25 research outputs found
Low Complexity Joint Sub-Carrier Pairing, Allocation and Relay Selection in Cooperative Wireless Networks
Multi-carrier cooperative relay-based wireless communication is of particular interest in future wireless networks. In this paper we present resource allocation algorithm in which sub-carrier pairing is of particular interest along with fairness constraint in multi-user networks. An optimization of sub-carrier pair selection is formulated through capacity maximization problem. Sub-carrier pairing is applied in both two-hop Amplify & Forward (AF) and Decode & Forward (DF) cooperative multi-user networks. We develop a less complex centralized scheme for joint Sub-carrier pairing and allocation along with relay selection. The computational complexity of the proposed algorithms has been analyzed and performance is compared with Exhaustive Search Algorithm
Optimal Path Pair Routes through Multi-Criteria Weights in Ad Hoc Network Using the Genetic Algorithm
An ad hoc network can establish cooperative communication through path pair routes. The path pair route formed depends on the number of hops and multi-criteria used. The cross-layer criteria observed is power consumption, signal-to-noise ratio (SNR), and load variance optimized using multi-criteria optimization through scalarization with varying weights. With many path pair routes and complicated computing then in finding the optimal value genetic algorithm method was used. From the simulation results, the optimal path pair routes were obtained with varying weights; greater weight had higher priorities and produced optimum performance and computing time for scalarization function with varying weights having a very small difference even almost identical. Different computing time will be seen when compared in an exhaustive manner
Multi-Objective Cross-Layer Optimization for Selection of Cooperative Path Pairs in Multihop Wireless Ad hoc Networks
This paper focuses in the selection of an optimal path pair for cooperative diversity based on cross-layer optimization in multihop wireless ad hoc networks. Cross-layer performance indicators, including power consumption, signal-to-noise ratio, and load variance are optimized using multi-objective optimization (MOO) with Pareto method. Consequently, optimization can be performed simultaneously to obtain a compromise among three resources over all possible path pairs. The Pareto method is further compared to the scalarization method in achieving fairness to each resource. We examine the statistics of power consumption, SNR, and load variance for both methods through simulations. In addition, the complexity of the optimization of both methods is evaluated based on the required computing time
User-centric Networks Selection with Adaptive Data Compression for Smart Health
The increasing demand for intelligent and sustainable healthcare services has prompted the development of smart health systems. Rapid advances in wireless access technologies and in-network data reduction techniques can significantly assist in implementing such smart systems through providing seamless integration of heterogeneous wireless networks, medical devices, and ubiquitous access to data. Utilization of the spectrum across diverse radio technologies is expected to significantly enhance network capacity and quality of service (QoS) for emerging applications such as remote monitoring over mobile-health (m-health) systems. However, this imposes an essential need to develop innovative networks selection mechanisms that account for energy efficiency while meeting application quality requirements. In this context, this paper proposes an efficient networks selection mechanism with adaptive compression for improving medical data delivery over heterogeneous m-health systems. We consider different performance aspects, as well as networks characteristics and application requirements, so as to obtain an efficient solution that grasps the conflicting nature of the various users’ objectives and addresses their inherent tradeoffs. The proposed methodology advocates a user-centric approach towards leveraging heterogeneous wireless networks to enhance the performance of m-health systems. Simulation results show that our solution significantly outperforms state-of-the-art techniques
Analysis of a contention-based approach over 5G NR for Federated Learning in an Industrial Internet of Things scenario
The growing interest in new applications involving co-located heterogeneous
requirements, such as the Industrial Internet of Things (IIoT) paradigm, poses
unprecedented challenges to the uplink wireless transmissions. Dedicated
scheduling has been the fundamental approach used by mobile radio systems for
uplink transmissions, where the network assigns contention-free resources to
users based on buffer-related information. The usage of contention-based
transmissions was discussed by the 3rd Generation Partnership Project (3GPP) as
an alternative approach for reducing the uplink latency characterizing
dedicated scheduling. Nevertheless, the contention-based approach was not
considered for standardization in LTE due to limited performance gains.
However, 5G NR introduced a different radio frame which could change the
performance achievable with a contention-based framework, although this has not
yet been evaluated. This paper aims to fill this gap. We present a
contention-based design introduced for uplink transmissions in a 5G NR IIoT
scenario. We provide an up-to-date analysis via near-product 3GPP-compliant
network simulations of the achievable application-level performance with
simultaneous Ultra-Reliable Low Latency Communications (URLLC) and Federated
Learning (FL) traffic, where the contention-based scheme is applied to the FL
traffic. The investigation also involves two separate mechanisms for handling
retransmissions of lost or collided transmissions. Numerical results show that,
under some conditions, the proposed contention-based design provides benefits
over dedicated scheduling when considering FL upload/download times, and does
not significantly degrade the performance of URLLC
Optimal Distributed Resource Allocation for Decode-and-Forward Relay Networks
This paper presents a distributed resource allocation algorithm to jointly
optimize the power allocation, channel allocation and relay selection for
decode-and-forward (DF) relay networks with a large number of sources, relays,
and destinations. The well-known dual decomposition technique cannot directly
be applied to resolve this problem, because the achievable data rate of DF
relaying is not strictly concave, and thus the local resource allocation
subproblem may have non-unique solutions. We resolve this non-strict concavity
problem by using the idea of the proximal point method, which adds quadratic
terms to make the objective function strictly concave. However, the proximal
solution adds an extra layer of iterations over typical duality based
approaches, which can significantly slow down the speed of convergence. To
address this key weakness, we devise a fast algorithm without the need for this
additional layer of iterations, which converges to the optimal solution. Our
algorithm only needs local information exchange, and can easily adapt to
variations of network size and topology. We prove that our distributed resource
allocation algorithm converges to the optimal solution. A channel resource
adjustment method is further developed to provide more channel resources to the
bottleneck links and realize traffic load balance. Numerical results are
provided to illustrate the benefits of our algorithm
Conjoint Routing and Resource Allocation in OFDMA-based D2D Wireless Networks
In this paper, we develop a highly efficient twotier technique for jointly optimizing the routes, the subcarrier schedules, th