Game Theory-based Channel Selection for LTE-U

The Project intends to analyse the performance of a game theory-based channel selection in LTE-U.The main topic of this thesis project is the study of a channel selection strategy for LTE-U based on the game theory. The method consists on a repeated game where each small cell is a player with the purpose of finding the best channel where to set up the LTE-U carrier and it uses the ITEL-BA algorithm in order to make the system to converge to a Nash Equilibrium state. The aim is to evaluate the performance of the system in terms of achieved throughput and convergence time depending on the variation of some parameters, which are the exploration rate, the achieved throughput and the non-stationarity condition. The work environment consists of a software that simulate the scenario where several small cells apply this strategy

An experimental assessment of channel selection in cognitive radio networks

The management of future networks is expected to fully exploit cognitive capabilities that embrace knowledge and intelligence, increasing the degree of automation, making the network more self-autonomous and enabling a personalized user experience. In this context, this paper presents the use of knowledge-based capabilities through a specific lab experiment focused on the Channel Selection functionality for Cognitive Radio Networks (CRN). The selection is based on a supervised classification that allows estimating the number of interfering sources existing in a given frequency channel. Four different classifiers are considered, namely decision tree, neural net-work, naive Bayes and Support Vector Machine (SVM). Additionally, a comparison against other channel selection strategies using Q-learning and game theory has also been performed. Results obtained in an illustrative and realistic test scenario have revealed that all the strategies allow identifying an optimum solution. However, the time to converge to this solution can be up to 27 times higher according to the algorithm selected.Peer ReviewedPostprint (author's final draft

Spectrum Sharing, Latency, and Security in 5G Networks with Application to IoT and Smart Grid

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches

Measurement and Optimization of LTE Performance

4G Long Term Evolution (LTE) mobile system is the fourth generation communication system adopted worldwide to provide high-speed data connections and high-quality voice calls. Given the recent deployment by mobile service providers, unlike GSM and UMTS, LTE can be still considered to be in its early stages and therefore many topics still raise great interest among the international scientific research community: network performance assessment, network optimization, selective scheduling, interference management and coexistence with other communication systems in the unlicensed band, methods to evaluate human exposure to electromagnetic radiation are, as a matter of fact, still open issues. In this work techniques adopted to increase LTE radio performances are investigated. One of the most wide-spread solutions proposed by the standard is to implement MIMO techniques and within a few years, to overcome the scarcity of spectrum, LTE network operators will offload data traffic by accessing the unlicensed 5 GHz frequency. Our Research deals with an evaluation of 3GPP standard in a real test best scenario to evaluate network behavior and performance

Distributed Cognitive RAT Selection in 5G Heterogeneous Networks: A Machine Learning Approach

The leading role of the HetNet (Heterogeneous Networks) strategy as the key Radio Access Network (RAN) architecture for future 5G networks poses serious challenges to the current cell selection mechanisms used in cellular networks. The max-SINR algorithm, although effective historically for performing the most essential networking function of wireless networks, is inefficient at best and obsolete at worst in 5G HetNets. The foreseen embarrassment of riches and diversified propagation characteristics of network attachment points spanning multiple Radio Access Technologies (RAT) requires novel and creative context-aware system designs. The association and routing decisions, in the context of single-RAT or multi-RAT connections, need to be optimized to efficiently exploit the benefits of the architecture. However, the high computational complexity required for multi-parametric optimization of utility functions, the difficulty of modeling and solving Markov Decision Processes, the lack of guarantees of stability of Game Theory algorithms, and the rigidness of simpler methods like Cell Range Expansion and operator policies managed by the Access Network Discovery and Selection Function (ANDSF), makes neither of these state-of-the-art approaches a favorite. This Thesis proposes a framework that relies on Machine Learning techniques at the terminal device-level for Cognitive RAT Selection. The use of cognition allows the terminal device to learn both a multi-parametric state model and effective decision policies, based on the experience of the device itself. This implies that a terminal, after observing its environment during a learning period, may formulate a system characterization and optimize its own association decisions without any external intervention. In our proposal, this is achieved through clustering of appropriately defined feature vectors for building a system state model, supervised classification to obtain the current system state, and reinforcement learning for learning good policies. This Thesis describes the above framework in detail and recommends adaptations based on the experimentation with the X-means, k-Nearest Neighbors, and Q-learning algorithms, the building blocks of the solution. The network performance of the proposed framework is evaluated in a multi-agent environment implemented in MATLAB where it is compared with alternative RAT selection mechanisms

