176 research outputs found
D13.2 Techniques and performance analysis on energy- and bandwidth-efficient communications and networking
Deliverable D13.2 del projecte europeu NEWCOM#The report presents the status of the research work of the
various Joint Research Activities (JRA) in WP1.3 and the results
that were developed up to the second year of the project. For
each activity there is a description, an illustration of the
adherence to and relevance with the identified fundamental
open issues, a short presentation of the main results, and a
roadmap for the future joint research. In the Annex, for each
JRA, the main technical details on specific scientific activities
are described in detail.Peer ReviewedPostprint (published version
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Improving next-generation wireless network performance and reliability with deep learning
A rudimentary question whether machine learning in general, or deep learning in particular, could add to the well-established field of wireless communications, which has been evolving for close to a century, is often raised. While the use of deep learning based methods is likely to help build intelligent wireless solutions, this use becomes particularly challenging for the lower layers in the wireless communication stack. The introduction of the fifth generation of wireless communications (5G) has triggered the demand for “network intelligence” to support its promises for very high data rates and extremely low latency. Consequently, 5G wireless operators are faced with the challenges of network complexity, diversification of services, and personalized user experience. Industry standards have created enablers (such as the network data analytics function), but these enablers focus on post-mortem analysis at higher stack layers and have a periodicity in the time scale of seconds (or larger). The goal of this dissertation is to show a solution for these challenges and how a data-driven approach using deep learning could add to the field of wireless communications. In particular, I propose intelligent predictive and prescriptive abilities to boost reliability and eliminate performance bottlenecks in 5G cellular networks and beyond, show contributions that justify the value of deep learning in wireless communications across several different layers, and offer in-depth analysis and comparisons with baselines and industry standards. First, to improve multi-antenna network reliability against wireless impairments with power control and interference coordination for both packetized voice and beamformed data bearers, I propose the use of a joint beamforming, power control, and interference coordination algorithm based on deep reinforcement learning. This algorithm uses a string of bits and logic operations to enable simultaneous actions to be performed by the reinforcement learning agent. Consequently, a joint reward function is also proposed. I compare the performance of my proposed algorithm with the brute force approach and show that similar performance is achievable but with faster run-time as the number of transmit antennas increases. Second, in enhancing the performance of coordinated multipoint, I propose the use of deep learning binary classification to learn a surrogate function to trigger a second transmission stream instead of depending on the popular signal to interference plus noise measurement quantity. This surrogate function improves the users' sum-rate through focusing on pre-logarithmic terms in the sum-rate formula, which have larger impact on this rate. Third, performance of band switching can be improved without the need for a full channel estimation. My proposal of using deep learning to classify the quality of two frequency bands prior to granting the band switching leads to a significant improvement in users' throughput. This is due to the elimination of the industry standard measurement gap requirement—a period of silence where no data is sent to the users so they could measure the frequency bands before switching. In this dissertation, a group of algorithms for wireless network performance and reliability for downlink are proposed. My results show that the introduction of user coordinates enhance the accuracy of the predictions made with deep learning. Also, the choice of signal to interference plus noise ratio as the optimization objective may not always be the best choice to improve user throughput rates. Further, exploiting the spatial correlation of channels in different frequency bands can improve certain network procedures without the need for perfect knowledge of the per-band channel state information. Hence, an understanding of these results help develop novel solutions to enhancing these wireless networks at a much smaller time scale compared to the industry standards todayElectrical and Computer Engineerin
Modeling and Analysis of Cellular Networks Using Stochastic Geometry: A Tutorial
This paper presents a tutorial on stochastic geometry (SG)-based analysis for cellular networks. This tutorial is distinguished by its depth with respect to wireless communication details and its focus on cellular networks. This paper starts by modeling and analyzing the baseband interference in a baseline single-tier downlink cellular network with single antenna base stations and universal frequency reuse. Then, it characterizes signal-to-interference-plus-noise-ratio and its related performance metrics. In particular, a unified approach to conduct error probability, outage probability, and transmission rate analysis is presented. Although the main focus of this paper is on cellular networks, the presented unified approach applies for other types of wireless networks that impose interference protection around receivers. This paper then extends the unified approach to capture cellular network characteristics (e.g., frequency reuse, multiple antenna, power control, etc.). It also presents numerical examples associated with demonstrations and discussions. To this end, this paper highlights the state-of-the-art research and points out future research directions
Content delivery over multi-antenna wireless networks
The past few decades have witnessed unprecedented advances in information technology, which have significantly shaped the way we acquire and process information in our daily lives. Wireless communications has become the main means of access to data through mobile devices, resulting in a continuous exponential growth in wireless data traffic, mainly driven by the demand for high quality content.
