125 research outputs found
Randomized Adversarial Imitation Learning for Autonomous Driving
With the evolution of various advanced driver assistance system (ADAS)
platforms, the design of autonomous driving system is becoming more complex and
safety-critical. The autonomous driving system simultaneously activates
multiple ADAS functions; and thus it is essential to coordinate various ADAS
functions. This paper proposes a randomized adversarial imitation learning
(RAIL) method that imitates the coordination of autonomous vehicle equipped
with advanced sensors. The RAIL policies are trained through derivative-free
optimization for the decision maker that coordinates the proper ADAS functions,
e.g., smart cruise control and lane keeping system. Especially, the proposed
method is also able to deal with the LIDAR data and makes decisions in complex
multi-lane highways and multi-agent environments
Feasibility Study of Stochastic Streaming with 4K UHD Video Traces
This paper performs the feasibility study of stochastic video streaming
algorithms with up-to-date 4K ultra-high-definition (UHD) video traces. In
previous work, various stochastic video streaming algorithms were proposed
which maximize time-average video streaming quality subject to queue stability
based on the information of queue-backlog length. The performance improvements
with the stochastic video streaming algorithms were verified with traditional
MPEG test sequences; but there is no study how much the proposed stochastic
algorithm is better when we consider up-to-date 4K UHD video traces. Therefore,
this paper evaluates the stochastic streaming algorithms with 4K UHD video
traces; and verifies that the stochastic algorithms perform better than
queue-independent algorithms, as desired.Comment: Presented at the International Conference on ICT Convergence (ICTC),
Jeju Island, Korea, 28 - 30 October 201
Wireless Video Caching and Dynamic Streaming under Differentiated Quality Requirements
This paper considers one-hop device-to-device (D2D)-assisted wireless caching
networks that cache video files of varying quality levels, with the assumption
that the base station can control the video quality but cache-enabled devices
cannot. Two problems arise in such a caching network: file placement problem
and node association problem. This paper suggests a method to cache videos of
different qualities, and thus of varying file sizes, by maximizing the sum of
video quality measures that users can enjoy. There exists an interesting
trade-off between video quality and video diversity, i.e., the ability to
provision diverse video files. By caching high-quality files, the cache-enabled
devices can provide high-quality video, but cannot cache a variety of files.
Conversely, when the device caches various files, it cannot provide a good
quality for file-requesting users. In addition, when multiple devices cache the
same file but their qualities are different, advanced node association is
required for file delivery. This paper proposes a node association algorithm
that maximizes time-averaged video quality for multiple users under a playback
delay constraint. In this algorithm, we also consider request collision, the
situation where several users request files from the same device at the same
time, and we propose two ways to cope with the collision: scheduling of one
user and non-orthogonal multiple access. Simulation results verify that the
proposed caching method and the node association algorithm work reliably.Comment: 13 pages, 11 figures, accepted for publication in IEEE Journal on
Selected Areas in Communication
Blind Signal Classification for Non-Orthogonal Multiple Access in Vehicular Networks
For downlink multiple-user (MU) transmission based on non-orthogonal multiple
access (NOMA), the advanced receiver strategy is required to cancel the
inter-user interference, e.g., successive interference cancellation (SIC). The
SIC process can be applicable only when information about the co-scheduled
signal is known at the user terminal (UT) side. In particular, the UT should
know whether the received signal is OMA or NOMA, whether SIC is required or
not, and which modulation orders and power ratios have been used for the
superposed UTs, before decoding the signal. An efficient network, e.g.,
vehicular network, requires that the UTs blindly classify the received signal
and apply a matching receiver strategy to reduce the high-layer signaling
overhead which is essential for high-mobility vehicular networks. In this
paper, we first analyze the performance impact of errors in NOMA signal
classification and address ensuing receiver challenges in practical MU usage
cases. In order to reduce the blind signal classification error rate, we
propose transmission schemes that rotate data symbols or pilots to a specific
phase according to the transmitted signal format. In the case of pilot
rotation, a new signal classification algorithm is also proposed. The
performance improvements by the proposed methods are verified by intensive
simulation results.Comment: 13 pages, 15 figure
Max-Weight Scheduling and Quality-Aware Streaming for Device-to-Device Video Delivery
We propose and analyze centralized and distributed algorithms for
device-to-device video scheduling and streaming. The proposed algorithms
address jointly the problems of device-to-device link scheduling and video
quality adaptation in streaming. Our simulations show that the proposed
algorithms significantly outperform conventional separated approaches that
treat these two problems independently.Comment: 2 pages, 1 figure, 1 tabl
Demo: Light-Weight Programming Language for Blockchain
This demo abstract introduces a new light-weight programming language koa
which is suitable for blockchain system design and implementation. In this
abstract, the basic features of koa are introduced including working system
