38,879 research outputs found
Optimisation of stochastic networks with blocking: a functional-form approach
This paper introduces a class of stochastic networks with blocking, motivated
by applications arising in cellular network planning, mobile cloud computing,
and spare parts supply chains. Blocking results in lost revenue due to
customers or jobs being permanently removed from the system. We are interested
in striking a balance between mitigating blocking by increasing service
capacity, and maintaining low costs for service capacity. This problem is
further complicated by the stochastic nature of the system. Owing to the
complexity of the system there are no analytical results available that
formulate and solve the relevant optimization problem in closed form.
Traditional simulation-based methods may work well for small instances, but the
associated computational costs are prohibitive for networks of realistic size.
We propose a hybrid functional-form based approach for finding the optimal
resource allocation, combining the speed of an analytical approach with the
accuracy of simulation-based optimisation. The key insight is to replace the
computationally expensive gradient estimation in simulation optimisation with a
closed-form analytical approximation that is calibrated using a single
simulation run. We develop two implementations of this approach and conduct
extensive computational experiments on complex examples to show that it is
capable of substantially improving system performance. We also provide evidence
that our approach has substantially lower computational costs compared to
stochastic approximation
Joint Subcarrier and Power Allocation in NOMA: Optimal and Approximate Algorithms
Non-orthogonal multiple access (NOMA) is a promising technology to increase
the spectral efficiency and enable massive connectivity in 5G and future
wireless networks. In contrast to orthogonal schemes, such as OFDMA, NOMA
multiplexes several users on the same frequency and time resource. Joint
subcarrier and power allocation problems (JSPA) in NOMA are NP-hard to solve in
general. In this family of problems, we consider the weighted sum-rate (WSR)
objective function as it can achieve various tradeoffs between sum-rate
performance and user fairness. Because of JSPA's intractability, a common
approach in the literature is to solve separately the power control and
subcarrier allocation (also known as user selection) problems, therefore
achieving sub-optimal result. In this work, we first improve the computational
complexity of existing single-carrier power control and user selection schemes.
These improved procedures are then used as basic building blocks to design new
algorithms, namely Opt-JSPA, -JSPA and Grad-JSPA. Opt-JSPA
computes an optimal solution with lower complexity than current optimal schemes
in the literature. It can be used as a benchmark for optimal WSR performance in
simulations. However, its pseudo-polynomial time complexity remains impractical
for real-world systems with low latency requirements. To further reduce the
complexity, we propose a fully polynomial-time approximation scheme called
-JSPA. Since, no approximation has been studied in the literature,
-JSPA stands out by allowing to control a tight trade-off between
performance guarantee and complexity. Finally, Grad-JSPA is a heuristic based
on gradient descent. Numerical results show that it achieves near-optimal WSR
with much lower complexity than existing optimal methods
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