A Survey of Resource Allocation Techniques for Cellular Network’s Operation in the Unlicensed Band

With an ever increasing demand for data, better and efficient spectrum operation has become crucial in cellular networks. In this paper, we present a detailed survey of various resource allocation schemes that have been considered for the cellular network’s operation in the unlicensed spectrum. The key channel access mechanisms for cellular network’s operation in the unlicensed bands are discussed. The various channel selection techniques are explored and their operation explained. The prime issue of fairness between cellular and Wi-Fi networks is discussed, along with suitable resource allocation techniques that help in achieving this fairness. We analyze the coverage, capacity, and impact of coordination in LTE-U systems. Furthermore, we study and discuss the impact and discussed the impact of various traffic type, environments, latency, handover, and scenarios on LTE-U’s performance. The new upcoming 5G New Radio and MulteFire is briefly described along with some of the critical aspects of LTE-U which require further research. © 2020 by the authors. Licensee MDPI, Basel, Switzerland

A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

셀룰러 사이드링크 성능 향상을 위한 상위계층 기법

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2020. 8. 박세웅.In typical cellular communications, User Equipments (UEs) have always had to go through a Base Station (BS) to communicate with each other, e.g., a UE transmits a packet to a BS via uplink and then the BS transmits the packet to another UE via downlink. Although the communication method can serve UEs efficiently, the communication method can cause latency problems and overload problems in BS. Thus, sidelink has been proposed to overcome these problems in 3GPP release 12. Through sidelink, UEs can communicate directly with each other. There are two representative communications using sidelink, i.e., Device-to-Device (D2D) communication and Vehicle-to-Vehicle (V2V) communication. In this dissertation, we consider three strategies to enhance the performances of D2D and V2V communications: (i) efficient feedback mechanism for D2D communications, (ii) context-aware congestion control scheme for V2V communication, and (iii) In-Device Coexistence (IDC)-aware LTE and NR sidelink resource allocation scheme. Firstly, in the related standard, there is no feedback mechanism for D2D communication because D2D communications only support broadcast-type communications. A feedback mechanism is presented for D2D communications. Through our proposed mechanism, UEs can use the feedback mechanism without the help of BS and UEs do not need additional signals to allocate feedback resources. We also propose a rate adaptation algorithm, which consider in-band emission problem, on top of the proposed feedback mechanism. We find that our rate adaptation achieves higher and stable throughput compared with the legacy scheme that complies to the standard. Secondly, we propose a context-aware congestion control scheme for LTE-V2V communication. Through LTE-V2V communication, UEs transmit Cooperative Awareness Message (CAM), which is a periodic message, and Decentralized Environmental Notification Message (DENM), which is a event-driven message and allows one-hop relay. The above two messages have different characteristics and generation rule. Thus, it is difficult and inefficient to apply the same congestion control scheme to two messages. We propose a congestion control schemes for each message. Through the proposed congestion control schemes, UEs decide whether to transmit according to their situation. Through simulation results, we show that our proposed schemes outperform comparison schemes as well as the legacy scheme. Finally, we propose a NR sidelink resource allocation scheme based