In this letter, we address joint channel and power allocation in a device-to-device (D2D) network underlaying a cellular network in a time-varying environment. A fully distributed solution, which ...does not require information exchange, is proposed to allocate channel and power levels to D2D pairs while ensuring the quality of service (QoS) of the cellular user equipments (CUEs). The problem is modeled as a Stackelberg game with pricing. At the leader level, base station sets prices for the channels to ensure the QoS of the CUEs. At the follower level, D2D pairs use an uncoupled stochastic learning algorithm to learn the channel indices and power levels while minimizing the weighted aggregate interference and the price paid. The follower game is shown to be an ordinal potential game. We perform simulations to study the convergence of the algorithm.
Ultrareliable low-latency communication (URLLC) is one of the important service categories of 5G, where high reliability and low latency are to be met simultaneously. Coordinated multipoint (CoMP), ...where a user equipment (UE) is connected to multiple base stations simultaneously, can provide high reliability for interference limited cell-edge UEs. Short-packet communication and 5G physical layer enablers combined with CoMP can be a potential solution to meet the quality of service requirements of the URLLC traffic. However, there exist some challenges in using CoMP for URLLC in a cloud radio access network (C-RAN) architecture; mainly fronthaul capacity and remote radio head resource availability constraints. These resource constraints are addressed in this article; and a resource allocation scheme based on the effective bandwidth concept and 5G enablers is proposed, which can meet the reliability and delay requirements of the URLLC traffic. End-to-end error and latency components are identified and the interplay existing among the error components is utilized for bandwidth minimization. A novel packet delivery mechanism, queuing strategy, and time-frequency resource allocation for CoMP enabled URLLC in C-RAN architecture are proposed. The proposed scheme shows superior performance, in terms of the resource utilization and UE satisfaction, when compared with the existing techniques.
When multiple wireless body area networks (WBANs) exist in close proximity to each other, the inter-user interference considerably degrades the signal to interference plus noise ratio of the packets ...arriving at each WBAN coordinator. Also, the propagation paths within each WBAN experience fading due to the continuous changes in the body posture and mobility of the human body. The most preferred coexisting mechanisms specified in the IEEE 802.15.6 standard is the channel hopping mechanism, which fails to consider the varying radio environment and obtained reward in its channel selection. Thus, our paper investigates this channel selection problem for interference mitigation in a time-varying environment. We formulate this channel selection problem as a finite repeated potential game and propose two learning algorithms, Stochastic Learning Algorithm (SLA) and Stochastic Estimator Learning Algorithm (SELA) to achieve the Nash Equilibrium (NE) of the game. Numerical results show the convergence of the learning algorithms to the NE point of the game. The performance evaluation and impact of parameters on these two algorithms are also analyzed in our paper.
Real-time continuous and remote health monitoring has become widespread due to the developments in Wireless body area networks (WBANs). Based on the criticality of health data to be transmitted, ...regular healthcare data and critical emergency health data must be provided differential service. In this paper, we consider the beyond WBAN communication in a system comprising multiple WBANs with different quality of service (QoS) requirements and multiple access points (APs), and propose two hybrid approaches for resource allocation. In the first approach, the AP association to the WBANs and channel allocation to the APs are done jointly and is modelled as an optimization problem, which is computationally complex and it also requires global network information. In order to reduce the involvement of APs in making decisions for resource allocations of WBANs, the problem is reformulated as a Stackelberg game with price update, which guarantees QoS of the critical users. A learning based algorithm, namely distributed learning for Pareto optimality, is used by the normal users, in this second approach. The performance of both the proposed approaches are evaluated and compared, in terms of the throughput of the critical and normal users as well as the QoS guarantee of the critical users.
Increase in the popularity of non-invasive health monitoring systems has led to the large deployment of dedicated access points (APs) in a medical environment. Most of the existing literature assumes ...the wireless body area network (WBAN) association with the AP to be a nonissue but several factors can affect the medical quality of service (mQoS) requirements of the WBANs. The factors like the population associated with each AP, link quality between each WBAN and AP, and available capacity with each AP are some of them. Therefore to establish the best connectivity between each WBAN and its AP, a non-cooperative game based on a no-regret learning algorithm is proposed. A novel inter-WBAN communication model for packet transmission from each WBAN to its associated AP is also proposed. It is based on a dynamic priority scheduling in each WBAN coordinator and multi-class slotted Aloha (S-Aloha) reservation for data transmission from multiple WBAN coordinators to the APs. Simulation results show that the proposed scheme reduces the packet delays experienced by both the critical as well as the normal medical packets, when compared with two existing schemes, under different scenarios. The convergence behavior and the equilibrium efficiency of the proposed model are also validated.
