1,172 research outputs found
Source and channel coding using Fountain codes
The invention of Fountain codes is a major advance in the field of error correcting codes. The goal of this work is to study and develop algorithms for source and channel coding using a family of Fountain codes known as Raptor codes. From an asymptotic point of view, the best currently known sum-product decoding algorithm for non binary alphabets has a high complexity that limits its use in practice. For binary channels, sum-product decoding algorithms have been extensively studied and are known to perform well. In the first part of this work, we develop a decoding algorithm for binary codes on non-binary channels based on a combination of sum-product and maximum-likelihood decoding. We apply this algorithm to Raptor codes on both symmetric and non-symmetric channels. Our algorithm shows the best performance in terms of complexity and error rate per symbol for blocks of finite length for symmetric channels. Then, we examine the performance of Raptor codes under sum-product decoding when the transmission is taking place on piecewise stationary memoryless channels and on channels with memory corrupted by noise. We develop algorithms for joint estimation and detection while simultaneously employing expectation maximization to estimate the noise, and sum-product algorithm to correct errors. We also develop a hard decision algorithm for Raptor codes on piecewise stationary memoryless channels. Finally, we generalize our joint LT estimation-decoding algorithms for Markov-modulated channels. In the third part of this work, we develop compression algorithms using Raptor codes. More specifically we introduce a lossless text compression algorithm, obtaining in this way competitive results compared to the existing classical approaches. Moreover, we propose distributed source coding algorithms based on the paradigm proposed by Slepian and Wolf
Best Arm Identification Based Beam Acquisition in Stationary and Abruptly Changing Environments
We study the initial beam acquisition problem in millimeter wave (mm-wave)
networks from the perspective of best arm identification in multi-armed bandits
(MABs). For the stationary environment, we propose a novel algorithm called
concurrent beam exploration, CBE, in which multiple beams are grouped based on
the beam indices and are simultaneously activated to detect the presence of the
user. The best beam is then identified using a Hamming decoding strategy. For
the case of orthogonal and highly directional thin beams, we characterize the
performance of CBE in terms of the probability of missed detection and false
alarm in a beam group (BG). Leveraging this, we derive the probability of beam
selection error and prove that CBE outperforms the state-of-the-art strategies
in this metric.
Then, for the abruptly changing environments, e.g., in the case of moving
blockages, we characterize the performance of the classical sequential halving
(SH) algorithm. In particular, we derive the conditions on the distribution of
the change for which the beam selection error is exponentially bounded. In case
the change is restricted to a subset of the beams, we devise a strategy called
K-sequential halving and exhaustive search, K-SHES, that leads to an improved
bound for the beam selection error as compared to SH. This policy is
particularly useful when a near-optimal beam becomes optimal during the
beam-selection procedure due to abruptly changing channel conditions. Finally,
we demonstrate the efficacy of the proposed scheme by employing it in a tandem
beam refinement and data transmission scheme
Neural network mechanisms of working memory interference
[eng] Our ability to memorize is at the core of our cognitive abilities. How could we effectively make decisions without considering memories of previous experiences? Broadly, our memories can be divided in two categories: long-term and short-term memories. Sometimes, short-term memory is also called working memory and throughout this thesis I will use both terms interchangeably. As the names suggest, long-term memory is the memory you use when you remember concepts for a long time, such as your name or age, while short-term memory is the system you engage while choosing between different wines at the liquor store. As your attention jumps from one bottle to another, you need to hold in memory characteristics of previous ones to pick your favourite. By the time you pick your favourite bottle, you might remember the prices or grape types of the other bottles, but you are likely to forget all of those details an hour later at home, opening the wine in front of your guests.