on multi-agent reinforcement learning, which awares a IDC problem between LTE and NR in Intelligent Transport System (ITS) band. First, we model a realistic IDC interference based on spectrum emission mask specified at the standard. Then, we formulate the resource allocation as a multi-agent reinforcement learning with fingerprint method. Each UE achieves its local observation and rewards, and learns its policy to increase its rewards through updating Q-network. Through simulation results, we observe that the proposed resource allocation scheme further improves Packet Delivery Ratio (PDR) performances compared to the legacy scheme.전형적인 셀룰러 통신에서는, 단말들은 서로 통신하기 위해 항상 기지국을 거쳐야 한다. 예를 들면, 단말이 uplink를 통해 기지국에게 패킷을 전송한 다음 기지국은 downlink를 통해 해당 패킷을 전송해준다. 이러한 통신방식은 단말들에게 효율적으로 서비스를 제공할 수 있지만, 상황에 따라서는 지연문제와 기지국의 과부하 문제를 야기할 수 있다. 따라서 3GPP release12에서 이러한 문제점들을 극복하기 위해 sidelink가 제안되었다. 덕분에 단말들은 sidelink를 통해서 서로 직접 통신을 할 수 있게 되었다. Sidelink를 사용하는 두 가지 대표적인 통신은 D2D(Device-to-Device) 통신과 V2V(Vehicle-to-Vehicle) 통신이다. 본 논문에서는 D2D 와 V2V 통신 성능을 향상시키기 위한 세가지 전략을 고려한다. (i) D2D 통신을 위한 효율적인 피드백 메커니즘, (ii) V2V 통신을 위한 상황인식기반 혼잡제어 기법, 그리고 (iii) IDC(In-Device Coexistence) 인지 기반 sidelink 자원 할당 방식. 첫째, 관련 표준에는 D2D 통신이 브로드캐스트 유형의 통신만을 지원하기 때문에 D2D 통신에 대한 피드백 메커니즘이 없다. 우리는 이러한 한계점을 극복하고자 D2D 통신을 위한 피드백 메커니즘을 제안한다. 제안된 메커니즘을 통해, 단말은 기지국의 도움없이 피드백 메커니즘을 사용할 수 있으며 피드백 자원을 할당하기 위한 추가 신호를 필요로 하지 않는다. 우리는 또한 제안된 피드백 메커니즘위에서 동작할 수 있는 data rate 조절 기법을 제안하였다. 우리는 시뮬레이션 결과를 통하여, 제안한 data rate 조절 기법이 기존 방식보다 더 높고 안정적인 수율을 제공하는 것을 확인하였다. 둘째, LTE-V2V 통신을 위한 상황 인지 기반 혼잡 제어 기법을 제안한다. LTE-V2V 통신에서 단말들은 주기적인 메시지인 CAM(Cooperative Awareness Message) 및 비주기적 메시지이며 one-hop릴레이를 허용하는 DENM(Decentralized Environmental Notification Message)를 전송한다. 위의 두 메시지는 특성과 생성 규칙이 다르기 때문에 동일한 혼잡 제어 기법을 적용하는 것은 비효율적이다. 따라서 우리는 각 메시지에 적용할 수 있는 혼잡 제어 기법들을 제안한다. 제안된 기법들을 통해서 단말들은 그들의 상황에 따라서 전송 여부를 결정하게 된다. 시뮬레이션 결과를 통해 제안된 기법이 기존 표준 방식 뿐만 아니라 최신의 비교 기법들보다 우수한 성능을 얻는 것을 확인하였다. 마지막으로 ITS(Intelligent Transport System)대역에서 LTE와 NR사이의 IDC문제를 고려하는 NR sidelink 자원할당 기법을 제안한다. 먼저, 표준에 지정된 스펙트럼 방출 마스크를 기반으로 현실적인 IDC 간섭을 모델링한다. 그런 다음 다중 에이전트 강화학습으로 자원할당 기법을 제안한다. 각 단말들은 자신들의 주변 환경을 관측하고 관측된 환경을 기반으로 행동하여 보상을 얻고 Q-network을 자신의 보상을 증가시키도록 정책을 업데이트 및 학습한다. 우리는 시뮬레이션 결과를 통하여 제안된 자원할당 박식이 기존기법 대비하여 PDR(Packet Delivery Ratio) 성능을 향상시키는 것을 확인하였다.Introduction 1 Efficient feedback mechanism for LTE-D2D Communication 8 CoCo: Context-aware congestion control scheme for C-V2X communications 35 IDC-aware resource allocation based on multi-agents reinforcement learning 67 Concluding remarks 84 Abstract(In Korean) 96 감사의 글 99Docto

D6.6 Final report on the METIS 5G system concept and technology roadmap

This deliverable presents the METIS 5G system concept which was developed to fulfil the requirements of the beyond-2020 connected information society and to extend today’s wireless communication systems to include new usage scenarios. The METIS 5G system concept consists of three generic 5G services and four main enablers. The three generic 5G services are Extreme Mobile BroadBand (xMBB), Massive Machine- Type Communications (mMTC), and Ultra-reliable Machine-Type Communication (uMTC). The four main enablers are Lean System Control Plane (LSCP), Dynamic RAN, Localized Contents and Traffic Flows, and Spectrum Toolbox. An overview of the METIS 5G architecture is given, as well as spectrum requirements and considerations. System-level evaluation of the METIS 5G system concept has been conducted, and we conclude that the METIS technical objectives are met. A technology roadmap outlining further 5G development, including a timeline and recommended future work is given.Popovski, P.; Mange, G.; Gozalvez -Serrano, D.; Rosowski, T.; Zimmermann, G.; Agyapong, P.; Fallgren, M.... (2014). D6.6 Final report on the METIS 5G system concept and technology roadmap. http://hdl.handle.net/10251/7676