Implementing Linear Predictive Coding based on a statistical model for LTE fronthaul

This thesis studies the application of Linear Predictive coding (LPC) in the downlink of Long Term Evolution (LTE) fronthaul, which comprises of BBU and RRH. This can act as an additional module in the existing system. Today, the transmission of a single complex sample from the BBU to the RRH consumes 30 bits. The research of the thesis is to analyze the application of linear prediction theory in the LTE downlink transmission, which will work as a compression scheme and reduce this 30 bits to lower value, at the same time fulfill the Error Vector Magnitude (EVM) requirement stated in the LTE standards made by 3rd Generation Partnership Project (3GPP). As 4G-LTE and the upcoming access technologies will deal with large number of data samples in the transmission, it is an advantage if those data samples can be compressed without destroying the information content. LPC or linear prediction coding has been proved to be a very effective method for speech compression in audio related applications. In this thesis, the same logic of compression is applied on digital data samples of the LTE and the results are analyzed. It is found that, if LPC is applied properly on the LTE, it is possible to compress data samples efficiently and transmit them from the BBU to the RRH with fewer bits. At the RRH those compressed data samples can be processed and the main information data can be reconstructed, with additional quantization error and noise. This is obvious because LPC is a lossy compression method. A statistical model is established to generate a table of linear prediction filter coefficients which will be present both at the BBU and the RRH, when compression and decompression of data samples are performed. Entropy is also calculated in order to analyze the achievable compression on an actual error vector after implementing certain compression coding such as Huffman coding. The specific coding technique is left as a scope of future research.Due to the growth of number of users and faster communication methods, mobile operators have to use the allocated resources more efficiently to meet the user demands. Like any other systems, mobile communication networks go through series of updates over time. In mobile communication system, these updates are known as “Releases”. The transition from 3rd Generation (3G) to 4th Generation (4G) took place with Release 8 in 2008. Many new techniques are introduced in 4G in order to use the available resources more efficiently for improving quality of services (QoSs). LTE (Long term evolution) or more commonly known as 4G communication system deals with much larger amount of data traffic than any other previous technologies. Hence it is of utmost importance that the operators make use of the allocated bandwidth more efficiently to serve the ever increasing number of users. It is possible for LTE to deal with this large amount of data due to the use of OFDM modulation technique which ensures better quality of communication. In OFDM, there exists multiple blocks of frequency bands stacked together as a whole, which are not related to one another. The LTE structure is different from any previous systems. In telecommunication systems, there exists a unit which handles all the data traffic to and from the transmitter and the receiver. This module is called the base station. In LTE, the base station is divided into two parts namely the Baseband Unit (BBU) and the Radio Unit (RU), where almost all the data processing takes place at the BBU, and the RU is used as both transmitter and receiver when data is exchanged to and from a mobile device. In recent years, a new type of architecture is proposed, which is called the C-RAN (Cloud Radio Access Network). In C-RAN, the BBU and the RU would be placed at two different locations. Multiple BBUs can be placed together at a single place called the BBU pool, whereas the RUs will be placed in separate places far from the BBU pool and connected via optical fibers. In this structure, RU is known as RRH (Remote Radio Head) as they are separated from the BBU. One main advantage of such a structure is that, only the RRH is placed near the user locality and the BBU can be put at the network operator’s vicinity. This also helps in reducing the operating and maintenance cost for the operator in many ways. Since the LTE imposes with massive amount of data traffic on the fronthaul (almost tenfold of the actual information data after applying error correcting coding, control signals etc.), it is very important to carry out compression of those data traffic before they are sent from the BBU to the RU. If good compression is carried out, then it becomes possible to accommodate more users, using the available resources. Although analog signals are used to transmit a message from the transmitter to the receiver over a medium, it is always important to convert those analog signal to digital signal to be transmitted from one block to the next block for processing, through the connecting link. The main purpose of this thesis work is to apply a compression technique which will minimize the number of bits needed to represent each of those data samples transmitted from the BBU to the RRH. The compression technique used in this thesis is to employ a module which will use certain number of previous data samples values to make a prediction of the next data sample. Then this predicted data sample is compared with the actual data sample and their difference is found. The difference between these two samples has a low magnitude, as a result it is possible to use lower number of bits in the digital domain to represent this value, and finally transmitted through the link to the RRH. At the RRH, the same prediction module is used to utilize these received samples of low magnitude, to make a prediction of the original data samples which are intended to be sent at the first place. In order to make the prediction module to function properly, it is very important to set up the filter values, which are known as the prediction coefficients. These coefficients play the role of successfully predicting data samples which are very similar to the original data samples. These coefficients are calculated by statistical method so that they can be used for any set of random data sample vector in the LTE. This thesis studies the performance of applying this prediction technique in LTE. In order to identify the efficiency of this applied compression technique, certain parameters are calculated using various simulations, and compared with the set of values as specified by the main researching bodies of the LTE. It is found that, the applied compression technique works fine in LTE as the simulation results support the validity of the scheme. It also proves that, it is possible to introduce this compression technique as an extension to the upcoming upgrades of the LTE, and this will facilitate accommodating more users with the available infrastructure resources

