Xue Han

Distributed coverage optimization for small cell clusters using game theory

Small cell cluster is a new paradigm to extend the usage of small cells from residential environment to large indoor or outdoor areas. However, coverage optimization is a challenge due to the ad-hoc deployment and plug-and-play feature of small cells. This paper considers decentralized self-optimization network (SON) architecture of small cell cluster and proposes distributed coverage optimization algorithm using game theory (DGT). A non-cooperate game is modeled to tune the Tx power of each small cell with a net utility function considering both gain of throughput and punishment of interference. Nash Equilibrium (NE) is proved to be existed in the game and a power update scheme is proposed which converges to the NE. Simulation results show that DGT can significantly improve throughput as well as coverage ratio with only several iterations. Compared with centralized algorithm such as modified particle swarm optimization (MPSO) and simulated annealing (SA), DGT algorithm reaches higher network throughput, uses less iteration and keeps considerable coverage ratio.

Maximum entropy based IP-traffic classification in mobile communication networks

In order to maintain sufficient mobile communication network capacity for value added services, 3GPP has recently introduced an architecture called ”SIPTO”(Selected IP Traffic Offload) to offload selected mobile IP traffic from the core network. This brings new challenge to on line traffic classification scheme needed to figure out the traffic which should be offloaded in real time. Although traffic classification methodologies have already been investigated in wired IP network, they are not applicable in mobile environment where high bit error rates (BER) and temporary disconnections are observed due to hostile wireless channel conditions. This paper proposes a maximum entropy based IP-traffic classification scheme (METCS) to address on line traffic classification problems in mobile communication networks. METCS extracts the application layer payload pattern using randomly arrived packets instead of the first few sequential packets of an application flow. Simulation results show that METCS outperforms existing methods by offering average 5%-8% improvement in classification accuracy with about 60% time.

Advanced coverage optimization techniques for small cell clusters

Coverage optimization is a main challenge for small cell clusters which are considered to be a promising solution to provide seamless cellular coverage for large indoor or outdoor areas. This paper focuses on small cell cluster coverage problems and proposes both centralized and distributed self-optimization methods. Modified Particle swarm optimization (MPSO) is introduced to centralized optimization which employs particle swarm optimization (PSO) and introduces a heuristic power control scheme to accelerate the algorithm to search for the global optimum solution. Distributed coverage optimization is modeled as a non-cooperative game, with a utility function considering both throughput and interference. An iterative power control algorithm is then proposed using game theory (DGT) which converges to Nash Equilibrium (NE). Simulation results show that both MPSO and DGT have excellent performance in coverage optimization and outperform optimization using simulated annealing algorithm (SA), reaching higher coverage ratio and throughput while with less iterations.

Investigation on energy efficiency of OFDM-based two-stage cooperative multicast with CP combining

This paper focuses on the energy efficiency of two-stage cooperative multicast with CPC (CP combining). Aiming to provide a coverage of 95%, cooperative multicast with SC (selective combining) has been analytically investigated and a power saving of 43% can be achieved compared with conventional one-stage multicast [9]. However, CP combining which is more practical can provide a stronger received signal than SC and thus is expected to be able to provide further energy saving. Assuming high user density, analysis is carried out on the coverage performance of OFDM based two-stage cooperative multicast with CPC, which is decided by the BS (base station) transmission power at the 1st stage and the number and locations of MRs (mobile relays) at the 2nd stage. Given a fixed coverage, it is difficult to obtain the BS transmission power and MR scheme that provide the optimal total power consumption. Therefore, two sub-optimal schemes are proposed, i.e., BS coordination and MR coordination, which are based on the schemes for cooperative multicast with SC [9] and reduce the BS transmission power and the number of MRs, respectively. Numerical results show that with BS and MR coordination, cooperative multicast with CPC can save 9% and 17% compared to that with SC, respectively. Moreover, MR coordination provides more uniform coverage than BS coordination. Simulations are carried out to verify the numerical results. It is shown that MR coordination provides better coverage than BS coordination for all investigated user densities and the performance gap increases as the density decrease.

