Network slicing is emerging as a promising method to provide sought after versatility and flexibility to cope with ever-increasing demands. To realize such potential advantages and to meet the challenging requirements of various network slices in an on-demand fashion, we need to develop an agile and distributed mechanism for resource provisioning to different network slices in a heterogeneous multi-resource multi-domain mobile network environment. We formulate inter-domain resource provisioning to network slices in such an environment as an optimization problem which maximizes social welfare among network slice tenants (so that maximizing tenants’ satisfaction), while minimizing operational expenditures for infrastructure service providers at the same time. To solve the envisioned problem, we implement an iterative auction game among network slice tenants, on one hand, and a plurality of price-taking subnet service providers, on the other hand. We show that the proposed solution method results in a distributed privacy-saving mechanism which converges to theoptimal solutionof the described optimization problem. In addition to providing analytical results to characterize the performance of the proposed mechanism, we also employ numerical evaluations to validate the results, demonstrate convergence of the presented algorithm, and show the enhanced performance of the proposed approach (in terms of resource utilization, fairness and operational costs) against the existing solutions.

The exponential growth of data traffic volume dominates the demand for the next generation mobile networks (5G). The consistent and satisfied quality of experience (QoE) is one of the leading challenges of provisioning services in 5G mobile networks. Thus, in this paper, we propose a novel adaptive prefetching scheme to compensate the undesired transmission conditions in the 5G mobile network by extending the content prefetching concept from the users to the network. Specifically, an optimization problem is proposed for a prefetching scheme that adaptively retrieves users' data to access nodes and (or) user equipments (UEs) before the actual requests according to the network status, QoE status, predicted data rates, and mobility patterns of users. For the sake of tractability, the prefetching problem is transferred to a convex problem that can be solved efficiently. Accordingly, to implement the proposed schemes in the 5G network, system interactions among entities in the network are designed to realize prefetching-related functions. A signaling protocol to support the adaptive prefetching scheme is also presented. Simulation results show that an adaptive prefetching scheme can improve the network performance significantly.

Simultaneous bidirectional transmission and reception of signals on a single channel in a full duplex (FD) system is gaining increased attention from both academia and industry. Recent advances in radio transceiver design have been able to minimize the self interference (SI) to a great extent making realization of a FD transceiver possible. For FD deployment in a multi-cell environment, minimizing SI is not sufficient due to the presence of a complex array of network-wide interference. This paper investigates the critical deployment challenges for a dense urban FD multi- cell network and provides solutions for successful deployment of FD cellular networks. Extensive simulation results show that the proposed user selection and power control algorithms are able to increase the FD performance gain by more than 80% when compared to the traditional half duplex (HD) counterpart.

In this paper, we tackle the problem of multi-cell resource scheduling, where the objective is to maximize the weighted sum-rate through inter-cell interference coordination (ICIC). The blanking method is used to mitigate the inter-cell interference, where a resource is either used with a predetermined transmit power or not used at all, i.e., blanked. This problem is known to be strongly NP-hard, which means that it is not only hard to solve in polynomial time, but it is also hard to find an approximation algorithm with guaranteed optimality gap. In this work, we identify special scenarios where a polynomial-time algorithm can be constructed to solve this problem with theoretical guarantees. In particular, we define a dominant interference environment, in which for each user the received power from each interferer is significantly greater than the aggregate received power from all other weaker interferers. We show that the originally strongly NP-hard problem can be tightly relaxed to a linear programming problem in a dominant interference environment. Consequently, we propose a polynomial time distributed algorithm that is not only guaranteed to be tight in a dominant interference environment, but which also computes an upper bound on the optimality gap without additional computational complexity. The proposed scheme is based on the primal-decomposition method, where the problem is divided into a master-problem and multiple subproblems. We solve the master-problem iteratively using the projected-subgradient method. We also show that each subproblem has a special network flow structure. By exploiting this network structure, each subproblem is solved using the network-based optimization methods, which significantly reduces the complexity in comparison to the general-purpose convex or linear optimization methods. In comparison with baseline schemes, simulation results of the International Mobile Telecommunications-Advanced (IMT-Advanced) scenarios show that the proposed scheme achieves higher gains in aggregate throughput, cell-edge throughput, and outage probability.

In heterogeneous networks, interference management is very important since the interference due to spectrum-sharing can significantly decrease the overall performance. In the existing work, various resource allocation methods are proposed to either improve energy efficiency or alleviate interference. Very little research has addressed their joint interaction. Furthermore, most existing work assumes that all of the channel state information is known, which might not be realistic. In this paper, we propose a game-theoretical scheme using energy-efficient resource allocation and interference pricing for an interference-limited environment in two-tier heterogeneous networks without complete channel state information. We formulate the problems of resource allocation and interference management as a two-stage Stackelberg game with incomplete information. A backward induction method is used to analyze the proposed game, since it can capture the sequential dependencies of the decisions in the stages of the game.