Fronthaul C-RAN baseado em ethernet

For the last decade mobile data traffic has been increasing at impressive rates. The proliferation of mobile devices together with high-bandwidth services like video and music streaming, social media and other cloud services have increased the load on top of the mobile network infrastructure. In order to support this massive increase in both users and bandwidth the next generation of mobile telecommunications network - 5G - explores new approaches, like the utilization of new frequency bands and the densification of base stations. This kind of requirements along with the inefficiency of the co-location of base band processing near the radio units encourages a rethink of traditional radio access networks. In this scenario emerges the C-RAN paradigm that intend to centralize all the base band processing (BBU) and replace current base stations for simpler, more efficient and compact solutions that only incorporate the radio front-end and respective radio processing (RRH). In addition to these benefits, centralized processing facilitates virtualization and resource sharing, interference management and cooperative processing technologies. This split of functions brings however, some challenges in respect to the data rates, bandwidth and latency in the link that connects BBUs and RRHs - the fronthaul. Today’s existing standards like CPRI weren’t originally designed for such applications and present some intrinsic bandwidth and flexibility limitations. It’s considered that another approach, based on packet switching, could mitigate some of these problems in addition to bring some advantages such as statistical multiplexing, flexible routing and compatibility with current widespread packet switching networks. They do however, present a number of challenges regarding latency and synchronization. This dissertation work focuses on the study and development of a fronthaul solution based in 10 Gigabit Ethernet over optical fiber. Development is done on top of two development kits based in Field Programmable Gate Array (FPGA) and implemented in an already operational C-RAN test-bed - currently with CPRI based fronthaul - at the Instituto de Telecomunicações - Aveiro.Durante a última década o tráfego de dados móveis tem aumentado a um ritmo impressionante. A proliferação de dispositivos móveis juntamente com serviços consumidores de grande largura de banda como streaming de vídeo e música, redes sociais e serviços na cloud têm colocado grande pressão na infraestrutura da rede móvel. Para suportar este aumento massivo de utilizadores e largura de banda a próxima geração de telecomunicações móveis – o 5G – explora novos conceitos, entre eles a utilização de bandas de frequências mais elevadas e a massificação das estações base. A este tipo de requisitos junta-se o facto da ineficiência da co-localização do processamento junto da unidade de rádio que incentiva a uma restruturação da arquitectura tradicional das redes móveis. Neste cenário surge o paradigma C-RAN, que pretende centralizar todo o processamento em banda base (BBU) e substituir as base stations atuais por soluções mais simples, eficientes e compactas que englobam apenas o processamento da parte de rádio e respetivo front-end de rádio frequência (RRH). Para além destes beneficios, a centralização do processamento facilita a virtualização e partilha de recursos, a gestão da interferência e tecnologias de processamento cooperativo. Esta divisão de funções traz no entanto alguns desafios no que diz respeito a largura de banda, taxas de dados e latências na interligação entre BBUs e RRHs – o fronthaul. Standards atualmente utilizados no link de fronthaul como o CPRI não foram originalmente desenhados para aplicações desta dimensão e apresentam algumas limitações, sendo intrinsecamente pouco flexíveis e eficientes. Acredita-se que outro tipo de abordagem, baseada em comutação de pacotes, poderia mitigar alguns destes problemas para além de trazer vantagens como a multiplexagem estatística, routing flexível e compatibilidade com redes de comutação de pacotes actuais. Apresentam no entanto vários desafios a nível de latência e sincronização associados. Este trabalho de dissertação foca-se então no estudo e desenvolvimento de uma solução para o fronthaul baseada em 10 Gigabit Ethernet sobre fibra ótica. O desenvolvimento será feito em dois kits de desenvolvimento baseados em Field Programmable Gate Array (FPGA) e implementado num demonstrador C-RAN já operacional - com fronthaul atualmente baseado em CPRI - no Instituto de Telecomunicações de Aveiro.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Multiservice Ethernet Digital Distributed Antenna Systems