FFT Traffic Classification-Based Dynamic Selected IP Traffic Offload Mechanism for LTE HeNB Networks

Traffic offloading is a promising technique to alleviate the traffic load in LTE core networks. Based on 3GPP “SIPTO” (Selected IP Traffic Offload) architecture, this paper proposes dynamic SIPTO mechanism (D-SIPTO) for traffic offloading in LTE HeNB networks, which combines fast fourier transform (FFT) based IP traffic classification scheme (FFTTCS) with the dynamic traffic offload path selection algorithm (DTOPSA). Simulation results show that FFTTCS can realize on-line traffic classification with similar precisions but only using less than 10 % of the time needed by existing methods. Combined with DTOPSA, the proposed D-SIPTO can reduce the core network traffic by 60 % while selecting the optimal offload path according to the type of traffic to be offloaded.

Heterogeneous Wireless Network Traffic Load Estimation Based on Chaos Theory

With the rapid development of high-speed wireless communication technology, more novel and different wireless networks are emerging. The co-existence of multiple wireless networks has made the wireless network environment complex and heterogeneous. Traffic flow characteristics study and effective traffic prediction algorithm selection are very important for optimal allocation of network resources, network protocol design and improvement of service quality in this heterogeneous wireless network. This paper uses non-statistical method which is based on chaos theory to make analysis of heterogeneous wireless network traffic flow characteristics. On that basis, a traffic model which uses phase space reconstruction algorithm on multivariate traffic time series is proposed and used for traffic prediction. Simulation results show that the proposed traffic prediction model can achieve better prediction performance.

Game-Theoretic Power Control for Interference Mitigation in Two-Tier Small Cell Networks

Interference mitigation is a major challenge in deploying a two-tier small cell network, where small cells are deployed underlaying a central macrocell and share the same spectrum with the macrocell. In this paper, we develop a new decentralized power control solution for interference mitigation in a two-tier small cell network, from a game theoretic perspective. We aim to maximize the number of small cell user (SU) transmissions that can be admitted in the network while satisfying the signal-to- interference-noise ratio (SINR) constraints of both transmitting SUs and the macrocell user (MU). We formulate the problem of power control for SUs as a game with common utility. The Nash equilibria of the game are investigated. We then propose a learning automata based distributed discrete power control algorithm with which the SUs can learn from their action-reward histories and adjust their transmit powers towards a NE point. Simulation results show the proposed algorithm achieves higher number of SU transmissions that can be admitted compared with existing schemes in the literature.

Fast fourier transform based ip traffic classification system for SIPTO at H(e)NB

3GPP has recently introduced LIPA(Local IP Access) and SIPTO(Selected IP Traffic Offload) to offload traffic from the core network, which brings new challenge to on-line traffic classification, because of the large amount of data and the difference of mobile network from wired network, such as high bit error rates(BER) and temporary disconnections. Therefore, other proposed schemes which aim at ether LIPA at H(e)NB or SIPTO at macro network could not get high accuracy and high speed at the same time, and traffic classification methodologies in wired IP network are not applicable. This paper proposes a fast fourier transform(FFT) based IP traffic classification system for SIPTO at H(e)NB, which focuses on classifying each packet at H(e)NB by extracting the application layer payload pattern using FFT. Pattern extraction and classification using machine learning algorithms are simulated, and results show that our system outperforms existing methods by offering about 3%-6% improvement in classification accuracy with about 7% time. Simulation of SIPTO shows good reduction of press to the core network and low false rates.

Optimization of subcarrier allocation in highly dynamic cellular relay networks

Cellular relay networks prove to be a cost-effective approach that offers significant performance benefits in coverage extension, cell-edge throughput enhancement and increased spectral efficiency. Radio resource management for cellular relay networks, where the network topology is highly dynamic, is particularly challenging. The highly dynamic topology may be caused by an ever increasing number of users accessing Internet and other multimedia services using mobile devices carried by pedestrians, in a train or in vehicles. This paper studies the subcarrier allocation problem in highly dynamic cellular relay networks with an objective to maximize the overall throughput. This optimization problem is formulated and solved as an expectation maximization problem. Statistical multiplexing gain is explicitly explored to further improve the channel utilization and spectral efficiency. Numerical evaluations are performed which demonstrate that the proposed scheme offers higher system throughput and improved radio resource utilization compared with existing schemes.