Search

With the development of new radio (NR) technologies, sidelink (SL) transmission plays a more vital role in supporting low latency, higher reliability vehicle-to-everything (V2X) communications. Compared with the Long-Term Evolution (LTE), NR V2X aims to support enhanced V2X use cases with more stringent latency and reliability requirements. 3GPP introduces NR V2X distributed resource allocation to fulfill the low latency requirement to reduce the delay caused by requesting transmission resources from gNB. In distributed mode, UEs autonomously select resources without the scheduling from gNB. NR V2X introduces the physical sidelink feedback channel (PSFCH) to enhance transmission reliability. This paper presents the resource allocation mechanism for NR V2X distributed mode. Since the SL feedback channel is a new feature different from LTE V2X, we discuss the potential enhancements on resource allocation utilizing SL feedback channel to improve the reliability. This paper proposes a Receiver Grant-based resource allocation, which allows the receiver to send the grant with the feedback channel. By allowing the transmitter to indicate the pre-selected resources before the end of the previous reservation, the receiver can inform the transmitter about the collided resource selection through the PSFCH. The analytical models for NR V2X distributed mode with the feedback channel are also provided for UE to estimate the collision ratio. The simulation results and the analytical model show that the proposed Receiver (Rx) Grant-based resource allocation improves transmission reliability in terms of collision ratio. For future advanced V2X use cases, the analytical models provide an evaluation method and help UE make decisions to meet the reliability requirements. [ABSTRACT FROM AUTHOR]

Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a blended strategy to perform effective resource sharing. In this paper, we exploit deep learning techniques predict vehicles’ mobility patterns. Then we propose an architecture consisting of centralized decision making and distributed channel allocation to maximize the spectrum efficiency of all vehicles involved. To achieve this, we leverage two deep reinforcement learning techniques, namely deep Q-network (DQN) and advantage actor-critic (A2C) techniques. In addition, given the time varying nature of the user mobility, we further incorporate the long short-term memory (LSTM) into DQN and A2C techniques. The combined system tracks user mobility, varying demands and channel conditions and adapt resource allocation dynamically. We verify the performance of the proposed methods through extensive simulations and prove the effectiveness of the proposed LSTM-DQN and LSTM-A2C algorithms using real data obtained from California state transportation department. [ABSTRACT FROM AUTHOR]

Network slicing in future 5G systems enables the provision of multitenant networks in which a network infrastructure owned by an operator is shared among different tenants, such as mobile virtual operators, over-the-top providers or vertical market players. The support of network slicing within the radio access network requires the introduction of appropriate radio resource management functions to ensure that each tenant gets the required radio resources in accordance with the expected service level agreement (SLA). This paper addresses radio admission control (RAC) functionality in multiservice and multitenant scenarios as a mechanism for regulating the acceptance of new guaranteed bit rate service requests of different tenants. This paper proposes an optimization framework that models the RAC as a semi-Markov decision process and, as a result, derives an optimal decision-making policy that maximizes an average long-term function representing the desired optimization target. A reward function is proposed to capture the degree of tenant satisfaction with the received service in relation to the expected SLA, accounting for both the provision of excess capacity beyond the SLA and the cost associated with sporadic SLA breaches. The proposed approach is evaluated by means of simulations, and its superiority to other reference schemes in terms of reward and other key performance indicators is analyzed. [ABSTRACT FROM AUTHOR]

One of the pillars of the 5G architecture is network slicing, in which hardware, radio, and power resources are virtualized as a logical network taking into account the requirements of diverse applications. While ensuring performance isolation among different slices, resource allocation in 5G Radio Access Networks (RANs) is associated with different challenges due to network dynamics and the different applications’ requirements. In this paper, we have considered the allocation of power and radio resources to rate-based as well as resource-based users. We propose an energy-efficient deep reinforcement learning-assisted resource allocation (EE-DRL-RA) method for RAN slicing in 5G networks. The main idea of the proposed method is to exploit a collaborative learning framework that includes deep reinforcement learning (DRL) and deep learning (DL) to decide on resource allocation in the RAN. Specifically, we use DL for decision-making on resource allocation on a large time-scale and DRL for decision-making on resource allocation on a small time-scale. The asynchronous advantage actor-critic (A3C) and the stacked and bidirectional long-short-term-memory (SBiLSTM) network are used as DRL and supervised DL methods, respectively. Furthermore, we determine the optimal power and resource blocks (RBs) for rate-based users by formulating the energy-efficient power allocation (EE-PA) problem as a non-convex optimization problem and solve it by an efficient iterative algorithm. Our proposed approach is unique in that it simultaneously allocates power and RBs while ensuring slice isolation with low computational and time complexity. Simulation results show that EE-DRL-RA yields better performance compared to a state-of-the-art published method in terms of convergence speed, computational complexity, energy efficiency, and the number of accepted users as well as the degree of inter-slice isolation. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel adaptive cloud resource allocation model based on Semi-Markov Decision Process (SMDP) and Reinforcement Learning (RL) algorithm in vehicular cloud system. The issue of adaptive resource allocation for vehicular request is formed as an SMDP in order to gain the dynamics of vehicular requests’ arrival and departure. An optimized decision is made to guarantee the Quality of Service (QoS) of the vehicular cloud system and the Quality of Experience (QoE) of the vehicular users as well as to maximize the total system reward of the vehicular cloud system in consideration of the balance between the vehicular cloud resource expense and the system income. Furthermore, to capture the mobility feature of the vehicular cloud system, we also apply a neural-network-based RL algorithm to resolve our proposed SMDP-based adaptive cloud resource allocation model. Firstly, we use a Planning algorithm to get the action values under certain state-action pairs, which are the initial samples to train the neural network. Then the RL is used to update the parameters of the neural network, train the neural network and adaptively improve the decision strategy. Subsequently, an adaptive vehicular cloud resource allocation scheme which can approach the optimal strategy is obtained without the knowledge of the distribution function of vehicular requests’ arrival and departure during the RL process. The simulation results show that our proposed adaptive cloud resource allocation model for vehicular cloud system can reduce the probability of delay in processing requests and achieve high system rewards in comparison with the regularly used greedy resource allocation method. The performance of the RL solution approaches that of traditional value iteration solution for our proposed adaptive cloud resource allocation model. [ABSTRACT FROM AUTHOR]