The overall goal of this thesis is to study the neural mechanisms that underlie working memory interference, as reflected in quantitative, systematic behavioral biases. Ultimately, the goal of each chapter, even when focused exclusively on behavioral experiments, is to nail down plausible neural mechanisms that can produce specific behavioral and neurophysiological findings. To this end, we use the bump-attractor model as our working hypothesis, with which we often contrast the synaptic working memory model. The work performed during this thesis is described here in 3 main chapters, encapsulation 5 broad goals:
In Chapter 4.1, we aim at testing behavioral predictions of a bump-attractor (1) network when used to store multiple items. Moreover, we connected two of such networks aiming to model feature-binding through selectivity synchronization (2).
In Chapter 4.2, we aim to clarify the mechanisms of working memory interference from previous memories (3), the so-called serial biases. These biases provide an excellent opportunity to contrast activity-based and activity-silent mechanisms because both mechanisms have been proposed to be the underlying cause of those biases.
In Chapter 4.3, armed with the same techniques used to seek evidence for activity-silent mechanisms, we test a prediction of the bump-attractor model with short-term plasticity (4). Finally, in light of the results from aim 4 and simple computer simulations, we reinterpret previous studies claiming evidence for activity-silent mechanisms (5)
Unsupervised Learning on Monocular Videos for 3D Human Pose Estimation
In the presence of annotated data, deep human pose estimation networks yield
impressive performance. Nevertheless, annotating new data is extremely
time-consuming, particularly in real-world conditions. Here, we address this by
leveraging contrastive self-supervised (CSS) learning to extract rich latent
vectors from single-view videos. Instead of simply treating the latent features
of nearby frames as positive pairs and those of temporally-distant ones as
negative pairs as in other CSS approaches, we explicitly disentangle each
latent vector into a time-variant component and a time-invariant one. We then
show that applying CSS only to the time-variant features, while also
reconstructing the input and encouraging a gradual transition between nearby
and away features, yields a rich latent space, well-suited for human pose
estimation. Our approach outperforms other unsupervised single-view methods and
matches the performance of multi-view techniques
Recommended from our members
Research and developments of Dirac video codec
This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel University.In digital video compression, apart from storage, successful transmission of the compressed video
data over the bandwidth limited erroneous channels is another important issue. To enable a video
codec for broadcasting application, it is required to implement the corresponding coding tools (e.g.
error-resilient coding, rate control etc.). They are normally non-normative parts of a video codec and
hence their specifications are not defined in the standard. In Dirac as well, the original codec is
optimized for storage purpose only and so, several non-normative part of the encoding tools are still
required in order to be able to use in other types of application.
Being the "Research and Developments of the Dirac Video Codec" as the research title, phase I of
the project is mainly focused on the error-resilient transmission over a noisy channel. The error-resilient
coding method used here is a simple and low complex coding scheme which provides the
error-resilient transmission of the compressed video bitstream of Dirac video encoder over the packet
erasure wired network. The scheme combines source and channel coding approach where error-resilient
source coding is achieved by data partitioning in the wavelet transformed domain and
channel coding is achieved through the application of either Rate-Compatible Punctured
Convolutional (RCPC) Code or Turbo Code (TC) using un-equal error protection between header plus
MV and data. The scheme is designed mainly for the packet-erasure channel, i.e. targeted for the
Internet broadcasting application.
But, for a bandwidth limited channel, it is still required to limit the amount of bits generated from
the encoder depending on the available bandwidth in addition to the error-resilient coding. So, in the
2nd phase of the project, a rate control algorithm is presented. The algorithm is based upon the Quality
Factor (QF) optimization method where QF of the encoded video is adaptively changing in order to
achieve average bitrate which is constant over each Group of Picture (GOP). A relation between the
bitrate, R and the QF, which is called Rate-QF (R-QF) model is derived in order to estimate the
optimum QF of the current encoding frame for a given target bitrate, R.
