Utility-Based resource allocation for underlay D2D networks

We develop two distributed downlink resource allocation algorithms for user-centric, cell-free, spatially-distributed, multiple-input multiple-output (MIMO) networks. In such networks, each user is served by a subset of nearby transmitters that we call distributed units or DUs. The operation of the DUs in a region is controlled by a central unit (CU). Our first scheme is implemented at the DUs, while the second is implemented at the CUs controlling these DUs. We define a hybrid quality of service metric that enables distributed optimization of system resources in a proportional fair manner. Specifically, each of our algorithms performs user scheduling, beamforming, and power control while accounting for channel estimation errors. Importantly, our algorithm does not require information exchange amongst DUs (CUs) for the DU-distributed (CU-distributed) system, while also smoothly converging. Our results show that our CU-distributed system provides 1.3- to 1.8-fold network throughput compared to the DU-distributed system, with minor increases in complexity and front-haul load - and substantial gains over benchmark schemes like local zero-forcing. We also analyze the trade-offs provided by the CU-distributed system, hence highlighting the significance of deploying multiple CUs in user-centric cell-free networks.

Multicast device-to-device communications operating underlay with cellular networks is a spectral efficient technique for disseminating data to nearby receivers. However, due to the critical challenge of having an intelligent interference coordination between multicast groups along with the cellular network, it is necessary to judiciously perform resource allocation for the combined network. In this work, we present a framework for a joint channel and power allocation strategy to maximize the sum rate of the combined network while guaranteeing minimum rate to individual groups and cellular users. The objective function is augmented by an austerity function that penalizes excessive assignment of low rate channels. The formulated problem is a mixed-integer-non-convex program, which requires exponential complexity to obtain the optimal solution. To tackle this, we exploit fractional programming and integer relaxation to obtain a parametric convex approximation. Based on sequential convex approximation approach, we first propose a centralized algorithm that ensures convergence to a limit point. Next, we propose a distributed algorithm in which via dual decomposition, separable sub-problems are formulated to be solved at the respective groups in cooperation with the base station. We provide convergence guarantees of the proposed solutions and demonstrate their merits by simulations, showing improvement in network throughput.

Next generation high data rate applications such as High-Definition (HD) video transmission place a heavy load on wireless network resources. To achieve the strict quality of service requirements of HD video in a multi-user network requires efficient resource allocation. Rate-distortion theory provides an appropriate basis for this. The Nash Bargaining Solution (NBS) is proven to generate an optimal solution to a rate-distortion based convex resource allocation problem. Particle Swarm Optimization (PSO) is a meta-heuristic optimizer capable of generating near-optimal solutions. PSO, however, is not constrained to convex optimization problems. The motivation of this work is to demonstrate a PSO implementation capable of achieving an optimal solution to the resource allocation problem. The capability of the PSO approach to reach an optimal solution suggests its potential for resource allocation in a network of heterogeneous applications where convex optimization methods do not apply.

Resource allocation and admission control algorithm based on non-cooperation game in wireless mesh networks

This paper is devoted to studying the processes of resource allocation in dynamic resource networks. In such networks, the capacities of the arcs depend on time. Resource allocation in the network occurs in discrete time. The resource of each vertex is distributed only between adjacent vertices according to some rules. The study of the processes of resource redistribution in such networks is carried out. The main goal is to develop methods for finding the limit state (distribution) of a resource in a dynamic resource network. It is shown that the approach based on the construction of an auxiliary network is also applicable to reduce the problem of resource allocation in a dynamic network to a similar problem in an auxiliary network. Theorems on the existence of a limit state on an auxiliary graph are proved for strongly regular periodic dynamical networks. To find the limit states, one can use the approaches which are developed for the shortest path problem in dynamic networks.

In this paper, we address the joint channel and power allocation problem for Device-to-Device (D2D) communications underlaying uplink cellular networks. We aim to maximize the sum-rate of D2D communications while fulfilling the QoS requirements for both cellular users (CUs) and D2D pairs (DUs). We first propose a heuristic channel assignment algorithm in which a cellular channel is allowed to be shared by multiple DUs and every DU can reuse multiple channels. Given this assigned channels, a price-based Stackelberg game is then formulated to obtain the sub-optimal transmission power for DUs. The simulation results demonstrate that our proposed joint channel and power allocation scheme has a superiority over a few existing resource allocation schemes for D2D communications in terms of D2D sum-rate.

