our proposed algorithms. Our numerical results further illustrate the efficiency of the proposed approaches and their tradeoffs.

Given a number of wireless links, this paper addresses the problem of maximizing the network capacity, i.e., the number of links that can be activated simultaneously. Solving this problem under the physical signal-to-noise-plus-interference (SINR) model has been demonstrated to be NP-hard. Previous studies focused, almost exclusively, on approximation algorithms with guaranteed performance ratios. Although such algorithms have tremendous theoretical value, their surprisingly low approximation ratios limit their practicality. This paper solves the problem using another alternative: the genetic algorithm meta-heuristics. The main challenge in using genetic algorithms is to successfully handle optimization constraints, because the original algorithm was designed for unconstrained problems. To this end, we devise a novel constraint handling mechanism that theoretically guarantees finding feasible and optimal solutions. Our numerical results illustrate the efficiency of the proposed approach, and its superiority over existing methods.

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 that use time division multiple access (TDMA). The difficulty of this problem arises from the fact that the associated routing metric is neither isotonic nor monotone, and, thus, it cannot be solved directly using shortest path algorithms. The author has recently presented the first polynomial-time algorithm that solves the problem to exact optimality. This paper presents an alternative algorithm that enjoys a significantly reduced computation time, while still providing provably optimal communication routes. The proposed algorithm relies on the divide-and-conquer principle and a modified Bellman-Ford algorithm for 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.

This paper addresses the problem of joint admission control and power allocation in the downlink of a single-cell code-division-multiple-access (CDMA) wireless network. The objective is to maximize the user-capacity (or base station revenue), while meeting the strict quality-of-service (QoS) requirements of each admitted user. We prove that the global optimal solution can be found by performing admission control and power allocation separately. To solve the admission control problem, we give an exact polynomial-time algorithm for user-capacity maximization, and an efficient approximation algorithm for base station revenue maximization. To solve the power control problem, we provide two simple heuristics, that can be efficiently carried out by the base station. Numerical results further demonstrate the efficiency of our proposed approach.

We address the problem of allocating transmission rates to a set of network sessions with end-to-end bandwidth and delay requirements. We give a unified convex programming formulation that captures both average and probabilistic delay requirements. Moreover, we present a distributed algorithm and establish its convergence to the global optimum of the overall rate allocation problem. In our algorithm, session sources selfishly update their rates as to maximize their individual benefit (utility minus bandwidth cost), the network partitions end-to-end delay requirements into local per-link delays, and the links adjust their prices to coordinate the sources' and network's decisions, respectively. This algorithm relies on a network utility maximization (NUM) approach, and can be viewed as a generalization of TCP and active queue management (AQM) algorithms to handle end-to-end QoS. We extend our results to deterministic delay requirements when nodes employ Packet-level Generalized Processor Sharing (PGPS) schedulers.

We address the problem of allocating transmission rates to a set of network sessions with end-to-end bandwidth and delay requirements. We give a unified convex programming formulation that captures both average and probabilistic delay requirements. Moreover, we present a distributed algorithm and establish its convergence to the global optimum of the overall rate allocation problem. In our algorithm, session sources update their rates as to maximize their individual benefit (utility minus bandwidth cost), the network partitions end-to-end delay requirements into local per-link delays, and the links adjust their prices to coordinate the sources' and network's decisions, respectively. This algorithm relies on a network utility maximization approach, and can be viewed as a generalization of TCP and queue management algorithms to handle end-to-end QoS. We also extend our results to deterministic delay requirements when nodes employ packet- level generalized processor sharing (PGPS) schedulers.

Given a combination of unprotected and dedicated edge-disjoint path (1+1) protected connection requests and a finite set of fiber types, we consider the problem of allocating fibers on the links of a WDM network at minimum cost, such that all connection requests can be simultaneously realized. Each fiber type, is characterized by its capacity and its cost per unit length, where costs reflect an economy of scale. It is known that a solution induced by "simply" routing each unprotected (respectively 1+1 protected) connection along the shortest path (respectively shortest pair of edge-disjoint paths) minimizes the total wavelength mileage, but may not minimize the total fiber cost. In this paper, we quantify the increase in fiber cost due to shortest path routing. In particular, we prove that the total cost of a shortest path based solution is guaranteed to lie within a certain factor of the minimum possible cost. This leads also to the fact that shortest path routing is asymptotically cost-optimal for a large total number of connection requests. Furthermore, for sparse topologies, e.g., the ring, the ShuffleNet and the mesh(-torus), we show that shortest path routing is asymptotically cost-optimal in large-scale networks supporting all-to-all communication. En route, we prove that by shortest path routing we obtain a provably optimal solution to the linear programming (LP-) relaxation of the problem. We have thus presented a provably good upper bound and a lower bound on the total fiber cost, that can be computed in polynomial-time. These bounds can be used as benchmarks against which heuristic approaches are compared.

