13,728 research outputs found
Scheduling Policies in Time and Frequency Domains for LTE Downlink Channel: A Performance Comparison
A key feature of the Long-Term Evolution (LTE) system is that the packet scheduler can make use of the channel quality information (CQI), which is periodically reported by user equipment either in an aggregate form for the whole downlink channel or distinguished for each available subchannel. This mechanism allows for wide discretion in resource allocation, thus promoting the flourishing of several scheduling algorithms, with different purposes. It is therefore of great interest to compare the performance of such algorithms under different scenarios. Here, we carry out a thorough performance analysis of different scheduling algorithms for saturated User Datagram Protocol (UDP) and Transmission Control Protocol (TCP) traffic sources, as well as consider both the time- and frequency-domain versions of the schedulers and for both flat and frequency-selective channels. The analysis makes it possible to appreciate the difference among the scheduling algorithms and to assess the performance gain, in terms of cell capacity, users' fairness, and packet service time, obtained by exploiting the richer, but heavier, information carried by subchannel CQI. An important part of this analysis is a throughput guarantee scheduler, which we propose in this paper. The analysis reveals that the proposed scheduler provides a good tradeoff between cell capacity and fairness both for TCP and UDP traffic sources
Predicting a User's Next Cell With Supervised Learning Based on Channel States
Knowing a user's next cell allows more efficient resource allocation and
enables new location-aware services. To anticipate the cell a user will
hand-over to, we introduce a new machine learning based prediction system.
Therein, we formulate the prediction as a classification problem based on
information that is readily available in cellular networks. Using only Channel
State Information (CSI) and handover history, we perform classification by
embedding Support Vector Machines (SVMs) into an efficient pre-processing
structure. Simulation results from a Manhattan Grid scenario and from a
realistic radio map of downtown Frankfurt show that our system provides timely
prediction at high accuracy.Comment: The 14th IEEE International Workshop on Signal Processing Advances
for Wireless Communications (SPAWC), Darmstadt : Germany (2013
Study on Scheduling Techniques for Ultra Dense Small Cell Networks
The most promising approach to enhance network capacity for the next
generation of wireless cellular networks (5G) is densification, which benefits
from the extensive spatial reuse of the spectrum and the reduced distance
between transmitters and receivers. In this paper, we examine the performance
of different schedulers in ultra dense small cell deployments. Due to the
stronger line of sight (LOS) at low inter-site distances (ISDs), we discuss
that the Rician fading channel model is more suitable to study network
performance than the Rayleigh one, and model the Rician K factor as a function
of distance between the user equipment (UE) and its serving base station (BS).
We also construct a cross-correlation shadowing model that takes into account
the ISD, and finally investigate potential multi-user diversity gains in ultra
dense small cell deployments by comparing the performances of proportional fair
(PF) and round robin (RR) schedulers. Our study shows that as network becomes
denser, the LOS component starts to dominate the path loss model which
significantly increases the interference. Simulation results also show that
multi-user diversity is considerably reduced at low ISDs, and thus the PF
scheduling gain over the RR one is small, around 10% in terms of cell
throughput. As a result, the RR scheduling may be preferred for dense small
cell deployments due to its simplicity. Despite both the interference
aggravation as well as the multi-user diversity loss, network densification is
still worth it from a capacity view point.Comment: 6 pages, 7 figures, Accepted to IEEE VTC-Fall 2015 Bosto
Unified and Distributed QoS-Driven Cell Association Algorithms in Heterogeneous Networks
This paper addresses the cell association problem in the downlink of a
multi-tier heterogeneous network (HetNet), where base stations (BSs) have
finite number of resource blocks (RBs) available to distribute among their
associated users. Two problems are defined and treated in this paper: sum
utility of long term rate maximization with long term rate quality of service
(QoS) constraints, and global outage probability minimization with outage QoS
constraints. The first problem is well-suited for low mobility environments,
while the second problem provides a framework to deal with environments with
fast fading. The defined optimization problems in this paper are solved in two
phases: cell association phase followed by the optional RB distribution phase.
We show that the cell association phase of both problems have the same
structure. Based on this similarity, we propose a unified distributed algorithm
with low levels of message passing to for the cell association phase. This
distributed algorithm is derived by relaxing the association constraints and
using Lagrange dual decomposition method. In the RB distribution phase, the
remaining RBs after the cell association phase are distributed among the users.
Simulation results show the superiority of our distributed cell association
scheme compared to schemes that are based on maximum signal to interference
plus noise ratio (SINR)
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
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