In some applications like video conferencing, real-time encoding and decoding with minimum
delay is crucial, but, the ability to do real-time encoding/decoding is largely determined by the
complexity of the encoder/decoder. As we all know that motion estimation process inside the encoder
is the most time consuming stage. So, reducing the complexity of the motion estimation stage will
certainly give one step closer to the real-time application. So, as a partial contribution toward realtime
application, in the final phase of the research, a fast Motion Estimation (ME) strategy is designed
and implemented. It is the combination of modified adaptive search plus semi-hierarchical way of
motion estimation. The same strategy was implemented in both Dirac and H.264 in order to
investigate its performance on different codecs. Together with this fast ME strategy, a method which
is called partial cost function calculation in order to further reduce down the computational load of the
cost function calculation was presented. The calculation is based upon the pre-defined set of patterns
which were chosen in such a way that they have as much maximum coverage as possible over the
whole block.
In summary, this research work has contributed to the error-resilient transmission of compressed
bitstreams of Dirac video encoder over a bandwidth limited error prone channel. In addition to this,
the final phase of the research has partially contributed toward the real-time application of the Dirac
video codec by implementing a fast motion estimation strategy together with partial cost function
calculation idea.BBC R&D and Brunel University
ํฌ์์ธ์ง๋ฅผ ์ด์ฉํ ์ ์ก๊ธฐ์ ์ฐ๊ตฌ
ํ์๋
ผ๋ฌธ (๋ฐ์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2019. 2. ์ฌ๋ณํจ.The new wave of the technology revolution, named the fifth wireless systems, is changing our daily life dramatically. These days, unprecedented services and applications such as driverless vehicles and drone-based deliveries, smart cities and factories, remote medical diagnosis and surgery, and artificial intelligence-based personalized assistants are emerging. Communication mechanisms associated with these new applications and services are way different from traditional communications in terms of latency, energy efficiency, reliability, flexibility, and connection density. Since the current radio access mechanism cannot support these diverse services and applications, a new approach to deal with these relentless changes should be introduced.
This compressed sensing (CS) paradigm is very attractive alternative to the conventional information processing operations including sampling, sensing, compression, estimation, and detection. To apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. In the last decade, CS techniques have spread rapidly in many applications such as medical imaging, machine learning, radar detection, seismology, computer science, statistics, and many others. Also, various wireless communication applications exploiting the sparsity of a target signal have been studied. Notable examples include channel estimation, interference cancellation, angle estimation, spectrum sensing, and symbol detection. The distinct feature of this work, in contrast to the conventional approaches exploiting naturally acquired sparsity, is to exploit intentionally designed sparsity to improve the quality of the communication systems.
In the first part of the dissertation, we study the mapping data information into the sparse signal in downlink systems. We propose an approach, called sparse vector coding (SVC), suited for the short packet transmission. In SVC, since the data information is mapped to the position of sparse vector, whole data packet can be decoded by idenitifying nonzero positions of the sparse vector. From our simulations, we show that the packet error rate of SVC outperforms the conventional channel coding schemes at the URLLC regime. Moreover, we discuss the SVC transmission for the massive MTC access by overlapping multiple SVC-based packets into the same resources. Using the spare vector overlapping and multiuser CS decoding scheme, SVC-based transmission provides robustness against the co-channel interference and also provide comparable performance than other non-orthogonal multiple access (NOMA) schemes. By using the fact that SVC only identifies the support of sparse vector, we extend the SVC transmission without pilot transmission, called pilot-less SVC. Instead of using the support, we further exploit the magnitude of sparse vector for delivering additional information. This scheme is referred to as enhanced SVC. The key idea behind the proposed E-SVC transmission scheme is to transform the small information into a sparse vector and map the side-information into a magnitude of the sparse vector. Metaphorically, E-SVC can be thought as a standing a few poles to the empty table. As long as the number of poles is small enough and the measurements contains enough information to find out the marked cell positions, accurate recovery of E-SVC packet can be guaranteed.