Artificial Intelligence (AI)-enabled radios are expected to enhance the spectral efficiency of 5th generation (5G) millimeter wave (mmWave) networks by learning to optimize network resources. However, allocating resources over the mmWave band is extremely challenging due to rapidly-varying channel conditions. We consider several resource allocation problems for mmWave radio networks under unknown channel statistics and without any channel state information (CSI) feedback: i) dynamic rate selection for an energy harvesting transmitter, ii) dynamic power allocation for heterogeneous applications, and iii) distributed resource allocation in a multi-user network. All of these problems exhibit structured payoffs which are unimodal functions over partially ordered arms (transmission parameters) as well as over partially ordered contexts (side-information). Unimodality over arms helps in reducing the number of arms to be explored, while unimodality over contexts helps in using past information from nearby contexts to make better selections. We model this as a structured reinforcement learning problem, called contextual unimodal multi-armed bandit (MAB), and propose an online learning algorithm that exploits unimodality to optimize the resource allocation over time, and prove that it achieves logarithmic in time regret. Our algorithm’s regret scales sublinearly both in the number of arms and contexts for a wide range of scenarios. We also show via simulations that our algorithm significantly improves the performance in the aforementioned resource allocation problems.

We propose a distributed learning algorithm for the resource allocation problem in Device-to-Device (D2D) wireless networks that takes into account the throughput estimation noise. We first formulate a stochastic optimization problem with the objective of maximizing the generalized alpha-fair function of the network. In order to solve it distributively, we then define and use the framework of noisy-potential games. In this context, we propose a Binary Log-linear Learning Algorithm (BLLA), which is distributed across cells and converges to a Nash equilibrium of the resource allocation game. This equilibrium is also an optimal for the resource allocation optimization problem. A key enabler for the analysis of the convergence are the proposed rules for computation of resistance of trees of perturbed Markov chains. The convergence of BLLA is proved for bounded and unbounded noise, with fixed and decreasing temperature parameter. A sufficient number of estimation samples is also provided that guarantees the convergence to an optimal state in a single cell scenario and close to an optimal state in a multi-cell scenario. We assess the performance of BLLA by extensive simulations by considering both bounded and unbounded noise cases and show that BLLA achieves higher sum data rate compared to the state-of-the-art.

Underwater-wireless sensor network (WSN) are prone to the jamming attacks; mainly in case of reactive jamming. Reactive jamming has emerged as one of the critical security threat for underwater-WSN; this occurs due to the reactive jammer capabilities of controlling and regulating jamming duration. Further reactive jammer possesses low detection probability and high vulnerability; moreover the existing model has been designed in consideration with terrestrial-WSN. Hence these models possesses limited capabilities of detecting the jamming and distinguish among uncorrupted and corrupted packets; also it fails to adapt with the dynamic environment. Furthermore co-operative mechanism of jamming model is presented for utilizing the resources in efficient way; however only few existing work has been carried out through the co-operative jamming detection; especially under presence of multiple jammer nodes. For overcoming research issues this paper presents distributed resource allocation (DRA) model adopting cross layer architecture under presence of multiple jammer. DRA algorithm is designed for allocating resource to jammer user in optimal manner. Experiment outcome shows the proposed DRA model achieves much better detection rate considering multi-jammer environment. Thus aid in achieving much better detection accuracy, packet drop, packet transmission and resource utilization performance.

Heterogeneous cloud small cell network (HCSNet) with energy harvesting is a promising technique in the next generation mobile communications. However, the cross- tier and co-tier co-channel interference can be severe due to the spectrum sharing in inter-tier and intra- tier of HCSNet. This paper investigates the problem of power allocation and subchannel assignment with the consideration of cross-tier/co-tier interference mitigation, energy harvesting and incomplete channel state information. The power allocation problem in HCSNet is modeled as a non-cooperative game by introducing a time-varying cross-tier/co-tier interference pricing with simultaneous wireless information and power transfer. Subchannel allocation is modeled as a non-cooperative potential game by minimizing the the total interferences experienced by users on each subchannel. Iterative scheme of power optimization and subchannel allocation is proposed to obtain the Nash equilibrium points. Simulation results are presented to verify the effectiveness of the proposed algorithms in HCSNet.

