Ying Loong Lee

Recent advances in radio resource management for heterogeneous LTE/LTE-A networks

As heterogeneous networks (HetNets) emerge as one of the most promising developments toward realizing the target specifications of Long Term Evolution (LTE) and LTE-Advanced (LTE-A) networks, radio resource management (RRM) research for such networks has, in recent times, been intensively pursued. Clearly, recent research mainly concentrates on the aspect of interference mitigation. Other RRM aspects, such as radio resource utilization, fairness, complexity, and QoS, have not been given much attention. In this paper, we aim to provide an overview of the key challenges arising from HetNets and highlight their importance. Subsequently, we present a comprehensive survey of the RRM schemes that have been studied in recent years for LTE/LTE-A HetNets, with a particular focus on those for femtocells and relay nodes. Furthermore, we classify these RRM schemes according to their underlying approaches. In addition, these RRM schemes are qualitatively analyzed and compared to each other. We also identify a number of potential research directions for future RRM development. Finally, we discuss the lack of current RRM research and the importance of multi-objective RRM studies.

Improved Detection Performance of Cognitive Radio Networks in AWGN and Rayleigh Fading Environments

Cognitive radios (CRs) have been recently emerging as prime candidates to enhance spectral efficiency by exploitingspectrum-aware systems which can reliably monitor licensed users’ activities. CR users monitor such activities byperforming spectrum sensing to detect potential white spaces. However, this process of local sensing might be achallenging task in fading environments. The inefficiency of spectrum sensing might cause interference to licensees ifthey are miss-detected by CR users. Thus, cooperative spectrum sensing is proposed as a means to combat fading andimprove the detection performance. However, the detection performance does not improve by such cooperation whenlow-SNR environment is considered. In this paper, cooperative spectrum sensing with PSO-based threshold adaptationis presented to address the aforementioned problem. Simulation results show that the detection performance with PSObasedadaptive detection threshold is improved, particularly, in low-SNR environment.

Dynamic Resource Management for LTE-Based Hybrid Access Femtocell Systems

Hybrid access femtocells for long term evolution (LTE)-based cellular networks provide a tradeoff between closed and open access femtocells whereby all subscribers are granted access albeit with priority given to closed access subscribers. Due to the need to accommodate both closed and open access subscribers, quality of service (QoS) provisioning for LTE-based hybrid access femtocells has become more challenging. This paper addresses this issue and proposes a new dynamic resource management scheme for such hybrid architectures. In particular, the proposed scheme first classifies and performs lexicographic admission control on the incoming traffic data flows using an optimal greedy algorithm. A suboptimal delay-bounded packet scheduling algorithm and a dual decomposition-based power allocation algorithm are developed to solve the non-convex maximization problem such that the weighted sum rate of each femtocell is maximized, subject to bounded packet delays and power constraints. Simulation results show that the proposed scheme can significantly outperform existing schemes in terms of QoS, throughput and fairness.

Gravity-based particle swarm optimization with hybrid cooperative swarm approach for global optimization

Premature convergence has been recognized as one of the major drawbacks of particle swarm optimization PSO algorithms. In particular, the lack of diversity in PSO performance is an essential cause that commonly results in high susceptibility to prematurely converge to local optima especially in complex multimodal problems with high dimensionality. This paper presents a new PSO operational strategy based on gravity concept to address the aforementioned drawback and it is named as gravity-based particle swarm optimizer GPSO. In addition, GPSO is further modified by adopting the cooperation concept of the conventional cooperative particle swarm optimizer CPSO to develop an extended version of GPSO called cooperative gravity-based particle swarm optimizer CGPSO. Simulation results manifest that CGPSO performs satisfactorily on unimodal functions while it generally performs better on multimodal functions than GPSO and other conventional PSO variants. Finally, the proposed GPSO and CGPSO are applied into the problem of optimizing the detection performance of soft decision fusion for cooperative spectrum sensing in cognitive radio networks. For this problem, computer simulations show that the proposed CGPSO outperforms all other PSO variants in terms of quality of solutions whereas GPSO is found to be the best when the computational cost is taken into account.

Dynamic Network Slicing for Multitenant Heterogeneous Cloud Radio Access Networks

Multitenant cellular network slicing has been gaining huge interest recently. However, it is not well-explored under the heterogeneous cloud radio access network (H-CRAN) architecture. This paper proposes a dynamic network slicing scheme for multitenant H-CRANs, which takes into account tenants’ priority, baseband resources, fronthaul and backhaul capacities, quality of service (QoS), and interference. The framework of the network slicing scheme consists of an upper-level, which manages admission control, user association, and baseband resource allocation; and a lower-level, which performs radio resource allocation among users. Simulation results show that the proposed scheme can achieve a higher network throughput, fairness, and QoS performance compared with several baseline schemes.

