14 research outputs found
Statistical Learning in Automated Troubleshooting: Application to LTE Interference Mitigation
This paper presents a method for automated healing as part of off-line
automated troubleshooting. The method combines statistical learning with
constraint optimization. The automated healing aims at locally optimizing radio
resource management (RRM) or system parameters of cells with poor performance
in an iterative manner. The statistical learning processes the data using
Logistic Regression (LR) to extract closed form (functional) relations between
Key Performance Indicators (KPIs) and Radio Resource Management (RRM)
parameters. These functional relations are then processed by an optimization
engine which proposes new parameter values. The advantage of the proposed
formulation is the small number of iterations required by the automated healing
method to converge, making it suitable for off-line implementation. The
proposed method is applied to heal an Inter-Cell Interference Coordination
(ICIC) process in a 3G Long Term Evolution (LTE) network which is based on
soft-frequency reuse scheme. Numerical simulations illustrate the benefits of
the proposed approach.Comment: IEEE Transactions On Vehicular Technology 2010 IEEE transactions on
vehicular technolog
Joint Scheduling for Multi-Service in Coordinated Multi-Point OFDMA Networks
In this paper, the issues upon user scheduling in the downlink packet transmission for multiple services are addressed for coordinated multi-point (CoMP) OFDMA networks. We consider mixed traffic with voice over IP (VOIP) and best effort (BE) services. In order to improve cell-edge performance and guarantee diverse quality of service (QoS), a utility-based joint scheduling algorithm is proposed, which consists of two steps: ant colony optimization (ACO) based joint user selection and greedy subchannel assignment. We compare the proposed algorithm with the greedy user selection (GUC) based scheme. Via simulation results, we show that 95% of BE users are satsified with average cell-edge data rate greater than 200kbps by using either of the two algorithms. Whereas, our proposed algorithm ensures that more than 95% of VoIP users are satisfied with packet drop ratio less than 2%, compared to 78% by the GUC based algorithm
Graph-Based Radio Resource Management for Vehicular Networks
This paper investigates the resource allocation problem in device-to-device
(D2D)-based vehicular communications, based on slow fading statistics of
channel state information (CSI), to alleviate signaling overhead for reporting
rapidly varying accurate CSI of mobile links. We consider the case when each
vehicle-to-infrastructure (V2I) link shares spectrum with multiple
vehicle-to-vehicle (V2V) links. Leveraging the slow fading statistical CSI of
mobile links, we maximize the sum V2I capacity while guaranteeing the
reliability of all V2V links. We propose a graph-based algorithm that uses
graph partitioning tools to divide highly interfering V2V links into different
clusters before formulating the spectrum sharing problem as a weighted
3-dimensional matching problem, which is then solved through adapting a
high-performance approximation algorithm.Comment: 7 pages; 5 figures; accepted by IEEE ICC 201
Traffic-Driven Spectrum Allocation in Heterogeneous Networks
Next generation cellular networks will be heterogeneous with dense deployment
of small cells in order to deliver high data rate per unit area. Traffic
variations are more pronounced in a small cell, which in turn lead to more
dynamic interference to other cells. It is crucial to adapt radio resource
management to traffic conditions in such a heterogeneous network (HetNet). This
paper studies the optimization of spectrum allocation in HetNets on a
relatively slow timescale based on average traffic and channel conditions
(typically over seconds or minutes). Specifically, in a cluster with base
transceiver stations (BTSs), the optimal partition of the spectrum into
segments is determined, corresponding to all possible spectrum reuse patterns
in the downlink. Each BTS's traffic is modeled using a queue with Poisson
arrivals, the service rate of which is a linear function of the combined
bandwidth of all assigned spectrum segments. With the system average packet
sojourn time as the objective, a convex optimization problem is first
formulated, where it is shown that the optimal allocation divides the spectrum
into at most segments. A second, refined model is then proposed to address
queue interactions due to interference, where the corresponding optimal
allocation problem admits an efficient suboptimal solution. Both allocation
schemes attain the entire throughput region of a given network. Simulation
results show the two schemes perform similarly in the heavy-traffic regime, in
which case they significantly outperform both the orthogonal allocation and the
full-frequency-reuse allocation. The refined allocation shows the best
performance under all traffic conditions.Comment: 13 pages, 11 figures, accepted for publication by JSAC-HC