Distributed Artificial Intelligence Solution for D2D Communication in 5G Networks

Device to Device (D2D) Communication is one of the technology components of the evolving 5G architecture, as it promises improvements in energy efficiency, spectral efficiency, overall system capacity, and higher data rates. The above noted improvements in network performance spearheaded a vast amount of research in D2D, which have identified significant challenges that need to be addressed before realizing their full potential in emerging 5G Networks. Towards this end, this paper proposes the use of a distributed intelligent approach to control the generation of D2D networks. More precisely, the proposed approach uses Belief-Desire-Intention (BDI) intelligent agents with extended capabilities (BDIx) to manage each D2D node independently and autonomously, without the help of the Base Station. The paper includes detailed algorithmic description for the decision of transmission mode, which maximizes the data rate, minimizes the power consumptions, while taking into consideration the computational load. Simulations show the applicability of BDI agents in jointly solving D2D challenges.Comment: 10 pages,9 figure

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Opportunistic Spectrum Utilization for Vehicular Communication Networks

Recently, vehicular networks (VANETs), has become the key technology of the next-generation intelligent transportation systems (ITS). By incorporating wireless communication and networking capabilities into automobiles, information can be efficiently and reliably disseminated among vehicles, road side units, and infrastructure, which enables a number of novel applications enhancing the road safety and providing the drivers/passengers with an information-rich environment. With the development of mobile Internet, people want to enjoy the Internet access in vehicles just as anywhere else. This fact, along with the soaring number of connected vehicles and the emerging data-craving applications and services, has led to a problem of spectrum scarcity, as the current spectrum bands for VANETs are difficult to accommodate the increasing mobile data demands. In this thesis, we aim to solve this problem by utilizing extra spectrum bands, which are not originally allocated for vehicular communications. In this case, the spectrum usage is based on an opportunistic manner, where the spectrum is not available if the primary system is active, or the vehicle is outside the service coverage due to the high mobility. We will analyze the features of such opportunistic spectrum, and design efficient protocols to utilize the spectrum for VANETs. Firstly, the application of cognitive radio technologies in VANETs, termed CR-VANETs, is proposed and analyzed. In CR-VANETs, the channel availability is severely affected by the street patterns and the mobility features of vehicles. Therefore, we theoretically analyze the channel availability in urban scenario, and obtain its statistics. Based on the knowledge of channel availability, an efficient channel access scheme for CR-VANETs is then designed and evaluated. Secondly, using WiFi to deliver mobile data, named WiFi offloading, is employed to deliver the mobile data on the road, in order to relieve the burden of the cellular networks, and provide vehicular users with a cost-effective data pipe. Using queueing theory, we analyze the offloading performance with respect to the vehicle mobility model and the users' QoS preferences. Thirdly, we employ device-to-device (D2D) communications in VANETs to further improve the spectrum efficiency. In a vehicular D2D (V-D2D) underlaying cellular network, proximate vehicles can directly communicate with each other with a relatively small transmit power, rather than traversing the base station. Therefore, many current transmissions can co-exist on one spectrum resource block. By utilizing the spatial diversity, the spectrum utilization is greatly enhanced. We study the performance of the V-D2D underlaying cellular network, considering the vehicle mobility and the street pattern. We also investigate the impact of the preference of D2D/cellular mode on the interference and network throughput, and obtain the theoretical results. In summary, the analysis and schemes developed in this thesis are useful to understand the future VANETs with heterogeneous access technologies, and provide important guidelines for designing and deploying such networks

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