Joint Resource Allocation and User Association for Heterogeneous Cloud Radio Access Networks

Cloud radio access networks (C-RANs) have been regarded as a promising architecture for energy-efficient fifth generation systems. In this paper, a new joint remote radio head (RRH) activation, user-RRH pairing and resource allocation strategy is proposed for heterogeneous C-RANs (H-CRANs). We first formulate an optimization problem to maximize the energy efficiency of H-CRANs. Then, a low-complexity suboptimal solution is developed. Our proposed mechanism consists of three key procedures: 1) RRH activation is performed based on greedy RRH selection, 2) user-RRH pairing is performed based on the channel quality, 3) the resource allocation problem is solved by dual decomposition. Simulation results show that the proposed strategy can improve energy efficiency significantly.

Fair Resource Allocation With Interference Mitigation and Resource Reuse for LTE/LTE-A Femtocell Networks

Joint consideration of interference, resource utilization, fairness, and complexity issues is generally lacking in existing resource allocation schemes for Long-Term Evolution (LTE)/LTE-Advanced femtocell networks. To tackle this, we employ a hybrid spectrum allocation approach whereby the spectrum is split between the macrocell and its nearby interfering femtocells based on their resource demands, whereas the distant femtocells share the entire spectrum. A multiobjective problem is formulated for resource allocation between femtocells and is decomposed using a lexicographic optimization approach into two subproblems. A greedy algorithm of reasonably low complexity is proposed to solve these subproblems sequentially. Simulation results show that the proposed scheme achieves substantial throughput and packet loss improvements in low-density femtocell deployment scenarios while performing satisfactorily in high-density femtocell deployment scenarios with substantial complexity and overhead reduction. The proposed scheme also performs nearly as well as the optimal solution obtained by exhaustive search.

A new network slicing framework for multi-tenant heterogeneous cloud radio access networks

Research on network slicing for multi-tenant heterogeneous cloud radio access networks (H-CRANs) is still in its infancy. In this paper, we redefine network slicing and propose a new network slicing framework for multi-tenant H-CRANs. In particular, the network slicing process is formulated as a weighted throughput maximization problem that involves sharing of computational resources, fronthaul capacity, physical remote radio heads and radio resources. The problem is then jointly solved using a sub-optimal greedy approach and a dual decomposition method. Simulation results demonstrate that the framework can flexibly scale the throughput performance of multiple tenants according to the user priority weights associated with the tenants.

Multi-objective Resource Allocation for LTE/LTE-A Femtocell/HeNB Networks Using Ant Colony Optimization

Existing femtocell resource allocation schemes for Long Term Evolution or LTE-Advanced femtocell networks do not jointly achieve efficient resource utilization, fairness guarantee, interference mitigation and reduced complexity in a satisfactory manner. In this paper, a multi-objective resource allocation scheme is proposed to achieve these desired features simultaneously. We first formulate three objective functions to respectively maximize resource utilization efficiency, guarantee a high degree of fairness and minimize interference. A weighted sum approach is then used to combine these objective functions to form a single multi-objective optimization problem. An ant colony optimization algorithm is employed to find the Pareto-optimal solution to this problem. Simulation results demonstrate that the proposed scheme performs jointly well in all aspects, namely resource utilization, fairness and interference mitigation. Additionally, it maintains satisfactory performance in the handover process and has a reasonably low complexity compared to the existing schemes.

Performance Investigation on Binary Particle Swarm Optimization for Global Optimization

Binary particle swarm optimization (BinPSO) is introduced as a population-based random search algorithm for discrete binary optimization problems. A number of BinPSO variants have been introduced in the literature and showed performance improvements over the original BinPSO algorithm. However, no detailed performance comparison between these BinPSO variants has been found in the current literature. In this paper, a more thorough performance comparison study on the BinPSO variants in terms of convergence speed, solution quality and performance stability is presented. The BinPSO variants are further compared with a newly adopted cooperative BinPSO variant. The performance evaluation is conducted using De Jong’s test functions, several complex multimodal functions, and a real-world engineering problem, namely optimization of the detection performance of cooperative spectrum sensing in cognitive radio networks. Results show that most of the BinPSO variants exhibit excellent performance on solving De Jong’s test functions while the cooperative BinPSO variant performs better on the complex multimodal problems and the real-world engineering problem. Overall, the cooperative BinPSO variant shows the most promising performance, especially in terms of solution quality and performance stability.