In this work, a LOcation based RAN resource Management and Access (LOMA) map is designed for future radio access networks (RAN) to allocate radio resources in complex wireless environment, and to facilitate uplink/downlink data transmissions. Enabled by the AI and high-precise positioning techniques, the LOMA map can associate a set of radio resources and data transmission parameters (e.g, transmit power, MCS level) with a geographical location in the RAN area. Given the LOMA map, each user equipment (UE) or infrastructure associated with the RAN can directly determine the radio resources and parameters used for uplink/downlink data transmissions according to its location. An AI enabled LOMA map generation method are proposed to generate LOMA maps according to the statistical traffic and wireless environment data collected by UEs and infrastructures. A reinforcement learning (RL) based algorithm is further proposed in the LOMA map generation method to dynamically quantify the available radio resources according to the real-time data traffic and the performance of applied resource scheduling scheme. Case studies with numerical results are presented to show the benefits provided by LOMA map technique in terms of increasing resource sharing efficiency, reducing signal overheads in data transmissions, and enabling resource scheduling schemes with less computing cost.

In this paper, we present low-complexity algorithms for cache placement problem in software-defined radio access networks. Our key design target is that the packet delay from any cache to its associated users is below a threshold. This feature is a significant improvement compared to conventional approaches relying on the average hop count. We introduce a traffic engineering solution for joint multiserver selection and rate allocation. This algorithm is used to minimize the cache placement cost function, such as delivery cost per bit. Our cache placement method allows prediction of user throughput and thus the network throughput. Extensive simulation results verify that our method can give optimal or near optimal network throughput while guaranteeing packet delay performance.

Network slicing often involves instantiation of certain network functionality into network nodes, possibly subject to function chaining constraints. In this paper, we introduce the novel concept of elastic service function chain (SFC), which is an ordered list of virtual functions that may be optional or recursive, and we address network slicing with such chaining constraints as a Software Defined Topology (SDT) problem. We formulate the SDT problem as a combinatorial optimization problem that includes a multi-commodity flow problem and a bin packing problem as sub-problems. Since it inherits NP hardness from the bin packing sub problem, we develop a heuristic algorithm to tackle it. The algorithm's effectiveness and performance are evaluated via simulation study.

MTC services are undergoing rapid increase; billions of MTC connections can be anticipated in a few years. When supporting MTC services in telecommunications networks, MTC traffic characteristics and its unique service requirements should be exploited in order to optimize resource utilization and offer satisfactory service quality for all devices. In this paper, we present an Information-Centric Virtual Network Architecture (IC-VNA) that allows to meticulously combine both the customer and operator specific virtual functions along with their requirements to construct an augmented virtual network topology. It is shown that through the IC-VNA it is feasible to rapidly roll-out highly programmable and flexible services while providing high degree of scalability and cost effectiveness for both MTC service providers and network operators.

In this paper, we propose a cellular cognitive-radio vehicular ad hoc network (CCR-VANET) which consists of cellular network (primary network) and vehicular ad hoc network (secondary network). The two coexisting networks share the uplink spectrum of cellular network. The moving pattern of all vehicles is described as the classic Car-Following model. A cognitive carrier sense multiple access with collision avoidance (cognitive- CSMA) protocol with two-stage decision is investigated to opportunistically access the uplink spectrum. Based on this model and protocol, the transmission opportunity is derived by using stochastic geometry tools. Finally, simulation results show that, with cognitive-CSMA protocol and invariable transmission power of coexisting networks, the CCR-VANET transmission opportunity increases with the increasing of maximum received beacon power threshold, predefined carrier sensing threshold and the number of subchannels, while decreases with the increasing of the density of active primary transmitters.

Machine to machine (M2M) traffic is characterized as low-rate, small-packet traffic with correlated transmissions. In this paper, we propose a two-phase traffic control mechanism (2PTC) for carrying M2M traffic in the future fifth generation (5G) networks. In this mechanism, the packets from each machine are directed to a virtual serving gateway associated with the machine, which receives and aggregates traffic from multiple machines and forwards the aggregate traffic to the sink. The first communication phase takes place through a simple single-path routing technique, while the second phase is empowered by multipath traffic engineering (TE) optimization. At the virtual serving gateways, traffic aggregation (TA) may include network-layer flow trunking and application-layer content compression. At the core of 2PTC is joint gateway selection and machine-to-gateway association that favors TA potentials while minimizing association cost and virtual serving gateway count. The described problem is formulated as a mixed integer programming optimization problem. As the structure of the formulation is inherently NP hard, the problem is solved using relaxation and rounding techniques, whose solution quality is evaluated through numerical analysis. We also implement the solution in a network simulator and evaluate the performance of 2PTC, through extensive simulations. Simulation results indicate that routing M2M traffic to properly selected virtual serving gateways for TA can alleviate the large-quantity small-packet problem, while enhancing the performance of the background traffic. This paves the way for further performance enhancement or enabling new features when deploying virtual network functions at virtual serving gateways.