In the second part of this dissertation, we turn our attention to make sparsification of the non-sparse signal, especially for the pilot transmission and channel estimation. Unlike the conventional scheme where the pilot signal is transmitted without modification, the pilot signals are sent after the beamforming in the proposed technique. This work is motivated by the observation that the pilot overhead must scale linearly with the number of taps in CIR vector and the number of transmit antennas so that the conventional pilot transmission is not an appropriate option for the IoT devices. Primary goal of the proposed scheme is to minimize the nonzero entries of a time-domain channel vector by the help of multiple antennas at the basestation. To do so, we apply the time-domain sparse precoding, where each precoded channel propagates via fewer tap than the original channel vector. The received channel vector of beamformed pilots can be jointly estimated by the sparse recovery algorithm.5์ธ๋ ๋ฌด์ ํต์ ์์คํ
์ ์๋ก์ด ๊ธฐ์ ํ์ ์ ๋ฌด์ธ ์ฐจ๋ ๋ฐ ํญ๊ณต๊ธฐ, ์ค๋งํธ ๋์ ๋ฐ ๊ณต์ฅ, ์๊ฒฉ ์๋ฃ ์ง๋จ ๋ฐ ์์ , ์ธ๊ณต ์ง๋ฅ ๊ธฐ๋ฐ ๋ง์ถคํ ์ง์๊ณผ ๊ฐ์ ์ ๋ก ์๋ ์๋น์ค ๋ฐ ์์ฉํ๋ก๊ทธ๋จ์ผ๋ก ๋ถ์ํ๊ณ ์๋ค. ์ด๋ฌํ ์๋ก์ด ์ ํ๋ฆฌ์ผ์ด์
๋ฐ ์๋น์ค์ ๊ด๋ จ๋ ํต์ ๋ฐฉ์์ ๋๊ธฐ ์๊ฐ, ์๋์ง ํจ์จ์ฑ, ์ ๋ขฐ์ฑ, ์ ์ฐ์ฑ ๋ฐ ์ฐ๊ฒฐ ๋ฐ๋ ์ธก๋ฉด์์ ๊ธฐ์กด ํต์ ๊ณผ ๋งค์ฐ ๋ค๋ฅด๋ค. ํ์ฌ์ ๋ฌด์ ์ก์ธ์ค ๋ฐฉ์์ ๋น๋กฏํ ์ข
๋์ ์ ๊ทผ๋ฒ์ ์ด๋ฌํ ์๊ตฌ ์ฌํญ์ ๋ง์กฑํ ์ ์๊ธฐ ๋๋ฌธ์ ์ต๊ทผ์ sparse processing๊ณผ ๊ฐ์ ์๋ก์ด ์ ๊ทผ ๋ฐฉ๋ฒ์ด ์ฐ๊ตฌ๋๊ณ ์๋ค. ์ด ์๋ก์ด ์ ๊ทผ ๋ฐฉ๋ฒ์ ํ๋ณธ ์ถ์ถ, ๊ฐ์ง, ์์ถ, ํ๊ฐ ๋ฐ ํ์ง๋ฅผ ํฌํจํ ๊ธฐ์กด์ ์ ๋ณด ์ฒ๋ฆฌ์ ๋ํ ํจ์จ์ ์ธ ๋์ฒด๊ธฐ์ ๋ก ํ์ฉ๋๊ณ ์๋ค. ์ง๋ 10๋
๋์ compressed sensing (CS)๊ธฐ๋ฒ์ ์๋ฃ์์, ๊ธฐ๊ณํ์ต, ํ์ง, ์ปดํจํฐ ๊ณผํ, ํต๊ณ ๋ฐ ๊ธฐํ ์ฌ๋ฌ ๋ถ์ผ์์ ๋น ๋ฅด๊ฒ ํ์ฐ๋์๋ค. ๋ํ, ์ ํธ์ ํฌ์์ฑ(sparsity)๋ฅผ ์ด์ฉํ๋ CS ๊ธฐ๋ฒ์ ๋ค์ํ ๋ฌด์ ํต์ ์ด ์ฐ๊ตฌ๋์๋ค. ์ฃผ๋ชฉํ ๋งํ ์๋ก๋ ์ฑ๋ ์ถ์ , ๊ฐ์ญ ์ ๊ฑฐ, ๊ฐ๋ ์ถ์ , ๋ฐ ์คํํธ๋ผ ๊ฐ์ง๊ฐ ์์ผ๋ฉฐ ํ์ฌ๊น์ง ์ฐ๊ตฌ๋ ์ฃผ์ด์ง ์ ํธ๊ฐ ๊ฐ์ง๊ณ ์๋ ๋ณธ๋์ ํฌ์์ฑ์ ์ฃผ๋ชฉํ์์ผ๋ ๋ณธ ๋
ผ๋ฌธ์์๋ ๊ธฐ์กด์ ์ ๊ทผ ๋ฐฉ๋ฒ๊ณผ ๋ฌ๋ฆฌ ์ธ์์ ์ผ๋ก ์ค๊ณ๋ ํฌ์์ฑ์ ์ด์ฉํ์ฌ ํต์ ์์คํ
์ ์ฑ๋ฅ์ ํฅ์์ํค๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค.