Coordinated multi-point (CoMP) is a promising technology in next generation cellular networks to enhance the cell-edge performance under universal frequency reuse. In uplink CoMP, the transmitted signals from user equipments are cooperatively processed by multiple base stations assuming there is perfect channel state information (CSI) shared by those base stations. However, practical systems can only obtain imperfect CSI which suffers from quantization errors and especially delay introduced by channel estimation and signalling over backhaul networks. In this paper, we consider the optimal clustering and rate allocation problem in CoMP systems where the CSI feedback via backhaul is delayed. We model such system in the framework of networked Markov decision process (networked-MDP), which is equivalent to a partial observable Markov decision process (POMDP). We find out an optimal policy for such POMDP, which is computational inexpensive. Extensive simulations show promising performance gain of the proposed scheme over existing scheme which makes the decisions merely based on the current observation.

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.

Resource scheduling in OFDMA cellular wireless networks is a powerful technique on the MAC layer. Utilizing adaptive modulation and coding allows the effective use of all signal-to-interference ratio (SINR) ranges. Typical single antenna spectral efficiency values for LTE-Advanced range between 4.8 near the base station and 0.2 b/s/Hz at the cell edge. With best-effort traffic and full buffer assumption the tradeoff between emphasizing the cell center or cell edge can be explored extensively. However, with real-time traffic present, this takes priority without fairness adjustment alternatives. In this paper the mixed traffic scenario is studied with an abstract stochastic Petri net model. The exploration of the degrees of freedom by studying the real-time traffic proportion and a fairness adjustment parameter provides new insight to the potential feasible region. The results show that the tradeoff between emphasizing the cell edge performance and maintaining a high average spectral efficiency is most powerful in the best-effort case, while an increasing level of real-time traffic reduces the room for a tradeoff. The stochastic Petri net analysis approach allows numeric analysis without simulation by utilizing Markov chain equivalence and steady state calculations. This model is deliberately abstract but flexible enough to study the tradeoff.

Cellular wireless networks based on OFDMA utilize adaptive modulation and coding to operate effectively in regions of high and low SINR. Therefore the local single antenna spectral efficiency ranges between 5 in the cell center and 0.2 b/s/Hz at the cell edge. The scheduling goal of high average spectral efficiency contradicts the goal of a good rate fairness among all terminals. Also there is a tradeoff between increasing the cell edge performance and maintaining a high average spectral efficiency. In this paper a stochastic Petri net analysis approach is taken and a numeric analysis is performed based on Markov chain equivalence and steady state calculations. The proposed models are deliberately abstract but offer commonly used tuning parameters in order to study the tradeoff without too many degrees of freedom.

In this paper, we propose a novel distributed inter-cell interference coordination (ICIC) scheme. The proposed scheme, which runs in polynomial time, finds a near-optimum dynamic resource partitioning that maximizes a proportional-fairness criterion in the entire network. The proposed scheme is based on primal-decomposition method, where the problem is divided into a master and multiple sub-problems. The master-problem is solved using projected-subgradient method while each of the sub-problems is solved using minimum-cost network flow optimization. Through extensive simulations of four IMT-advanced scenarios, we quantify the gains achieved using the proposed scheme. We demonstrate that the proposed scheme achieves both high cell-edge throughput, that is comparable to frequency reuse 3, and high aggregate throughput, that is at least as good as the aggregate throughput achieved by frequency reuse 1.

Abstract Future wireless evolution envisages high rates, low hierarchy in the network architecture, antenna array processing, multiple access modes and multihop operation as part of the system concept. To exploit the increased capabilities of the systems in conception, efficient resource management strategies need to be developed. The goal of this paper is to examine the key aspects of the evolution which impact radio resource management for the mobile broadband wireless network, and to emphasize the areas that need to be addressed for servicing mobile users with varying quality of service requirements. Copyright © 2003 John Wiley & Sons, Ltd.

The drop-out probability and the unnecessary handover performance of several handover schemes have been investigated using a simulation model which incorporates detailed representation of mobility, traffic and propagation environments. Specifically the overall performance of variable hysteresis level handover schemes are investigated. The performance under various mobility conditions are presented for both Manhattan city environment and hexagonal cell layout. Also the performance dependency on more critical input parameters such as mobile speed and correlation of sample measurements, is presented. It is shown that an environment dependent, variable hysteresis function can be devised by closely studying the system handover behaviour to give the optimum overall performance.>