We consider the problem of traffic engineering (TE) in a software-defined radio access network (SD-RAN) consisting of backhaul and radio access parts. The role of TE is to route traffic from sources to their corresponding destinations in the network based on its knowledge of network topology and link capacities. Unlike wired networks, where links have fixed capacities, the capacity of a wireless link in a radio access network depends on the amount of resource (e.g., frequency, time, etc.) assigned to it by wireless link scheduler. Therefore, TE for SD-RAN also involves optimization of link/user scheduling. We propose a cross-layer solution by decoupling the SD-RAN TE problem into a wired network TE problem and a user scheduling problem using alternating direction method of multipliers (ADMM). The ADMM iterations are guaranteed to converge if the objective function of the SD-RAN TE problem is convex. The simulation results show that our proposed online implementation closely follows the optimal solution in a dynamic setting where individual flows independently join/leave the network.

We consider traffic engineering (TE) in networks where quality of service (QoS)-guaranteed as well as best effort (BE) services are present. We propose a joint TE optimization technique for both QoS and BE traffic using multi-objective optimization framework. We show that joint TE optimization results in more efficient network resource usage compared to sequential TE methodology where TE decision (finding paths and traffic splitting among paths) is made for QoS-guaranteed traffic first by assigning appropriate network resources and then TE decision is made for BE traffic according to remaining network resources. Furthermore, we consider QoS traffic demand fluctuations and propose a joint TE problem formulation to address both QoS traffic demand fluctuations and efficient network resource usage for BE traffic.

We consider central traffic engineering in a software-defined radio access network (SD-RAN) consisting of routers, wireless access nodes, and wired/wireless links. We present a multi-path traffic engineering formulation for downlink transmission considering both backhaul and wireless access constraints. We show large throughput and quality of experience (QoE) gains over traditional single-path transmission. In order to increase robustness against unpredicted changes in the radio access network, we propose using link buffer status as feedback to traffic engineering and present a heuristic method for traffic engineering to adjust flow allocations according to buffer status observation.

We consider a cloud-based heterogeneous network of base stations (BSs) connected via a backhaul network of routers and wired/wireless links with limited capacity. The optimal provision of such networks requires proper resource allocation across the radio access links in conjunction with appropriate traffic engineering within the backhaul network. In this paper, we propose an efficient algorithm for joint resource allocation across the wireless links and flow control over the entire network. The proposed algorithm, which maximizes the min-rate among all the transmitted commodities, is based on a decomposition approach that leverages both the alternating direction method of multipliers (ADMM) and the weighted-MMSE (WMMSE) algorithm. We show that this algorithm is easily parallelizable and converges globally to a stationary solution of the joint optimization problem. The proposed algorithm can also be extended to networks with multi-antenna nodes and other utility functions.

In this paper, we propose a step-wise optimal low power node (LPN) deployment algorithm for LTE heterogeneous networks. Our proposed LPN deployment algorithm takes signal quality, relative loading among macro cells and LPN cells, and user density into account. Simulation results show that the proposed algorithm can achieve close to 100% coverage gain and more than 50% average cell throughput gain over a random LPN deployment scheme.

This paper presents an optimization framework for downlink transmission parameters of mobile cellular systems. A typical network optimization approach is to divide the network into disjoint clusters of base-stations (BS). Optimization is then performed within each cluster for important parameters such as transmit power, precoder, etc. This approach is widely adopted in academic research and industrial standard bodies, e.g. 3GPP LTE-Advanced. While reducing the optimization complexity, this strategy suffers from a performance limit due to interference from the nodes outside cluster. We thus propose a framework to overcome this limit by allowing clusters to exchange their parameters and optimization information via low-rate and non-zero delay backhauls. System-level simulations for LTE downlink transmit power optimization show that the proposed optimization model, while having the low-complexity of cluster-based approach, could nearly achieve the performance of network-wise optimization. Therefore, this model is particularly suitable for optimization of 4G and beyond-4G cellular networks.