85,004 research outputs found
Slow Adaptive OFDMA Systems Through Chance Constrained Programming
Adaptive OFDMA has recently been recognized as a promising technique for
providing high spectral efficiency in future broadband wireless systems. The
research over the last decade on adaptive OFDMA systems has focused on adapting
the allocation of radio resources, such as subcarriers and power, to the
instantaneous channel conditions of all users. However, such "fast" adaptation
requires high computational complexity and excessive signaling overhead. This
hinders the deployment of adaptive OFDMA systems worldwide. This paper proposes
a slow adaptive OFDMA scheme, in which the subcarrier allocation is updated on
a much slower timescale than that of the fluctuation of instantaneous channel
conditions. Meanwhile, the data rate requirements of individual users are
accommodated on the fast timescale with high probability, thereby meeting the
requirements except occasional outage. Such an objective has a natural chance
constrained programming formulation, which is known to be intractable. To
circumvent this difficulty, we formulate safe tractable constraints for the
problem based on recent advances in chance constrained programming. We then
develop a polynomial-time algorithm for computing an optimal solution to the
reformulated problem. Our results show that the proposed slow adaptation scheme
drastically reduces both computational cost and control signaling overhead when
compared with the conventional fast adaptive OFDMA. Our work can be viewed as
an initial attempt to apply the chance constrained programming methodology to
wireless system designs. Given that most wireless systems can tolerate an
occasional dip in the quality of service, we hope that the proposed methodology
will find further applications in wireless communications
DTER: Schedule Optimal RF Energy Request and Harvest for Internet of Things
We propose a new energy harvesting strategy that uses a dedicated energy
source (ES) to optimally replenish energy for radio frequency (RF) energy
harvesting powered Internet of Things. Specifically, we develop a two-step dual
tunnel energy requesting (DTER) strategy that minimizes the energy consumption
on both the energy harvesting device and the ES. Besides the causality and
capacity constraints that are investigated in the existing approaches, DTER
also takes into account the overhead issue and the nonlinear charge
characteristics of an energy storage component to make the proposed strategy
practical. Both offline and online scenarios are considered in the second step
of DTER. To solve the nonlinear optimization problem of the offline scenario,
we convert the design of offline optimal energy requesting problem into a
classic shortest path problem and thus a global optimal solution can be
obtained through dynamic programming (DP) algorithms. The online suboptimal
transmission strategy is developed as well. Simulation study verifies that the
online strategy can achieve almost the same energy efficiency as the global
optimal solution in the long term
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