(with playground), architecture, and virtual machine operations. Rum-time
execution of software implemented by koa will be presented during the session
Markov Decision Policies for Dynamic Video Delivery in Wireless Caching Networks
This paper proposes a video delivery strategy for dynamic streaming services
which maximizes time-average streaming quality under a playback delay
constraint in wireless caching networks. The network where popular videos
encoded by scalable video coding are already stored in randomly distributed
caching nodes is considered under adaptive video streaming concepts, and
distance-based interference management is investigated in this paper. In this
network model, a streaming user makes delay-constrained decisions depending on
stochastic network states: 1) caching node for video delivery, 2) video
quality, and 3) the quantity of video chunks to receive. Since wireless link
activation for video delivery may introduce delays, different timescales for
updating caching node association, video quality adaptation, and chunk amounts
are considered. After associating with a caching node for video delivery, the
streaming user chooses combinations of quality and chunk amounts in the small
timescale. The dynamic decision making process for video quality and chunk
amounts at each slot is modeled using Markov decision process, and the caching
node decision is made based on the framework of Lyapunov optimization. Our
intensive simulations verify that the proposed video delivery algorithm works
reliably and also can control the tradeoff between video quality and playback
latency.Comment: 28 pages, 11 figures, submission to IEEE TW
Blind Signal Classification Analysis and Impact on User Scheduling and Power Allocation in Nonorthogonal Multiple Access
For a massive number of devices, nonorthogonal multiple access (NOMA) has
been recognized as a promising technology for improving the spectral efficiency
compared to orthogonal multiple access (OMA). However, it is difficult for a
base station (BS) to provide all of the information about NOMA signals via a
high layer owing to signaling overhead concerns. This paper studies blind
signal classification, which determines whether or not the received NOMA signal
requires successive interference cancellation (SIC) without a priori signal
information. In this paper, two types of blind signal classification errors are
analyzed: 1) the signal is classified as one that does not require SIC on the
SIC user side and 2) the signal is classified as one for which SIC is necessary
on the non-SIC user side. In addition, we formulate the joint optimization
problem for user scheduling and power allocation, which maximizes the sum-rate
gain of NOMA over OMA with constraints on the maximum classification error
probability and minimum data rate. The proposed algorithm iteratively finds
solutions for user scheduling and power allocation. Simulation results show
that the proposed scheme outperforms existing user scheduling methods.Comment: 30 pages, 12 figure
Multi-Agent Deep Reinforcement Learning for Cooperative Connected Vehicles
Millimeter-wave (mmWave) base station can offer abundant high capacity
channel resources toward connected vehicles so that quality-of-service (QoS) of
them in terms of downlink throughput can be highly improved. The mmWave base
station can operate among existing base stations (e.g., macro-cell base
station) on non-overlapped channels among them and the vehicles can make
decision what base station to associate, and what channel to utilize on
heterogeneous networks. Furthermore, because of the non-omni property of mmWave
communication, the vehicles decide how to align the beam direction toward
mmWave base station to associate with it. However, such joint problem requires
high computational cost, which is NP-hard and has combinatorial features. In
this paper, we solve the problem in 3-tier heterogeneous vehicular network
(HetVNet) with multi-agent deep reinforcement learning (DRL) in a way that
maximizes expected total reward (i.e., downlink throughput) of vehicles. The
multi-agent deep deterministic policy gradient (MADDPG) approach is introduced
to achieve optimal policy in continuous action domain.Comment: 6 pages, 4 figures, conference paper (GLOBECOM 2019
A Personalized Preference Learning Framework for Caching in Mobile Networks
This paper comprehensively studies a content-centric mobile network based on
a preference learning framework, where each mobile user is equipped with a
finite-size cache. We consider a practical scenario where each user requests a
content file according to its own preferences, which is motivated by the
existence of heterogeneity in file preferences among different users. Under our
model, we consider a single-hop-based device-to-device (D2D) content delivery
protocol and characterize the average hit ratio for the following two file
preference cases: the personalized file preferences and the common file
preferences. By assuming that the model parameters such as user activity
levels, user file preferences, and file popularity are unknown and thus need to
be inferred, we present a collaborative filtering (CF)-based approach to learn
these parameters. Then, we reformulate the hit ratio maximization problems into
a submodular function maximization and propose two computationally efficient
algorithms including a greedy approach to efficiently solve the cache
allocation problems. We analyze the computational complexity of each algorithm.
Moreover, we analyze the corresponding level of the approximation that our
greedy algorithm can achieve compared to the optimal solution. Using a
real-world dataset, we demonstrate that the proposed framework employing the
personalized file preferences brings substantial gains over its counterpart for
various system parameters.Comment: 21 pages, 10 figures, 1 table, to appear in the IEEE Transactions on
Mobile Computin
- …