์ฐ์ ๋ณธ ๋
ผ๋ฌธ์ ๋ค์ด๋งํฌ ์ ์ก์์ ํฌ์ ์ ํธ ๋งคํ์ ํตํ ๋ฐ์ดํฐ ์ ์ก ๋ฐฉ๋ฒ์ ์ ์ํ๋ฉฐ ์งง์ ํจํท (short packet) ์ ์ก์ ์ ํฉํ CS ์ ๊ทผ๋ฒ์ ํ์ฉํ๋ ๊ธฐ์ ์ ์ ์ํ๋ค. ์ ์ํ๋ ๊ธฐ์ ์ธ ํฌ์๋ฒกํฐ์ฝ๋ฉ (sparse vector coding, SVC)์ ๋ฐ์ดํฐ ์ ๋ณด๊ฐ ์ธ๊ณต์ ์ธ ํฌ์๋ฒกํฐ์ nonzero element์ ์์น์ ๋งคํํ์ฌ ์ ์ก๋ ๋ฐ์ดํฐ ํจํท์ ํฌ์๋ฒกํฐ์ 0์ด ์๋ ์์น๋ฅผ ์๋ณํจ์ผ๋ก ์์ ํธ ๋ณต์์ด ๊ฐ๋ฅํ๋ค. ๋ถ์๊ณผ ์๋ฎฌ๋ ์ด์
์ ํตํด ์ ์ํ๋ SVC ๊ธฐ๋ฒ์ ํจํท ์ค๋ฅ๋ฅ ์ ultra-reliable and low latency communications (URLLC) ์๋น์ค๋ฅผ ์ง์์ ์ํด ์ฌ์ฉ๋๋ ์ฑ๋์ฝ๋ฉ๋ฐฉ์๋ณด๋ค ์ฐ์ํ ์ฑ๋ฅ์ ๋ณด์ฌ์ค๋ค. ๋ํ, ๋ณธ ๋
ผ๋ฌธ์ SVC๊ธฐ์ ์ ๋ค์์ ์ธ๊ฐ์ง ์์ญ์ผ๋ก ํ์ฅํ์๋ค. ์ฒซ์งธ๋ก, ์ฌ๋ฌ ๊ฐ์ SVC ๊ธฐ๋ฐ ํจํท์ ๋์ผํ ์์์ ๊ฒน์น๊ฒ ์ ์กํจ์ผ๋ก ์ํฅ๋งํฌ์์ ๋๊ท๋ชจ ์ ์ก์ ์ง์ํ๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ค์ฒฉ๋ ํฌ์๋ฒกํฐ๋ฅผ ๋ค์ค์ฌ์ฉ์ CS ๋์ฝ๋ฉ ๋ฐฉ์์ ์ฌ์ฉํ์ฌ ์ฑ๋ ๊ฐ์ญ์ ๊ฐ์ธํ ์ฑ๋ฅ์ ์ ๊ณตํ๊ณ ๋น์ง๊ต ๋ค์ค ์ ์ (NOMA) ๋ฐฉ์๊ณผ ์ ์ฌํ ์ฑ๋ฅ์ ์ ๊ณตํ๋ค. ๋์งธ๋ก, SVC ๊ธฐ์ ์ด ํฌ์ ๋ฒกํฐ์ support๋ง์ ์๋ณํ๋ค๋ ์ฌ์ค์ ์ด์ฉํ์ฌ ํ์ผ๋ฟ ์ ์ก์ด ํ์์๋ pilotless-SVC ์ ์ก ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ์ฑ๋ ์ ๋ณด๊ฐ ์๋ ๊ฒฝ์ฐ์๋ ํฌ์ ๋ฒกํฐ์ support์ ํฌ๊ธฐ๋ ์ฑ๋์ ํฌ๊ธฐ์ ๋น๋กํ๊ธฐ ๋๋ฌธ์ pilot์์ด ๋ณต์์ด ๊ฐ๋ฅํ๋ค. ์
์งธ๋ก, ํฌ์๋ฒกํฐ์ support์ ํฌ๊ธฐ์ ์ถ๊ฐ ์ ๋ณด๋ฅผ ์ ์กํจ์ผ๋ก ๋ณต์ ์ฑ๋ฅ์ ํฅ์ ์ํค๋ enhanced SVC (E-SVC)๋ฅผ ์ ์ํ๋ค. ์ ์๋ E-SVC ์ ์ก ๋ฐฉ์์ ํต์ฌ ์๋๋์ด๋ ์งง์ ํจํท์ ์ ์ก๋๋ ์ ๋ณด๋ฅผ ํฌ์ ๋ฒกํฐ๋ก ๋ณํํ๊ณ ์ ๋ณด ๋ณต์์ ๋ณด์กฐํ๋ ์ถ๊ฐ ์ ๋ณด๋ฅผ ํฌ์ ๋ฒกํฐ์ ํฌ๊ธฐ (magnitude)๋ก ๋งคํํ๋ ๊ฒ์ด๋ค. ๋ง์ง๋ง์ผ๋ก, SVC ๊ธฐ์ ์ ํ์ผ๋ฟ ์ ์ก์ ํ์ฉํ๋ ๋ฐฉ๋ฒ์ ์ ์ํ๋ค. ํนํ, ์ฑ๋ ์ถ์ ์ ์ํด ์ฑ๋ ์ํ์ค ์๋ต์ ์ ํธ๋ฅผ ํฌ์ํํ๋ ํ๋ฆฌ์ฝ๋ฉ ๊ธฐ๋ฒ์ ์ ์ํ๋ค. ํ์ผ๋ฟ ์ ํธ์ ํ๋ก์ฝ๋ฉ ์์ด ์ ์ก๋๋ ๊ธฐ์กด์ ๋ฐฉ์๊ณผ ๋ฌ๋ฆฌ, ์ ์๋ ๊ธฐ์ ์์๋ ํ์ผ๋ฟ ์ ํธ๋ฅผ ๋นํฌ๋ฐํ์ฌ ์ ์กํ๋ค. ์ ์๋ ๊ธฐ๋ฒ์ ๊ธฐ์ง๊ตญ์์ ๋ค์ค ์ํ
๋๋ฅผ ํ์ฉํ์ฌ ์ฑ๋ ์๋ต์ 0์ด ์๋ ์์๋ฅผ ์ต์ํํ๋ ์๊ฐ ์์ญ ํฌ์ ํ๋ฆฌ์ฝ๋ฉ์ ์ ์ฉํ์๋ค. ์ด๋ฅผ ํตํด ๋ ์ ํํ ์ฑ๋ ์ถ์ ์ ๊ฐ๋ฅํ๋ฉฐ ๋ ์ ์ ํ์ผ๋ฟ ์ค๋ฒํค๋๋ก ์ฑ๋ ์ถ์ ์ด ๊ฐ๋ฅํ๋ค.Abstract i
Contents iv
List of Tables viii
List of Figures ix
1 INTRODUCTION 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1.1 Three Key Services in 5G systems . . . . . . . . . . . . . . . 2
1.1.2 Sparse Processing in Wireless Communications . . . . . . . . 4
1.2 Contributions and Organization . . . . . . . . . . . . . . . . . . . . . 7
1.3 Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2 Sparse Vector Coding for Downlink Ultra-reliable and Low Latency Communications
12
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2 URLLC Service Requirements . . . . . . . . . . . . . . . . . . . . . 15
2.2.1 Latency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.2.2 Ultra-High Reliability . . . . . . . . . . . . . . . . . . . . . 17