Cache management and resource scheduling schemes enhance the mobility and flow control in SDN. Dynamic and opportunistic spectrum accessing modes enhance the resource allocation and reduce the cache delays in CRN. In this paper, the proposed cognitive radio enabled cache map-and-route (CR-CMR) scheme considers the multi-layered framework using context mapping and decision making approach in SDN. User requests are classified based on mapped and unmapped contexts. CR-CMR control plane integrates the cognitive radio control unit, centralized SDN controller, context mapping unit (CMU), and decision making unit (DMU). The control plane interacts with the virtual plane and performs the cache map-and-route process. Base rule analyzes the context variance and cache variance. The decision-making rules apply step-wise decision approach to map the service requests of unmapped contexts and optimize the cache map-and-route process. The splay tree analysis applied at virtual cache centers improves the resource availability and minimizes the mapping cost. CR-CMR supports scalability and minimizes the cache variance. The virtual cache and data centers verify the route tables of one-hop, two-hop neighbors and establish the forward decision paths with optimal route cost and minimal latency in SDN. [ABSTRACT FROM AUTHOR]

Network slicing in fog radio access networks is recognized as a cost-efficient solution to support future diverse use cases. However, with the number of user equipments (UE) fast increasing, the centralized resource allocation architecture for network slicing can put heavy burdens on the global radio resource manager (GRRM), and meanwhile slice customization is not easy to achieve. To overcome the two issues, a hierarchical radio resource allocation architecture is proposed in this paper, where the GRRM is responsible for allocating subchannels to local radio resource managers (LRRMs) in slices, which then allocate the assigned resources to their UEs. Under this architecture, a hierarchical resource allocation problem is formulated, and the problem is further modeled as a Stackelberg game with the GRRM as the leader and LRRMs as followers, considering the hierarchy between the GRRM and LRRMs. Due to the NP-hardness of the followers’ problems, a process based on exhaustive search is first proposed to achieve the Stackelberg equilibrium (SE). Nevertheless, when the network scale is large, achieving SE within limited decision making time is impractical for game players. Facing this challenge, the GRRM and LRRMs are seen as bounded rational players, and low complexity algorithms are developed to help them achieve local optimal solutions that lead to a weak version of SE. Simulation results show that there exists a tradeoff between the performance of slices, and the low complexity algorithms achieve close performance to that of exhaustive search and outperform other baselines significantly. [ABSTRACT FROM AUTHOR]

The drastic growth of mobile traffic greatly challenges the capacity of mobile infrastructures. Dense deployment of low-power small cells helps alleviate the congestion in the radio access network, yet it also introduces large complexity for network management. Software-defined radio access network has been proposed to tackle the added complexity. However, existing software-defined solutions rely on a fully centralized control plane to make decisions for the whole network, which greatly limits the scalability and responsiveness of the control plane. In this paper, we propose a hierarchical software-defined radio access network architecture. The proposed architecture leverages the hierarchical structure of radio access networks, deploying additional local controllers near the network edge. Utilizing the intrinsic locality in radio access networks, it offloads control tasks from the central controller to local controllers with limited overhead introduced. Under the architecture, a distributed optimization framework is proposed, and a typical optimization problem is studied to illustrate the effectiveness of the proposed architecture and framework. Both analysis and experiments validate that the proposed architecture and framework can improve the network objective during the optimization, meanwhile balancing load and improving scalability and responsiveness. [ABSTRACT FROM AUTHOR]

In this paper, we study an uplink network configuration in a two-tier macro–femto heterogeneous system with hybrid access control. We consider a system where one macro base station (MBS) and a cluster of adjacent femto base stations (FBSs) together serve a number of mobile users. In this system, base stations and users make decisions in various network configuration processes with different optimization objectives. Such decision-making processesz are usually correlated, and an efficient mechanism is needed to coordinate the decision makers. In this paper, we propose a five-stage network configuration mechanism where access control, resource allocation, and power management are sequentially performed at the base stations and users, respectively. We show that this mechanism provides incentive for the FBSs to operate at the hybrid access mode. We model the configuration mechanism as a multistage decision-making process and formulate a multilevel optimization problem. We analyze the problem in a bottom-up manner and propose efficient algorithms to solve the optimization problem in each level sequentially. Simulation results show that the proposed network configuration mechanism achieves a higher system utility than configuration mechanisms with topology-based hybrid access or closed access. [ABSTRACT FROM AUTHOR]