In this letter, we present a two-phase distributed resource allocation algorithm for a Device-to-Device (D2D) network underlaying a cellular network. In the first phase, D2D pairs learn resource ...block (RB) of the cellular user equipment (CUEs) to be used, while playing an interference minimization game. Each D2D pair is ensured an interference below an upper limit, which is chosen as the criterion for admission control. In the next phase, power allocation for the device pairs allocated to each RB is carried out to maximize their sum rate while ensuring a minimum rate to the CUEs using a pricing scheme. We perform simulations to evaluate the performance of the proposed scheme.
Widespread use of wireless body area networks (WBANs) has led to the deployment of dedicated access points (APs) to serve them. An increased traffic load at an AP can degrade the performance of all ...associated WBANs. To resolve this issue, deployed APs should judiciously invest in the available bandwidth. We model the WBAN-AP interactions as a multi-leader multi-follower Stackelberg game. The multi-leader game is a non-cooperative game, in which each AP decides its bandwidth to maximize its own profit. The multi-follower game is an evolutionary game, in which each WBAN decides which AP can provide the best QoS at the least cost. The game models are evaluated to verify the effectiveness of our approach.
Link adaptation for downlink 5G ultra-reliable low latency communication (URLLC) is quite challenging. While reduced feedback schemes for link adaptation are crucial to achieve high spectral ...efficiency, accuracy of sub-band channel quality indicator (CQI) reports is more critical in 5G URLLC because of the ultra-high reliability and efficient resource utilization requirements. The existing differential feedback mechanism cannot reconstruct sub-band CQIs at the BS exactly. In this paper, existing differential feedback mechanism for link adaptation is first investigated using analytical and simulation models to verify its limitations in reconstructing the sub-band CQIs and to study its impact on URLLC reliability and resource utilization efficiency. Then a cognitive link adaptation using a controlled differential feedback scheme for sub-band CQIs is proposed based on the investigations. Extensive simulation results are provided to show the superior performance of the proposed cognitive solution and to illustrate the theoretical analysis.
We investigate the problem of resource block (RB) and power allocation jointly and in a distributed manner using game theoretic learning solutions, in an underlay device-to-device network where ...device pairs communicate directly with each other by reusing the spectrum allocated to the cellular users. We formulate the joint RB and power allocation as multi-agent learning problems with discrete strategy sets; and suggest partially distributed and fully distributed learning algorithms to determine the RB and power level to be used by each device pair. The partially distributed algorithms, viz., Fictitious Play and its variant Fading Memory Joint Strategy Fictitious Play with Inertia, achieve Nash Equilibrium (NE) of the sum-rate maximization game in a static wireless environment. The completely distributed and uncoupled Stochastic Learning Algorithm converges to pure strategy NE of the interference mitigation game in a time-varying radio environment. We provide proofs for the existence of NE and convergence of the learning algorithms to the NE. Performance of the proposed schemes are evaluated in log-normal, Rayleigh and Nakagami fading environments and compared with an existing hybrid scheme and a centralized scheme. The simulation results show that the partially distributed schemes give the same performance as the centralized scheme, and the fully distributed scheme gives similar performance as the hybrid scheme but with much reduced signaling and computation overhead.
Device to device (D2D) communication that provides high data rate proximity based direct communication between users, along with simultaneous wireless information and power transfer (SWIPT) that ...extracts energy from the received RF power, can achieve high energy and spectral efficiencies together with low latency communication. Heterogeneous networks, that employ efficient frequency reuse, provide high gains in the coverage and capacity of the cellular networks. SWIPT helps in converting the harmful interference, incurred by the frequency reuse of D2D tier underlaying the Hetnet, into energy that can be stored for the future. This work presents a resource allocation and power control scheme for SWIPT enabled underlay D2D networks, designed to improve the energy efficiency and the amount of energy harvested while ensuring the minimum required data rates for the users. Resource allocation is performed using a stable many-to-one matching game model inspired from Gale-Shapley algorithm. Two techniques of SWIPT, namely power splitting SWIPT and time splitting SWIPT, are considered and the resource allocation schemes are designed for both. Simulation results demonstrate the superior performance of the proposed algorithm compared to an existing work in single tier network with power splitting SWIPT. The work is extended to a typical 5G HetNet scenario, and extensive simulations are done to compare the performance of the two SWIPT architectures in the HetNet scenario.
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