2.2.3 Coexistence . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.3 URLLC Physical Layer in 5G NR . . . . . . . . . . . . . . . . . . . 18
2.3.1 Packet Structure . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.2 Frame Structure and Latency-sensitive Scheduling Schemes . 20
2.3.3 Solutions to the Coexistence Problem . . . . . . . . . . . . . 22
2.4 Short-sized Packet in LTE-Advanced Downlink . . . . . . . . . . . . 24
2.5 Sparse Vector Coding . . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.5.1 SVC Encoding and Transmission . . . . . . . . . . . . . . . 25
2.5.2 SVC Decoding . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.3 Identification of False Alarm . . . . . . . . . . . . . . . . . . 33
2.6 SVC Performance Analysis . . . . . . . . . . . . . . . . . . . . . . . 36
2.7 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.7.1 Codebook Design . . . . . . . . . . . . . . . . . . . . . . . . 48
2.7.2 High-order Modulation . . . . . . . . . . . . . . . . . . . . . 49
2.7.3 Diversity Transmission . . . . . . . . . . . . . . . . . . . . . 50
2.7.4 SVC without Pilot . . . . . . . . . . . . . . . . . . . . . . . 50
2.7.5 Threshold to Prevent False Alarm Event . . . . . . . . . . . . 51
2.8 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 52
2.8.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 52
2.8.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 53
2.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3 Sparse Vector Coding for Uplink Massive Machine-type Communications 59
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.2 Uplink NOMA transmission for mMTC . . . . . . . . . . . . . . . . 61
3.3 Sparse Vector Coding based NOMA for mMTC . . . . . . . . . . . . 63
3.3.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.3.2 Joint Multiuser Decoding . . . . . . . . . . . . . . . . . . . . 66
3.4 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 68
3.4.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 68
3.4.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 69
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4 Pilot-less Sparse Vector Coding for Short Packet Transmission 72
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2 Pilot-less Sparse Vector Coding Processing . . . . . . . . . . . . . . 75
4.2.1 SVC Processing with Pilot Symbols . . . . . . . . . . . . . . 75
4.2.2 Pilot-less SVC . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.2.3 PL-SVC Decoding in Multiple Basestation Antennas . . . . . 78
