In this paper we present and evaluate the performance of a routing and link scheduling algorithm for millimeter wave backhaul networks. The proposed algorithm models the end user behavior as being selfish, i.e., it considers users always aiming to maximize their individual utility, rather than the global optimization objective. In order to forward packets through the backhaul network, the Shapley value method is applied, which is shown to induce solutions with reduced latency. The performance of the proposed algorithm is evaluated in terms of the total delay as well as the price of anarchy, which represents the inefficiency of a scheduling policy when users are allowed to adapt their rates in a selfish manner and reach an equilibrium. A relaxed version of the problem is also presented, which provides a lower bound on the value of the optimal solution. According to simulation results, the system employing the proposed algorithm outperforms in terms of delay and price of anarchy a system considering a First-In-First-Out packet forwarding policy, as well as a system employing local search global optimization, under which users aim at optimizing the overall delay in the network.

Autonomous systems and mission-critical applications demand ultra-reliable low-latency communication (URLLC). To build wireless communication networks capable of accommodating such applications, optimization of the airinterface characteristics is vital. This paper leverages recent advancements in the field of Artificial Intelligence (AI) technologies to optimize specific aspects of the air interface design to satisfy these stringent link reliability and latency requirements. The precise aim of this research is to reduce the link latency caused by the presence of the Hybrid Automatic Repeat reQuest (HARQ) mechanism. To this end, we propose a novel deep learning-based algorithm (Deep-HARQ), employing a deep neural network (DNN) with fully connected layers to estimate the decodability of the coded-received in-phase and quadrature (I/Q) signals prior to accomplishing the majority of the complex reception tasks. This enables the receiver to respond faster, allowing for the reduction of the signal round-trip time (RTT). To evaluate Deep-HARQ with a realistic dataset, we collected training and validation samples from a waveform compatible with 3GPP 5G NR Release 15 standards. The simulation results reveal a faster estimation response, with an accuracy enhancement of 12% compared to relevant algorithms in the literature.

In this paper we present a co-primary spectrum sharing algorithm for the Quality of Service (QoS) enhancement of uplink Single-Carrier Frequency Division Multiple Access (SC-FDMA) systems. We consider the limitations that are resulting from the fact that each user can only be provided with only contiguous sets of resource blocks (following the constraints of the localized SC-FDMA physical layer), and the effect of the limited, or even lack of, knowledge of each user's buffer status and packet delays in the uplink. The sharing of available resources is based on the operator spectrum access priority, an estimation of the packet delays in the uplink direction, the average delay and data rate of earlier allocations, and the power per resource block. Simulation results show that the proposed algorithm considerably improves the performance in terms of packet loss rate, goodput, and fairness.

In this paper we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of downlink Orthogonal Frequency Division Multiple Access (OFDMA) systems. The proposed algorithm performs resource allocation using information on the downlink packet delay, the average delay and data rate of past allocations, as well as the downlink users' buffer status in order to minimize packet segmentation. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, fairness, and average delay. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit.

In this paper, we present and evaluate the performance of a resource allocation algorithm to enhance the Quality of Service (QoS) provision and energy efficiency of uplink Long Term Evolution (LTE) systems. The proposed algorithm considers the main constraints in uplink LTE resource allocation, i.e., the allocation of contiguous sets of resource blocks of the localized Single Carrier-Frequency Division Multiple Access (SC-FDMA) physical layer to each user, and the imperfect knowledge of the users' uplink buffer status and packet waiting time. The optimal resource allocation is formulated as a discrete connected cake-cutting problem, where different agents are allocated consecutive subsequences of a sequence of indivisible items. This problem is NP-hard, therefore a suboptimal algorithm is introduced, which performs resource allocation using information on the estimated uplink packet delay, the average delay and data rate of past allocations, as well as the required uplink power per resource block. Based on simulation results, the proposed algorithm achieves significant performance improvement in terms of packet timeout rate, goodput, and fairness. Moreover, the effect of poor QoS provision on energy efficiency is demonstrated through the evaluation of the performance in terms of energy consumption per successfully received bit.

In this paper we present an analytical framework that aims to improve the energy efficiency of traffic offloading via Wireless Local Area Networks, taking into account the energy consumption for both data transmission and network discovery operations. More specifically, the network scanning period is optimized in order to minimize the energy consumption in a vehicular scenario where a user moves along a road covered by a long range cellular network and a number of randomly deployed Wireless Local Area Networks. The performance of the system that performs periodic network scanning with the optimal period is compared against a sub-optimal system that does not take into consideration the user and network context information when determining the network scanning period. According to performance evaluation results, the use of the optimal network scanning period achieves significant improvement in terms of energy consumption and network detection delay.

A cross-layer mechanism for the performance improvement of real-time applications over IEEE 802.16e networks is proposed. The mechanism coordinates i) adaptation capabilities at different layers of the protocol stack, and ii) the handover initiation procedure. It uses information from the physical (PHY) and medium access control (MAC) layers to determine the appropriate burst profile, transmission power level and media encoding rate for a connection, or initialize a handover. The main contribution of this mechanism is the integration of the handover initiation into an existing cross-layer logic mechanism to improve the overall system performance. Extensive simulation results show that the proposed design achieves significantly improved performance in terms of packet loss rate, power consumption, and throughput, as well as an increased system capacity.

A cross-layer mechanism to improve the performance of real-time applications over IEEE 802.16e metropolitan area networks is presented. The proposed mechanism uses channel quality and service quality information from the physical and medium access control layers, respectively, to determine the most suitable burst profile, transmission power level and media encoding rate for a connection, or even initialize a handover execution. The main contribution of this mechanism is the integration of the handover initiation into the cross layer logic, aiming to improve the overall system performance. Extensive simulation results show that the proposed mechanism offers significant performance improvement in terms of packet loss rate, power consumption, throughput, and system capacity.

Abstract In this paper, an efficient cross‐layer design that performs joint adaptation of the physical (PHY) and application layers of a mobile WiMAX network is proposed. The design takes into account channel state and performance information from the PHY and medium access control (MAC) layers, respectively. It uses a decision algorithm to evaluate this information, specify unfavorable conditions regarding low channel quality and increased congestion, and take measures by coordinating modulation order, transmission power, and media encoding rate, toward improved overall quality of service (QoS) offered to the user. Extensive simulation results show that the proposed design achieves considerably reduced packet loss and power consumption, combined with increased throughput as compared to a typical mobile WiMAX system. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper we propose and study a cross-layer mechanism that can improve real-time QoS provisioning over IEEE 802.16 metropolitan area networks. This mechanism utilizes information provided by the Physical and MAC layers and using a heuristic algorithm it derives new operational parameters for the Physical and Application layers, which can improve the performance of real-time applications. The main idea is to coordinate the adaptive modulation capability of the Physical layer and the multi-rate data-encoding capability of modern real-time applications in order to avoid inefficiencies caused by their independent operation. Simulations show that the proposed mechanism can assist 802.16 systems better adapt to frequent channel and traffic changes, leading to considerably reduced packet loss rates, especially under heavy traffic conditions.