Over 90% of wireless communications traffic occurs indoors and in-building wireless coverage is still one of the biggest obstacles for wireless users. As the growing demands on wireless capacity, coverage and connectivity have led to 4G and 5G standards, it has also become increasingly important to design and implement future-proof indoor wireless services in a cost effective manner. This thesis introduces a novel multi-service digital distributed antenna systems (DDAS) for indoor wireless coverage, which not only is able to transport multiple wireless carriers from different vendors and mobile operators, but also allows a converged architecture to integrate indoor wireless system with existing Ethernet infrastructures. The Cloud Radio Access Networks (C-RAN) has been suggested by major telecom vendors as the main architecture for last-mile coverage in 5G. However, the digital fronthaul interface defined in common public radio interface (CPRI), which is most widely adopted standard for C-RAN, requires very expensive infrastructures to be built due to the high data rate generated after digitisation. A solution has been introduced at the University of Cambridge previously to remove the digital redundancy by using a data compression technique which has shown 3-times higher transmission efficiency than CPRI. This thesis extends the concept to a more robust architecture allowing multiple wireless services to be transmitted simultaneously as well as being carried over standard Ethernet without losing the Quality of End-user Experience (QoE) and the Quality of Service (QoS) of in-building mobile network. A two-channel DDAS system with data compression algorithm is experimentally demonstrated, showing wide RF dynamic range for both 4G LTE service and 3G WCDMA service simultaneously carried over a single fibre-based infrastructure. The system leads to the design and implementation of full-service DDAS system allowing 14 channels (all 2/3/4G service from three major mobile operators) to be carried over single 10Gbps network. Typically, the system using CPRI will need over 30Gbps network to be installed for wireless coverage. Another key aspect covered in this thesis is the design and implementation of the multi-service DDAS over Ethernet (Eth-DDAS). Due to the stringent latency requirement in wireless services, mitigation of delays and errors in frame ordering has become a key challenge for putting DDAS over Ethernet. To overcome these problems, a special Eth-DDAS frame structure is proposed in this thesis. After digitisation, digital signal bearing RF information is packetised onto Ethernet-compatible frames with additional timestamps and sequence numbers before transported via fibre to the receiver. Three latency scenarios are tested with different payload sizes of the proposed frame structure and real-time RF performance is measured to prove the capability of implementation of such system in real-life using commercial off-the-shelf (COTS) ADC/DAC and FPGAs

