The ongoing evolution of personal mobile devices capabilities and wireless technologies result in a huge growth of traffic on mobile networks (3G/4G). One of the most promising approaches to handle this data crisis is to offload the fast growing traffic onto femtocell networks. Since both the 3G/4G macrocell and femtocells operate on the same licensed spectrum, the cross-tier interference should be well managed. In this paper, we propose a utility-based transmission power allocation policy for the uplink transmission in femtocell networks. Our main motivation is to design the transmission power allocation policy aiming at optimizing the joint utility of femtocell users (FUs) subject to a interference temperature constraint at the macrocell base station (MBS) side. We provide a novel floating-ceiling water-filling (FCWF) algorithm with little computational overhead to obtain the optimal solution for the joint utility optimization problem. Numerical results confirm that the joint utility and average SINR can be significant improved with the proposed method.
In mobile edge computing systems, base stations (BSs) equipped with edge servers can provide computing services to users to reduce their task execution time. However, there is always a conflict of interest between the BS and users. The BS prices the service programs based on user demand to maximize its own profit, while the users determine their offloading strategies based on the prices to minimize their costs. Moreover, service programs need to be pre-cached to meet immediate computing needs. Due to the limited caching capacity and variations in service program popularity, the BS must dynamically select which service programs to cache. Since service caching and pricing have different needs for adjustment time granularities, we propose a two-time scale framework to jointly optimize service caching, pricing and task offloading. For the large time scale, we propose a game-nested deep reinforcement learning algorithm to dynamically adjust service caching according to the estimated popularity information. For the small time scale, by modeling the interaction between the BS and users as a two-stage game, we prove the existence of the equilibrium under incomplete information and then derive the optimal pricing and offloading strategies. Extensive simulations based on a real-world dataset demonstrate the efficiency of the proposed approach.
Recent studies have shown that the traffic load is often distributed unevenly among the access points. Such load imbalance results in an ineffective bandwidth utilization. The load imbalance and the consequent ineffective bandwidth utilization could be alleviated via intelligently selecting user-AP associations. In this paper, the diversity in users' utilities is sufficiently taken into account, and a Stackelberg leader-follower game is formulated to obtain the optimal user-AP association. The effectiveness of the proposed algorithm on improving the degree of load balance is evaluated via simulations. Simulation results show that the performance of the proposed algorithm is superior to or at least comparable with the best existing algorithms.
In order to improve the quality of service (QoS) for mobile users (MUs) and save investment cost for deploying new cellular base station, mobile network operators (MNOs) are deploying wireless local area network (LAN) access points (APs) to offload MU's traffic from cellular network to wireless LAN. However, offloading too much traffic from cellular network may impair MNO's profit since the cellular network price is higher than that of wireless LAN, whose price is low or even zero. Therefore, how to deploy wireless LAN APs to offload traffic without impairing MNO's profit is a critical problem for MNOs. As far as the authors understand, existing studies about deployment of wireless LAN APs do not consider MNO's profit and are usually in a heuristic manner. In this paper, we study the location-based advertising (LBA) leveraged wireless LAN deployment, where MNO may also collect revenue by selling LBA service in different locations to advertisers. We formulate MNO's profit maximization problem by considering different MU's demand in different locations, wireless LAN price for MUs, and revenue from LBA service. Extensive simulations are conducted to validate our analytical results.
This paper proposes a novel hopping height controller for legged robot that significantly reduces the number of measured variables without detecting the ground contact. The controller is composed of a positive velocity feedback loop and a nonlinear parameter, e.g. saturation, of which the limit dictates the hopping height. A command velocity feedforward loop is used to improve the system response, plus a closed loop position control of the leg actuator is added. A system model where all aspects but the saturation limit is linearized is used to analyze the controller performance via the describing function technique. The conditions for self-excited hopping are theoretically derived as well as it is successfully demonstrated via simulation using SYSU-HOPPER legged robot model. A detailed nonlinear model is developed to explain the main findings from the simulation. It is found that the oil compressibility causes a sustained oscillation of the robot when the hopping control signal is zero.
Nowadays, with great advances in transportation systems, intelligent vehicles are equipped with a rich set of sensors (e.g., cameras, radars, thermometers and ultrasonic sensors), triggering the emergence of a new vehicle-centric sensing paradigm, i.e., vehicular crowdsensing (VCS). By recruiting sensor-embedded vehicles to accomplish sensing tasks, VCS is able to collect city-scale information in a more efficient way, facilitating a series of smart services, such as environmental monitoring, object movement tracking, traffic jam alerts, noise mapping, parking space identification, and digital map updating. This chapter presents an edge-assisted vehicular crowdsensing (EVCS) framework where a deep reinforcement learning (DRL)-based incentive mechanism is introduced to ensure efficient vehicle recruitment and data collection. Specifically, edge computing utilizes edge nodes, also known as a sub-sensing platform (SSP), to perform distributed vehicle recruitment and data processing. Furthermore, the competitive interaction between the SSP and vehicles is formulated as a one-leader multi-follower Stackelberg game, where a multi-agent DRL-based incentive mechanism is developed to assist vehicles to choose optimal bid prices for participation.
Non-uniform distributions of mobile nodes are the norm for a mobile network. Often, there can be concentration areas or grouping of nodes. Early work has explored these features to help message disseminations. However, a mobile network application can generate complex mixing mobility patterns that render these work less effective and efficient. In addition, many applications run with in a sparse mode, namely, the network may not be connected all the time. In this paper, we propose two entropy based metrics to identify the nodes with different mobility patterns and further use the metrics to accomplish clustering. Aiming at low-end devices which have no inputs of velocity and location, we employ neighbor information through hello messages and draw speed implication through neighbor change rates. The entropy based metrics are used in a clustering algorithm to find stable nodes as cluster heads. According to the the simulation results, two metrics, namely, speed entropy and relation entropy can be applied to distinguish active nodes from stable nodes in different group mixing configurations. The simulations also show that our new metric-based clustering algorithm generates more stable clusters.
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