This research presents a novel framework integrating Flexible-Duplex (FlexD) and Integrated Sensing and Communications (ISAC) technologies to address the challenges of spectrum efficiency and resource optimization in next-generation wireless networks. We develop a unified system model for a dual-functional radar-communication base station with multiple-input multiple-output capabilities, enabling dynamic uplink and downlink channel allocation. The framework maximizes network throughput while maintaining radar sensing performance, subject to signal-to-clutter-plus-noise ratio (SCNR) requirements and power constraints. Given the non-convex and combinatorial nature of the resulting optimization problem, we propose an iterative algorithm that converges to a locally optimal solution. Extensive simulations demonstrate the superiority of the proposed FlexD-ISAC framework compared to conventional half-duplex networks. Additionally, sensitivity analyses reveal the impact of SCNR requirements and power constraints on system performance, providing valuable insights for practical implementation. This work establishes a foundation for future research in dynamic, resource-efficient wireless systems that simultaneously support sensing and communication capabilities.

We develop a gradient-like algorithm to minimize a sum of peer objective functions based on coordination through a peer interconnection network. The coordination admits two stages: the first is to constitute a gradient, possibly with errors, for updating locally replicated decision variables at each peer and the second is used for error-free averaging for synchronizing local replicas. Unlike many related algorithms, the errors permitted in our algorithm can cover a wide range of inexactnesses, as long as they are bounded. Moreover, we do not impose any gradient boundedness conditions for the objective functions. Furthermore, the second stage is not conducted in a periodic manner, like many related algorithms. Instead, a locally verifiable criterion is devised to dynamically trigger the peer-to-peer coordination at the second stage, so that expensive communication overhead for error-free averaging can significantly be reduced. Finally, the convergence of the algorithm is established under mild conditions.

This paper investigates a three-layer IoT-fog-cloud computing system to determine the optimum workload and power allocation at each layer. The objective is to minimize maximum per-layer latency (including both data processing and transmission delays) with individual power constraints. The resulting optimum resource allocation problem is a mixed-integer optimization problem with exponential complexity. Hence, the problem is first relaxed under appropriate modeling assumptions, and then an efficient iterative method is proposed to solve the relaxed but still non-convex problem. The proposed algorithm is based on an alternating optimization approach, which yields close-to-optimum results with significantly reduced complexity. Numerical results are provided to illustrate the performance of the proposed algorithm compared to the exhaustive search method. The latency gain of three-layer distributed IoT-fog-cloud computing is quantified with respect to fog-only and cloud-only computing systems.

Empirical studies show that cruising for car parking accounts for a non-negligible amount of the daily traffic, especially in central areas of large cities. Therefore, mechanisms for minimizing traffic from cruising directly affect the dynamics of traffic congestions. One way to minimizing cruising traffic is efficient car-parking-slot assignment. Usually, the related design problems are combinatorial and the worst-case complexity of optimal methods grows exponentially with the problem sizes. As a result, almost all existing methods for parking slot assignment are simple and greedy approaches, where each car or the user is assigned a free parking slot, which is closer to its destination. Moreover, no emphasis is placed to optimize any form of fairness among the users as the a social benefit. In this paper, the fairness as a metric for modeling the aggregate social benefit of the users is considered. An algorithm based on Lagrange duality is developed for car-parking-slot assignment. Numerical results illustrate the performance of the proposed algorithm compared to the optimal assignment and a greedy method.

Millimeter-wave communications in the 60-GHz band are considered one of the key technologies for enabling multigigabit wireless access. However, the special characteristics of such a band pose major obstacles to the optimal utilization of the wireless resources, where the problem of efficient client association to access points (APs) is of vital importance. In this paper, the client association in 60-GHz wireless access networks is investigated. The AP utilization and the quality of the rapidly vanishing communication links are the control parameters. Because of the tricky nonconvex and combinatorial nature of the client association optimization problem, a novel solution method is developed to guarantee balanced and fair resource allocation. A new distributed, lightweight, and easy-to-implement association algorithm, based on Lagrangian duality theory and subgradient methods, is proposed. It is shown that the algorithm is asymptotically optimal, that is, the relative duality gap diminishes to zero as the number of clients increases .