Allocation of Communication and Computation Resources in Mobile Networks

Konvergence komunikačních a výpočetních technologií vedlo k vzniku Multi-Access Edge Computing (MEC). MEC poskytuje výpočetní výkon na tzv. hraně mobilních sítí (základnové stanice, jádro mobilní sítě), který lze využít pro optimalizaci mobilních sítí v reálném čase. Optimalizacev reálném čase je umožněna díky nízkému komunikačnímu zpoždění například v porovnání s Mobile Cloud Computing (MCC). Optimalizace mobilních sítí vyžaduje informace o mobilní síti od uživatelských zařízeních, avšak sběr těchto informací využívá komunikační prostředky, které jsou využívány i pro přenos uživatelských dat. Zvyšující se počet uživatelských zařízení, senzorů a taktéž komunikace vozidel tvoří překážku pro sběr informací o mobilních sítích z důvodu omezeného množství komunikačních prostředků. Tudíž je nutné navrhnout řešení, která umožní sběr těchto informací pro potřeby optimalizace mobilních sítí. V této práci je navrženo řešení pro komunikaci vysokého počtu zařízeních, které je postaveno na využití přímé komunikace mezi zařízeními. Pro motivování uživatelů, pro využití přeposílání dat pomocí přímé komunikace mezi uživateli je navrženo přidělování komunikačních prostředků jenž vede na přirozenou spolupráci uživatelů. Dále je provedena analýza spotřeby energie při využití přeposílání dat pomocí přímé komunikace mezi uživateli pro ukázání jejích výhod z pohledu spotřeby energie. Pro další zvýšení počtu komunikujících zařízení je využito mobilních létajících základových stanic (FlyBS). Pro nasazení FlyBS je navržen algoritmus, který hledá pozici FlyBS a asociaci uživatel k FlyBS pro zvýšení spokojenosti uživatelů s poskytovanými datovými propustnostmi. MEC lze využít nejen pro optimalizaci mobilních sítí z pohledu mobilních operátorů, ale taktéž uživateli mobilních sítí. Tito uživatelé mohou využít MEC pro přenost výpočetně náročných úloh z jejich mobilních zařízeních do MEC. Z důvodu mobility uživatel je nutné nalézt vhodně přidělení komunikačních a výpočetních prostředků pro uspokojení uživatelských požadavků. Tudíž je navržen algorithmus pro výběr komunikační cesty mezi uživatelem a MEC, jenž je posléze rozšířen o přidělování výpočetných prostředků společně s komunikačními prostředky. Navržené řešení vede k snížení komunikačního zpoždění o desítky procent.The convergence of communication and computing in the mobile networks has led to an introduction of the Multi-Access Edge Computing (MEC). The MEC combines communication and computing resources at the edge of the mobile network and provides an option to optimize the mobile network in real-time. This is possible due to close proximity of the computation resources in terms of communication delay, in comparison to the Mobile Cloud Computing (MCC). The optimization of the mobile networks requires information about the mobile network and User Equipment (UE). Such information, however, consumes a significant amount of communication resources. The finite communication resources along with the ever increasing number of the UEs and other devices, such as sensors, vehicles pose an obstacle for collecting the required information. Therefore, it is necessary to provide solutions to enable the collection of the required mobile network information from the UEs for the purposes of the mobile network optimization. In this thesis, a solution to enable communication of a large number of devices, exploiting Device-to-Device (D2D) communication for data relaying, is proposed. To motivate the UEs to relay data of other UEs, we propose a resource allocation algorithm that leads to a natural cooperation of the UEs. To show, that the relaying is not only beneficial from the perspective of an increased number of UEs, we provide an analysis of the energy consumed by the D2D communication. To further increase the number of the UEs we exploit a recent concept of the flying base stations (FlyBSs), and we develop a joint algorithm for a positioning of the FlyBS and an association of the UEs to increase the UEs satisfaction with the provided data rates. The MEC can be exploited not only for processing of the collected data to optimize the mobile networks, but also by the mobile users. The mobile users can exploit the MEC for the computation offloading, i.e., transferring the computation from their UEs to the MEC. However, due to the inherent mobility of the UEs, it is necessary to determine communication and computation resource allocation in order to satisfy the UEs requirements. Therefore, we first propose a solution for a selection of the communication path between the UEs and the MEC (communication resource allocation). Then, we also design an algorithm for joint communication and computation resource allocation. The proposed solution then lead to a reduction in the computation offloading delay by tens of percent

An LPC-based fronthaul compression scheme

Several new architectures are under investigation for cloud radio access networks, assuming distinct splits of functionality among the network elements. Consequently, the research on radio data compression for the fronthaul is based on assumptions that correspond to a wide variety of tradeoffs among data rate, signal distortion, latency, and computational cost. This letter describes a method for LTE downlink point-topoint signal compression based on linear prediction and Huffman coding, which is suitable for low cost encoding and decoding units with stringent restrictions on power consumption. The proposed method can work at various compression factors, such as 3.3:1 at an average EVM of 0.9%, or 4:1 at an average EVM of 2.1%