The feasibility of resource allocation schemes is a major challenge in the practical implementation of wireless systems. Decentralized resource allocation is one such feasibility requirement, as it yields optimized schemes when no centralized processing is possible. This paper evaluates one particular type of decentralized interference management schemes in a full-duplex (FD) cellular network. Consider an FD cellular network that allows flexible partial overlap between the uplink (UL) and downlink (DL) frequency channel. The channel overlap generates self-interference, cross-mode interference and intra-mode interference. The performance of the system becomes, therefore, a function of the powers allocated at each base station (BS)-user pair, and the fraction of spectrum overlap between the DL and UL of each communicating pair. The paper considers the problem of maximizing a network-wide utility function subject to power constraints, so as to appropriately fine-tune the spectrum overlap and the transmit powers in a distributed manner across the network. The paper proposes solving the problem using the externalities approach, which can be implemented in a distributed fashion with a reasonable amount of information exchange between the network entities. The paper further examines three types of utility functions: the sum of log-rate, the network spectral efficiency, and the energy efficiency, so as to clearly describe the trade-off between the achieved rate, the consumed power, and UL/DL fairness in the FD setup. Simulation results highlight the convergence of the proposed distributed algorithm, and illustrate its performance under different utilities as compared to centralized solutions for various networks scenarios.

We study the engineering of heterogeneous cellular networks composed of a macrocell and some picocells by investigating the interplay of different network processes and parameters such as channel allocation, user association and reuse pattern (to control inter-cell interference between picocells). We formulate a joint association, channel allocation, and inter-cell interference management problem that relies on very few assumptions. This problem turns out to be an Integer Non-Linear program that is NP-hard. However, its structure is such that we can solve it exactly for relatively large size systems. We use optimal solutions as benchmarks to understand how different simple association schemes perform. Our results show the critical impact of the association rules on system performance and shows the interplay of the different processes and parameters. We believe that these insights will help design online association schemes in the future.

This study analyzes the performance of Device-to-Device (D2D) communication in drone-assisted cellular networks, focusing on coverage probability, system throughput, and energy efficiency. Simulation results reveal that increasing UAV altitude improves the probability of line-of-sight (LoS) communication, enhancing coverage probability up to 92% at optimal altitudes. However, higher altitudes also increase path loss, leading to a 15% reduction in total system throughput. Additionally, an optimal D2D user density of 50 users per km2 is identified to balance interference and resource utilization effectively. Uplink and downlink scenarios show that interference significantly affects the success transmission probability, which declines by 25% when the SINR threshold increases from 0 to 10 dB. In public safety scenarios, underlay in-band communication enables D2D users to establish reliable indirect links via cellular users, ensuring critical message delivery with energy efficiency improvements of up to 18%. These findings provide actionable insights for optimizing UAV deployment and resource allocation in D2D networks.

We present a general framework for the problem of quality-of-service (QoS) routing with resource allocation for data networks. The framework represents the QoS parameters as functions rather than static metrics. The formulation incorporates the hardware/software implementation and its relation to the allocated resources into a single framework. The proposed formulation allows intelligent adaptation of QoS parameters and allocated resources during a path search, rather than decoupling the path search process from resource allocation. We present a dynamic programming algorithm that, under certain conditions, finds an optimal path between a source and destination node and computes the amount of resources needed at each node so that the end-to-end QoS requirements are satisfied. We present jitter and data droppage analyzes of various rate-based service disciplines and use the dynamic programming algorithm to solve the problem of QoS routing with resource allocation for networks that employ these service disciplines.

Problem statement. Due to their mobility, flexibility, ease of deployment, and low cost, Autonomous Aerial Vehicle (AAV) play an important role in future wireless networks. However, their pratical implementation faces challenges, including energy constraints, dynamic channel variations, interference management, and the need for efficient resource allocation to ensure seamless connectivity for ground users. Traditional optimization methods often fail to adapt to these complexities in real-time, limiting the effectiveness of AAV-assisted wireless networks. The aim of the work provides a comprehensive review of resource allocation in AAV-assisted wireless networks, focusing on power and bandwidth optimization strategies, as well as the key challenges in ensuring efficient and reliable communication. Methods: used in this study include a systematic review of existing literature, analyzing optimization approaches such as Game Theory, Artificial Intelligence for efficient resource allocation in AAV-assisted wireless networks. Novelty: this study analyzes resource allocation challenges in AAV-assisted network, focusing on the interdependence of power and bandwidth allocation. It explores optimization techniques like Game Theory, and Artificial Intelligence. Results. The analysis in this paper demonstrates that Game Theory and Artificial Intelligence based approaches significantly improve resource allocation efficiency. Additionally, the study identifies key challenges, including heterogeneous density network, security concerns, and complex channel modeling, providing insights for future research. Practical / Theoretical Relevance: This study advences the theoretical understanding of resource allocation in AAV-assisted wireless networks by integrating optimization strategies from Game Theory and Artificial Intelligence. Pratically, it provides insight into enhancing network efficiency, adaptability and security, making AAV-based communication more viable for real-world applications, such as disaster recovery, remote areas coverage, and IoT data collection.