4.3 Simulations and Discussions . . . . . . . . . . . . . . . . . . . . . . 80
4.3.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 80
4.3.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 81
4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
5 Joint Analog and Quantized Feedback via Sparse Vector Coding 84
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
5.2 System Model for Joint Spase Vector Coding . . . . . . . . . . . . . 86
5.3 Sparse Recovery Algorithm and Performance Analysis . . . . . . . . 90
5.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
5.4.1 Linear Interpolation of Sensing Information . . . . . . . . . . 96
5.4.2 Linear Combined Feedback . . . . . . . . . . . . . . . . . . 96
5.4.3 One-shot Packet Transmission . . . . . . . . . . . . . . . . . 96
5.5 Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.5.1 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . 97
5.5.2 Results and Discussions . . . . . . . . . . . . . . . . . . . . 98
5.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
6 Sparse Beamforming for Enhanced Mobile Broadband Communications 101
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
6.1.1 Increase the number of transmit antennas . . . . . . . . . . . 102
6.1.2 2D active antenna system (AAS) . . . . . . . . . . . . . . . . 103
6.1.3 3D channel environment . . . . . . . . . . . . . . . . . . . . 104
6.1.4 RS transmission for CSI acquisition . . . . . . . . . . . . . . 106
6.2 System Design and Standardization of FD-MIMO Systems . . . . . . 107
6.2.1 Deployment scenarios . . . . . . . . . . . . . . . . . . . . . 108
6.2.2 Antenna configurations . . . . . . . . . . . . . . . . . . . . . 108
6.2.3 TXRU architectures . . . . . . . . . . . . . . . . . . . . . . 109
6.2.4 New CSI-RS transmission strategy . . . . . . . . . . . . . . . 112
6.2.5 CSI feedback mechanisms for FD-MIMO systems . . . . . . 114
6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
6.3.1 Basic System Model . . . . . . . . . . . . . . . . . . . . . . 116
6.3.2 Beamformed Pilot Transmission . . . . . . . . . . . . . . . . 117
6.4 Sparsification of Pilot Beamforming . . . . . . . . . . . . . . . . . . 118
6.4.1 Time-domain System Model without Pilot Beamforming . . . 119
6.4.2 Pilot Beamforming . . . . . . . . . . . . . . . . . . . . . . . 120
6.5 Channel Estimation of Beamformed Pilots . . . . . . . . . . . . . . . 124
6.5.1 Recovery using Multiple Measurement Vector . . . . . . . . . 124
6.5.2 MSE Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 128