Various technologies have been proposed by researchers to tackle this growth in 5G and beyond, including the use of increasing number of antenna elements, integrated point-to-multipoint delivery and caching, which constitute the core of this thesis. In particular, we study non-orthogonal content delivery in multiuser multiple-input-single-output (MISO) systems. First, a joint beamforming strategy for simultaneous delivery of broadcast and unicast services is investigated, based on layered division multiplexing (LDM) as a means of superposition coding. The system performance in terms of minimum required power under prescribed quality-of-service (QoS) requirements is examined in comparison with time division multiplexing (TDM). It is demonstrated through simulations that the non-orthogonal delivery strategy based on LDM significantly outperforms the orthogonal strategy based on TDM in terms of system throughput and reliability. To facilitate efficient implementation of the LDM-based beamforming design, we further propose a dual decomposition-based distributed approach. Next, we study an efficient multicast beamforming design in cache-aided multiuser MISO systems, exploiting proactive content placement and coded delivery. It is observed that the complexity of this problem grows exponentially with the number of subfiles delivered to each user in each time slot, which itself grows exponentially with the number of users in the system. Therefore, we propose a low-complexity alternative through time-sharing that limits the number of subfiles that can be received by a user in each time slot. Moreover, a joint design of content delivery and multicast beamforming is proposed to further enhance the system performance, under the constraint on maximum number of subfiles each user can decode in each time slot. Finally, conclusions are drawn in Chapter 5, followed by an outlook for future works.Open Acces
Técnicas de equalização e pré-codificação para sistemas MC-CDMA
Mestrado em Engenharia Eletrónica e TelecomunicaçõesO número de dispositivos com ligações e aplicações sem fios está a aumentar exponencialmente, causando problemas de interferência e diminuindo a capacidade do sistema. Isto desencadeou uma procura por uma eficiência espectral superior e, consequentemente, tornou-se necessário desenvolver novas arquitecturas celulares que suportem estas novas exigências.
Coordenação ou cooperação multicelular é uma arquitectura promissora para sistemas celulares sem fios. Esta ajuda a mitigar a interferência entre células, melhorando a equidade e a capacidade do sistema. É, portanto, uma arquitectura já em estudo ao abrigo da tecnologia LTE-Advanced sob o conceito de coordenação multiponto (CoMP). Nesta dissertação, considerámos um sistema coordenado MC-CDMA com pré-codificação e equalização iterativas.
Uma das técnicas mais eficientes de pré-codificação é o alinhamento de interferências (IA). Este é um conceito relativamente novo que permite aumentar a capacidade do sistema em canais de elevada interferência. Sabe-se que, para os sistemas MC-CDMA, os equalizadores lineares convencionais não são os mais eficientes, devido à interferência residual entre portadoras (ICI). No entanto, a equalização iterativa no domínio da frequência (FDE) foi identificada como sendo uma das técnicas mais eficientes para lidar com ICI e explorar a diversidade oferecida pelos sistemas MIMO MC-CDMA. Esta técnica é baseada no conceito Iterative Block Decision Feedback Equalization (IB-DFE).
Nesta dissertação, é proposto um sistema MC-CDMA que une a pré-codificação iterativa do alinhamento de interferências no transmissor ao equalizador baseado no IB-DFE, com cancelamento sucessivo de interferências (SIC) no receptor. Este é construído por dois blocos: um filtro linear, que mitiga a interferência inter-utilizador, seguido por um bloco iterativo no domínio da frequência, que separa eficientemente os fluxos de dados espaciais na presença de interferência residual inter-utilizador alinhada. Este esquema permite atingir o número máximo de graus de liberdade e permite simultaneamente um ganho óptimo de diversidade espacial. O desempenho deste esquema está perto do filtro adaptado- Matched Filter Bound (MFB).The number of devices with wireless connections and applications is increasing exponentially, causing interference problems and reducing the system’s capacity gain. This initiated a search for a higher spectral efficiency and therefore it became necessary to develop new cellular architectures that support these new requirements.
Multicell cooperation or coordination is a promising architecture for cellular wireless systems to mitigate intercell interference, improving system fairness and increasing capacity, and thus is already under study in LTE-Advanced under the coordinated multipoint (CoMP) concept. In this thesis, efficient iterative precoding and equalization is considered for coordinated MC-CDMA based systems.
One of the most efficient precoding techniques is interference alignment (IA), which is a relatively new concept that allows high capacity gains in interfering channels. It is well known that for MC-CDMA systems standard linear equalizers are not the most efficient due to residual inter carrier interference (ICI). However, iterative frequency-domain equalization (FDE) has been identified as one of the most efficient technique to deal with ICI and exploit the inherent space-frequency diversity of the MIMO MC-CDMA systems, namely the one based on Iterative Block Decision Feedback Equalization (IB-DFE) concept.
In this thesis, it is proposed a MC-CDMA system that joins iterative IA precoding at the transmitter with IB-DFE successive interference cancellation (SIC) based receiver structure. The receiver is implemented in two steps: a linear filter, which mitigates the inter-user aligned interference, followed by an iterative frequency-domain receiver, which efficiently separates the spatial streams in the presence of residual inter-user aligned interference. This scheme provides the maximum degrees of freedom (DoF) and allows almost the optimum space-diversity gain. The scheme performance is close to the matched filter bound (MFB)
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