We address the problem of allocating fibers (each supporting only a limited set of wavelengths) on the links of a WDM network at minimum cost, such that a set of edge-disjoint path protected connection requests can be realized. The cost of a link is assumed to be linear in the number of fibers rather than being linear in the number of wavelengths used on this link, reflecting modular capacity considerations. Therefore, a solution induced by routing each connection "simply" along the minimum-cost (shortest) pair of edge-disjoint lightpaths may not minimize the total fiber cost. In this paper we quantify the increase in the total fiber cost due to this simple routing strategy. In particular, we prove that the cost of a solution induced by routing along shortest path pairs is guaranteed to lie within a certain factor of the minimum possible cost. This leads also to the fact that the cost of this solution is asymptotically minimum in heavily loaded networks, and in networks that are large, sparse and supporting all-to-all communications. En route, we prove that the optimal objective function value of the linear programming (LP) relaxation actually corresponds to routing along shortest path pairs. We have thus presented a provably good upper bound and a lower bound on the total fiber cost, that can be computed in polynomial-time, and can be used as benchmarks against which exact and heuristic approaches are compared.

This paper addresses the problem of routing and wavelength assignment (RWA) in multifiber WDM networks with limited resources. Given a traffic matrix, the number of fibers per link, and the number of wavelengths a fiber can support, we seek to maximize the carried traffic of connections. We formulate the problem as an integer linear program (ILP), and show that the lightpaths selected by this formulation can indeed be established by properly configuring the optical switches. An upper bound on the carried traffic can be computed by solving the linear programming (LP)-relaxation of the ILP formulation. It is shown that this bound can be also computed exactly, and in polynomial-time, by solving a significantly simplified LP which considers only one wavelength. The bound can, thus, easily scale to an arbitrarily large number of wavelengths. Furthermore, we demonstrate that any instance of the RWA problem is also an instance of the more general maximum coverage problem. This allows us to take a greedy algorithm for maximum coverage and obtain an algorithm which provides solutions for the RWA problem that are guaranteed to be within a factor of (1-(1/e)) of the optimal solution. Each iteration of the greedy algorithm selects a set of lightpaths that realizes, using one wavelength, the maximum number of connection requests not previously realized. Computational results confirm the high efficiency of our proposed algorithm.

In a 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. Compared to previously reported reconfiguration studies, we provide contributions along two different directions. First, we address the problem of finding the new virtual topology that maximizes the number of successfully established lightpaths, while guaranteeing absolutely no service disruptions. Second, based on a Lagrangean decomposition approach, we demonstrate that optimal and near-optimal virtual topologies can be obtained by considering only one wavelength in the formulation, leading to a reconfiguration algorithm that scales to an arbitrarily large number of wavelengths. Computational results confirm the high efficiency of the proposed algorithm.

This paper addresses the problem of routing and wavelength assignment (RWA) in multifiber WDM networks assuming neither a special topology nor wavelength converters. Given a set of connection requests, the number of fibers deployed on each link, and the number of wavelengths a fiber can support, we seek to maximize the number of lightpaths that can be established. We formulate the problem as an integer linear program (ILP), whose validity is proven by showing that the selected lightpaths can indeed be realized by properly configuring the optical switches. Furthermore, using a Lagrangean decomposition approach, the problem formulation is significantly simplified. The main advantage of our approach is that, independent of the number of wavelengths, provably optimal solutions to the problem can be obtained by considering only one wavelength in the formulation, leading to highly efficient and scalable algorithms. Although our formulation is path-flow based rather than link-flow based, we prove that, even if all, possibly exponentially many, paths are considered, its linear programming (LP) relaxation can always be solved in polynomial time. We use the branch-and-bound algorithm in the CPLEX optimization package to solve the resulting ILP formulation. Computational results confirm the high efficiency of the Lagrangean decomposition approach.