7 research outputs found
On optimizing power allocation for reliable communication over fading channels with uninformed transmitter
We investigate energy efficient packet scheduling
and power allocation problem for the services which require
reliable communication to guarantee a certain quality of experience
(QoE). We establish links between average transmit power
and reliability of data transfer, which depends on both average
amount of data transfer and short term rate guarantees. We
consider a slow-fading point-to-point channel without channel
state information at the transmitter side (CSIT). In the absence
of CSIT, the slow fading channel has an outage probability
associated with every transmit power. As a function of data
loss tolerance parameters, and minimum rate and peak power
constraints, we formulate an optimization problem that adapts
rate and power to minimize the average transmit power for
the user equipment (UE). Then, a relaxed optimization problem
is formulated where transmission rate is assumed to be fixed
for each packet transmission. We use Markov chain to model
constraints of the optimization problem. The corresponding
problem is not convex for both of the formulated problems, therefore
a stochastic optimization technique, namely the simulated
annealing algorithm, is used to solve them. The numerical results
quantify the effect of various system parameters on average
transmit power and show significant energy savings when the
service has less stringent requirements on timely and reliable
communication
Delay-Power-Rate-Distortion Optimization of Video Representations for Dynamic Adaptive Streaming
Dynamic adaptive streaming addresses user heterogeneity by providing multiple encoded representations at different rates and/or resolutions for the same video content. For delay-sensitive applications, such as live streaming, there is however a stringent requirement on the encoding delay, and usually the encoding power (or rate) budget is also limited by the computational (or storage) capacity of the server. It is therefore important, yet challenging, to optimally select the source coding parameters for each encoded representation in order to minimize the resource consumption while maintaining a high quality of experience for the users. To address this, we propose an optimization framework with an optimal representation selection problem for delay, power, and rate constrained adaptive video streaming. Then, by the optimal selection of source coding parameters for each selected representation, we maximize the overall expected user satisfaction, subject not only to the encoding rate constraint, but also to the delay and power constraints at the server. We formulate the proposed optimization problem as an integer linear program formulation to provide the performance upper bound, and as a submodular maximization problem with two knapsack constraints to develop a practically feasible algorithm. Simulation results show that the proposed weighted rate and power cost benefit greedy algorithm is able to achieve a near-optimal performance with very low time complexity. In addition, it can strike the best tradeoff both between the rate and power cost, and between the algorithm's performance and the delay requirements proposed by delay sensitive applications
Intelligence in 5G networks
Over the past decade, Artificial Intelligence (AI) has become an important part of our daily lives; however, its application to communication networks has been partial and unsystematic, with uncoordinated efforts that often conflict with each other. Providing a framework to integrate the existing studies and to actually build an intelligent network is a top research priority. In fact, one of the objectives of 5G is to manage all communications under a single overarching paradigm, and the staggering complexity of this task is beyond the scope of human-designed algorithms and control systems.
This thesis presents an overview of all the necessary components to integrate intelligence in this complex environment, with a user-centric perspective: network optimization should always have the end goal of improving the experience of the user. Each step is described with the aid of one or more case studies, involving various network functions and elements.
Starting from perception and prediction of the surrounding environment, the first core requirements of an intelligent system, this work gradually builds its way up to showing examples of fully autonomous network agents which learn from experience without any human intervention or pre-defined behavior, discussing the possible application of each aspect of intelligence in future networks