アダプティブアレーアンテナを備えたセルラーシステムのための正規化ドップラーシフトを規範とした適応変調およびユーザ制御

電気通信大学200

マクロセルにオーバーレイするスモールセルのための層間干渉低減に関する研究

電気通信大学201

マクロセルにオーバーレイするスモールセルのための層間干渉低減に関する研究

The huge number of mobile terminals in use and the radio frequency scarceness are the relevant issues for future wireless communications. Frequency sharing has been considered to solve the problem. Addressing the issues has led to a wide adoption of small cell networks particularly femtocells overlaid onto macrocell or small cells implemented with the support of distributed antenna systems (DASs). Small cell networks improve link quality and frequency reuse. Spectrum sharing improves the usage efficiency of the licensed spectrum. A macrocell underlaid with femtocells constitutes a typical two-tier network for improving spectral efficiency and indoor coverage in a spectrum sharing environment. Considering the end-user access control over the small cell base station (SBS), with shared usage of the macrocell’s spectrum, this dissertation contribution is an investigation of mitigation techniques of crosstier interference. Such cross-tier interference mitigation leads to possible implementation of multi-tier and heterogeneous networks. The above arguments underpin our work which is presented in the hereby dissertation. The contributions in this thesis are three-fold. Our first contribution is an interference cancellation scheme based on the transmitter symbols fed back to the femtocell base station (FBS) undergoing harmful cross-tier interference. We propose a cross-tier interference management between the FBS and the macrocell base station (MBS) in uplink communications. Our proposal uses the network infrastructure for interference cancellation at the FBS. Besides, we profit from terminal discovery to derive the interference level from the femtocell to the macrocell. Thus, additionally, we propose an interference avoidance method based on power control without cooperation from the MBS. In our second contribution, we dismiss the use of the MBS for symbol feedback due to delay issues. In a multi-tier cellular communication system, the interference from one tier to another, denoted as cross-tier interference, is a limiting factor for the system performance. In spectrum-sharing usage, we consider the uplink cross-tier interference management of heterogeneous networks using femtocells overlaid onto the macrocell. We propose a variation of the cellular architecture and introduce a novel femtocell clustering based on interference cancellation to enhance the sum rate capacity. Our proposal is to use a DAS as an interface to mitigate the cross-tier interference between the macrocell and femtocell tiers. In addition, the DAS can forward the recovered data to the macrocell base station (MBS); thus, the macrocell user can reduce its transmit power to reach a remote antenna unit (RAU) located closer than the MBS. By distributing the RAUs within the macrocell coverage, the proposed scheme can mitigate the cross-tier interference at different locations for several femtocell clusters. Finally, we address the issue of cross-tier interference mitigation in heterogeneous cognitive small cell networks comparing equal and unequal signal-to-noise ratio (SNR) branches in multi-input multi-output (MIMO) Alamouti scheme. Small cell networks enhance spectrum efficiency by handling the indoor traffic of mobile networks on a frequency-reuse operation. Because most of the current mobile traffic happens indoor, we introduce a prioritization shift by imposing a threshold on the outage generated by the outdoor mobile system to the indoor small cells. New closed-form expressions are derived to validate the proposed bit error rate (BER) function used in our optimization algorithm. We propose a joint transmit antenna selection and power allocation which minimizes the proposed BER function of the outdoor mobile terminal. The optimization is constrained by the outage at the small cell located near the cooperating transmit relays. Such constraint improves the initialization of the iterative algorithm compared to randomly choosing initial points. The proposed optimization yields a dynamic selection of the relays with power control pertaining to the outdoor mobile terminal performance.電気通信大学201

Distributionally Robust Optimization

This chapter presents a class of distributionally robust optimization problems in which a decision-maker has to choose an action in an uncertain environment. The decision-maker has a continuous action space and aims to learn her optimal strategy. The true distribution of the uncertainty is unknown to the decision-maker. This chapter provides alternative ways to select a distribution based on empirical observations of the decision-maker. This leads to a distributionally robust optimization problem. Simple algorithms, whose dynamics are inspired from the gradient flows, are proposed to find local optima. The method is extended to a class of optimization problems with orthogonal constraints and coupled constraints over the simplex set and polytopes. The designed dynamics do not use the projection operator and are able to satisfy both upper- and lower-bound constraints. The convergence rate of the algorithm to generalized evolutionarily stable strategy is derived using a mean regret estimate. Illustrative examples are provided

Adaptive Modulation and User Control for Cellular System with Adaptive Array Antenna based on Normalized Doppler Shift

200

Traffic Load Prediction and Power Consumption Reduction for Multi-band Networks

International audienceEnergy is a major expense issue for mobile operators. In the case of wireless networks, base stations have been identified as the main source of energy consumption. In this paper, we study the energy consumption reduction problem based on real measurements for a commercial multi-band LTE network. Specifically, we are interested in sleep modes to turn off certain frequency bands during low traffic periods and consequently reduce power consumption. We determine the number of frequency bands really needed at each time period. The frequency bands that are not needed can be disabled to reduce energy consumption. In order to allow the operator to predict how many bands can be switched off without major impact on the quality of service, we propose to use a deep learning algorithm, such as Long-Short Term Memory (LSTM). Based on the captured data traces, we have shown that the proposed LSTM model can save an average of 8% to 21% of the energy consumption during working days

Traffic Load Prediction and Power Consumption Reduction for Multi-band Networks

International audienceEnergy is a major expense issue for mobile operators. In the case of wireless networks, base stations have been identified as the main source of energy consumption. In this paper, we study the energy consumption reduction problem based on real measurements for a commercial multi-band LTE network. Specifically, we are interested in sleep modes to turn off certain frequency bands during low traffic periods and consequently reduce power consumption. We determine the number of frequency bands really needed at each time period. The frequency bands that are not needed can be disabled to reduce energy consumption. In order to allow the operator to predict how many bands can be switched off without major impact on the quality of service, we propose to use a deep learning algorithm, such as Long-Short Term Memory (LSTM). Based on the captured data traces, we have shown that the proposed LSTM model can save an average of 8% to 21% of the energy consumption during working days