2 research outputs found
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
Power and Beam Optimization for Uplink Millimeter-Wave Hotspot Communication Systems
We propose an effective interference management and beamforming mechanism for
uplink communication systems that yields fair allocation of rates. In
particular, we consider a hotspot area of a millimeter-wave (mmWave) access
network consisting of multiple user equipment (UE) in the uplink and multiple
access points (APs) with directional antennas and adjustable beam widths and
directions (beam configurations). This network suffers tremendously from
multi-beam multi-user interference, and, to improve the uplink transmission
performance, we propose a centralized scheme that optimizes the power, the beam
width, the beam direction of the APs, and the UE - AP assignments. This problem
involves both continuous and discrete variables, and it has the following
structure. If we fix all discrete variables, except for those related to the
UE-AP assignment, the resulting optimization problem can be solved optimally.
This property enables us to propose a heuristic based on simulated annealing
(SA) to address the intractable joint optimization problem with all discrete
variables. In more detail, for a fixed configuration of beams, we formulate a
weighted rate allocation problem where each user gets the same portion of its
maximum achievable rate that it would have under non-interfered conditions. We
solve this problem with an iterative fixed point algorithm that optimizes the
power of UEs and the UE - AP assignment in the uplink. This fixed point
algorithm is combined with SA to improve the beam configurations. Theoretical
and numerical results show that the proposed method improves both the UE rates
in the lower percentiles and the overall fairness in the network