A framework for the distributed optimization of reactive cognitive networks with multiple secondary users is presented. The secondary users iteratively locate the policy maximizing their aggregate performance under bounds on the maximum performance loss caused to the primary users. The policy accounts for the impact of interference on the dynamics of the primary users' network due to transmission and channel access protocols. To minimize coordination overhead, it is assumed that the secondary users only coordinate the policy, whereas actions in each slot are independently selected by the individual secondary user based on the agreed policy. The distributed optimization technique proposed herein is based on alternating convex optimization. Numerical results are presented assessing the performance of the obtained transmission policy with respect to the optimal centralized and fully-coordinated policy.

Wireless sensor networks for industrial automation applications must offer timely, reliable, and energy efficient communications at both low and high data rate. While traditional communication technologies between 2.4 GHz and 5 GHz are sometimes incapable to efficiently achieve the aforementioned goals, new communication strategies are emerging, such as millimeterWave communications. In this overview paper, the general requirements that factory and process automation impose on the network design are reviewed. Moreover, this paper presents and qualitatively evaluates the 60 GHz millimeterWave communication technology for automation. It is argued that the upcoming 60 GHz millimeterWave technology brings an enormous potential and can influence the design of the future communication infrastructures in factory and process automation.

We consider the problem of MaxWeight scheduling in wireless multihop networks. This problem is known to be NP-hard. We propose a solution method, based on the branch and bound technique, which solves globally the MaxWeight scheduling problem with an optimality certificate. Efficient analytic bounding techniques are introduced as well.

We consider the problem of weighted sum-rate maximization (WSRMax) in wireless networks. This problem is known to be NP-hard and it plays a central role in resource allocation, link scheduling or in finding achievable rate regions for both singlecast and multicast networks. We propose a solution method, based on the branch and bound technique, which solves globally the WSRMax problem with an optimality certificate. Efficient bounding techniques are introduced as well.

In a wireless network, the problem of self interference arises whenever a node transmits and receives simultaneously in the same frequency band. So far only two extreme approaches to circumvent this problem were thoroughly investigated in the literature. The first one prevents any node to transmit and receive simultaneously which may lead to a too conservative design. The second one assumes perfect self interference cancelation which can be too optimistic since it ignores all possible technological limitations. To fill this gap, we provide a method to evaluate the network layer benefits from simultaneous transmission and reception when the network nodes employ self interference cancelation techniques with different degrees of accuracy. From a network design perspective, the provided method can be used to find the required level of accuracy for the self interference cancelation such that certain gains are achieved at the network layer. Numerical results suggest that the accuracy of existing self interference cancelation techniques can provide significant gains for certain network setups.

We consider the cross-layer utility maximization problem for wireless networks. It is well known that the optimal network control policy can be decomposed in three separate subproblems: 1) flow control at the network layer, 2) routing and scheduling at the network layer, and 3) resource allocation (RA) at the medium access control and physical layers. The main contribution of this paper is a power and rate control algorithm for the RA subproblem. In the case of single-hop networks, the proposed algorithm provides a locally optimal solution for the RA subproblem. Even though the global optimality of the solution cannot be guaranteed due to the nonconvexity of the problem, the numerical results show that the algorithm can provide significant gains at the network layer in terms of end-to-end rates and network congestion as compared to the optimal time division multiple access (TDMA) based RA. For solving the RA subproblem in the case of multi-hop networks, the proposed algorithm must be used in conjunction with an exhaustive search for the optimal set of transmitter nodes. However, the numerical results show that even with a random selection of the set of transmitter nodes, the proposed algorithm can provide significant improvement at the network layer in terms of end-to-end rates and network congestion as compared the optimal TDMA based RA.

We consider the problem of cross-layer utility maximization subject to stability constraints for a multicommodity wireless network where all links are sharing a single channel. We assume a time slotted network and only one node is allowed to transmit at any given slot. The optimal cross-layer network control policy can be decomposed into three subproblems: (1) flow control at the transport and network layers, (2) routing and scheduling at the network layer, and (3) resource allocation (RA) at the medium access control and physical layers. Every time slot, a network controller decides the transmitter node, amount of each commodity data admitted to the network layer, schedules different commodities over networks' links and controls the power and rate allocated to every link. In this paper we provide solutions for the RA subproblem for two scenarios: (1) when the nodes are equipped with standard single user receivers, and (2) when the nodes are equipped with multiuser receivers performing successive interference cancelation. In addition we propose decentralized algorithms to obtain the solution of RA subproblem for each scenario. The numerical results show that as the signal-to-noise ratio increases, using multiuser receivers can provide significant gains at the network layer in terms of end-to-end rates and network congestion as compared to that of single user receivers.