104,157 research outputs found
Optimal decentralized spectral resource allocation for OFDMA downlink of femto networks via adaptive gradient vector step size approach
For the orthogonal frequency division multiple access (OFDMA) downlink of a femto network, the resource allocation scheme would aim to maximize the area spectral efficiency (ASE) subject to constraints on the radio resources per transmission interval accessible by each femtocell. An optimal resource allocation scheme for completely decentralized femtocell deployments leads to a nonlinear optimization problem because the cost function of the optimization problem is nonlinear. In this paper, an adaptive gradient vector step size approach is proposed for finding the optimal solution of the optimization problem. Computer numerical simulation results show that our proposed method is more efficient than existing exhaustive search methods
Notes on Alpha Stream Optimization
In these notes we discuss investment allocation to multiple alpha streams
traded on the same execution platform, including when trades are crossed
internally resulting in turnover reduction. We discuss approaches to alpha
weight optimization where one maximizes P&L subject to bounds on volatility (or
Sharpe ratio). The presence of negative alpha weights, which are allowed when
alpha streams are traded on the same execution platform, complicates the
optimization problem. By using factor model approach to alpha covariance
matrix, the original optimization problem can be viewed as a 1-dimensional root
searching problem plus an optimization problem that requires a finite number of
iterations. We discuss this approach without costs and with linear costs, and
also with nonlinear costs in a certain approximation, which makes the
allocation problem tractable without forgoing nonlinear portfolio capacity
bound effects.Comment: 42 pages; clarifying remarks added, minor misprints corrected; to
appear in The Journal of Investment Strategie
Distributed Interference-Aware Energy-Efficient Resource Allocation for Device-to-Device Communications Underlaying Cellular Networks
The introduction of device-to-device (D2D) into cellular networks poses many
new challenges in the resource allocation design due to the co-channel
interference caused by spectrum reuse and limited battery life of user
equipments (UEs). In this paper, we propose a distributed interference-aware
energy-efficient resource allocation algorithm to maximize each UE's energy
efficiency (EE) subject to its specific quality of service (QoS) and maximum
transmission power constraints. We model the resource allocation problem as a
noncooperative game, in which each player is self-interested and wants to
maximize its own EE. The formulated EE maximization problem is a non-convex
problem and is transformed into a convex optimization problem by exploiting the
properties of the nonlinear fractional programming. An iterative optimization
algorithm is proposed and verified through computer simulations.Comment: 6 pages, 3 figures, IEEE GLOBECOM 201
Resource Allocation for Downlink Multi-Cell OFDMA Cognitive Radio Network Using Hungarian Method
This paper considers the problem of resource allocation for downlink part of an OFDM-based multi-cell cognitive radio network which consists of multiple secondary transmitters and receivers communicating simultaneously in the presence of multiple primary users. We present a new framework to maximize the total data throughput of secondary users by means of subchannel assignment, while ensuring interference leakage to PUs is below a threshold. In this framework, we first formulate the resource allocation problem as a nonlinear and non-convex optimization problem. Then we represent the problem as a maximum weighted matching in a bipartite graph and propose an iterative algorithm based on Hungarian method to solve it. The present contribution develops an efficient subchannel allocation algorithm that assigns subchannels to the secondary users without the perfect knowledge of fading channel gain between cognitive radio transmitter and primary receivers. The performance of the proposed subcarrier allocation algorithm is compared with a blind subchannel allocation as well as another scheme with the perfect knowledge of channel-state information. Simulation results reveal that a significant performance advantage can still be realized, even if the optimization at the secondary network is based on imperfect network information
Combining Alpha Streams with Costs
We discuss investment allocation to multiple alpha streams traded on the same
execution platform with internal crossing of trades and point out differences
with allocating investment when alpha streams are traded on separate execution
platforms with no crossing. First, in the latter case allocation weights are
non-negative, while in the former case they can be negative. Second, the
effects of both linear and nonlinear (impact) costs are different in these two
cases due to turnover reduction when the trades are crossed. Third, the
turnover reduction depends on the universe of traded alpha streams, so if some
alpha streams have zero allocations, turnover reduction needs to be recomputed,
hence an iterative procedure. We discuss an algorithm for finding allocation
weights with crossing and linear costs. We also discuss a simple approximation
when nonlinear costs are added, making the allocation problem tractable while
still capturing nonlinear portfolio capacity bound effects. We also define
"regression with costs" as a limit of optimization with costs, useful in
often-occurring cases with singular alpha covariance matrix.Comment: 21 pages; minor misprints corrected; to appear in The Journal of Ris
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