685 research outputs found
Power Estimation in LTE systems with the General Framework of Standard Interference Mappings
We devise novel techniques to obtain the downlink power inducing a given load
in long-term evolution (LTE) systems, where we define load as the fraction of
resource blocks in the time-frequency grid being requested by users from a
given base station. These techniques are particularly important because
previous studies have proved that the data rate requirement of users can be
satisfied with lower transmit energy if we allow the load to increase. Those
studies have also shown that obtaining the power assignment from a desired load
profile can be posed as a fixed point problem involving standard interference
mappings, but so far the mappings have not been obtained explicitly. One of our
main contributions in this study is to close this gap. We derive an
interference mapping having as its fixed point the power assignment inducing a
desired load, assuming that such an assignment exists. Having this mapping in
closed form, we simplify the proof of the aforementioned known results, and we
also devise novel iterative algorithms for power computation that have many
numerical advantages over previous methods.Comment: IEEE Global SIP 201
Spectral radii of asymptotic mappings and the convergence speed of the standard fixed point algorithm
Important problems in wireless networks can often be solved by computing
fixed points of standard or contractive interference mappings, and the
conventional fixed point algorithm is widely used for this purpose. Knowing
that the mapping used in the algorithm is not only standard but also
contractive (or only contractive) is valuable information because we obtain a
guarantee of geometric convergence rate, and the rate is related to a property
of the mapping called modulus of contraction. To date, contractive mappings and
their moduli of contraction have been identified with case-by-case approaches
that can be difficult to generalize. To address this limitation of existing
approaches, we show in this study that the spectral radii of asymptotic
mappings can be used to identify an important subclass of contractive mappings
and also to estimate their moduli of contraction. In addition, if the fixed
point algorithm is applied to compute fixed points of positive concave
mappings, we show that the spectral radii of asymptotic mappings provide us
with simple lower bounds for the estimation error of the iterates. An immediate
application of this result proves that a known algorithm for load estimation in
wireless networks becomes slower with increasing traffic.Comment: Paper accepted for presentation at ICASSP 201
A robust machine learning method for cell-load approximation in wireless networks
We propose a learning algorithm for cell-load approximation in wireless
networks. The proposed algorithm is robust in the sense that it is designed to
cope with the uncertainty arising from a small number of training samples. This
scenario is highly relevant in wireless networks where training has to be
performed on short time scales because of a fast time-varying communication
environment. The first part of this work studies the set of feasible rates and
shows that this set is compact. We then prove that the mapping relating a
feasible rate vector to the unique fixed point of the non-linear cell-load
mapping is monotone and uniformly continuous. Utilizing these properties, we
apply an approximation framework that achieves the best worst-case performance.
Furthermore, the approximation preserves the monotonicity and continuity
properties. Simulations show that the proposed method exhibits better
robustness and accuracy for small training sets in comparison with standard
approximation techniques for multivariate data.Comment: Shorter version accepted at ICASSP 201
The role of asymptotic functions in network optimization and feasibility studies
Solutions to network optimization problems have greatly benefited from
developments in nonlinear analysis, and, in particular, from developments in
convex optimization. A key concept that has made convex and nonconvex analysis
an important tool in science and engineering is the notion of asymptotic
function, which is often hidden in many influential studies on nonlinear
analysis and related fields. Therefore, we can also expect that asymptotic
functions are deeply connected to many results in the wireless domain, even
though they are rarely mentioned in the wireless literature. In this study, we
show connections of this type. By doing so, we explain many properties of
centralized and distributed solutions to wireless resource allocation problems
within a unified framework, and we also generalize and unify existing
approaches to feasibility analysis of network designs. In particular, we show
sufficient and necessary conditions for mappings widely used in wireless
communication problems (more precisely, the class of standard interference
mappings) to have a fixed point. Furthermore, we derive fundamental bounds on
the utility and the energy efficiency that can be achieved by solving a large
family of max-min utility optimization problems in wireless networks.Comment: GlobalSIP 2017 (to appear
Deep Reinforcement Learning for Resource Allocation in V2V Communications
In this article, we develop a decentralized resource allocation mechanism for
vehicle-to-vehicle (V2V) communication systems based on deep reinforcement
learning. Each V2V link is considered as an agent, making its own decisions to
find optimal sub-band and power level for transmission. Since the proposed
method is decentralized, the global information is not required for each agent
to make its decisions, hence the transmission overhead is small. From the
simulation results, each agent can learn how to satisfy the V2V constraints
while minimizing the interference to vehicle-to-infrastructure (V2I)
communications
- …