6.6 Simulations and Discussion . . . . . . . . . . . . . . . . . . . . . . . 129
6.6.1 Simulation Setup . . . . . . . . . . . . . . . . . . . . . . . . 129
6.6.2 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 130
6.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
7 Conclusion 136
7.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
7.2 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . 139
Abstract (In Korean) 152Docto
Clustering of Preferred Directions During Brain-Computer Interface Usage
Brain-computer interfaces (BCIs) are proving to be viable clinical interventions for sufferers of amyotrophic lateral sclerosis, amputations, and spinal cord injuries. To improve the viability of BCIs, it will help to have a thorough understanding of how the brain controls them. Neural activity during usage of certain BCIs behaves in a surprising and seemingly counterintuitive manner โ the preferred directions (PDs) of neurons cluster together. We trained monkeys to reach to targets in a center-out task either using their arm or a BCI. We found that neuronsโ PDs cluster similarly during training of the BCI decoder and usage of the BCI, but remain relatively unclustered when the monkeys use their arms. Modulation depths increase upon usage of the BCI, and narrowness of tuning tends to either increase or decrease rather than staying the same. In addition, the cluster direction can be predicted from per-target performance. A model where two neuronsโ PDs approach one another reveals how much modulation depths have to increase to maintain controllability. This thesis concludes with considerations of why this clustering might occur, and whether or not it benefits BCI control
Optimal Power Allocation in MIMO wire-tap channels
Projecte Finl de Carrera fet en col.laboraciรณ amb Universitร La Sapienza di Roma.English: Study of a methodology that, without the use of cryptography, limits the possible "intelligence" present at the eavesdropper and increases the level of secrecy on a wireless environment using MIMO systems.Castellano: Estudio de una metodologia que, sin hacer uso de la criptografia, permite limitar la posible "inteligencia" del espia, con la finalidad de aumentar la confidencialidad en comunicacines wireless con sistemas MIMOCatalร : Estudi d'una metodologia que, sense fer รบs de la criptografia, permet limitar la possible "intel.ligรจncia" de l'espia per tal d'augmentar la confidencialitat en comunicacions wireless amb sistemes MIMO
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