1,981 research outputs found
Traffic Driven Resource Allocation in Heterogenous Wireless Networks
Most work on wireless network resource allocation use physical layer
performance such as sum rate and outage probability as the figure of merit.
These metrics may not reflect the true user QoS in future heterogenous networks
(HetNets) with many small cells, due to large traffic variations in overlapping
cells with complicated interference conditions. This paper studies the spectrum
allocation problem in HetNets using the average packet sojourn time as the
performance metric. To be specific, in a HetNet with base terminal stations
(BTS's), we determine the optimal partition of the spectrum into possible
spectrum sharing combinations. We use an interactive queueing model to
characterize the flow level performance, where the service rates are decided by
the spectrum partition. The spectrum allocation problem is formulated using a
conservative approximation, which makes the optimization problem convex. We
prove that in the optimal solution the spectrum is divided into at most
pieces. A numerical algorithm is provided to solve the spectrum allocation
problem on a slow timescale with aggregate traffic and service information.
Simulation results show that the proposed solution achieves significant gains
compared to both orthogonal and full spectrum reuse allocations with moderate
to heavy traffic.Comment: 6 pages, 5 figures IEEE GLOBECOM 2014 (accepted for publication
Joint Resource Allocation for eICIC in Heterogeneous Networks
Interference coordination between high-power macros and low-power picos
deeply impacts the performance of heterogeneous networks (HetNets). It should
deal with three challenges: user association with macros and picos, the amount
of almost blank subframe (ABS) that macros should reserve for picos, and
resource block (RB) allocation strategy in each eNB. We formulate the three
issues jointly for sum weighted logarithmic utility maximization while
maintaining proportional fairness of users. A class of distributed algorithms
are developed to solve the joint optimization problem. Our framework can be
deployed for enhanced inter-cell interference coordination (eICIC) in existing
LTE-A protocols. Extensive evaluation are performed to verify the effectiveness
of our algorithms.Comment: Accepted by Globecom 201
Green Networking in Cellular HetNets: A Unified Radio Resource Management Framework with Base Station ON/OFF Switching
In this paper, the problem of energy efficiency in cellular heterogeneous
networks (HetNets) is investigated using radio resource and power management
combined with the base station (BS) ON/OFF switching. The objective is to
minimize the total power consumption of the network while satisfying the
quality of service (QoS) requirements of each connected user. We consider the
case of co-existing macrocell BS, small cell BSs, and private femtocell access
points (FAPs). Three different network scenarios are investigated, depending on
the status of the FAPs, i.e., HetNets without FAPs, HetNets with closed FAPs,
and HetNets with semi-closed FAPs. A unified framework is proposed to
simultaneously allocate spectrum resources to users in an energy efficient
manner and switch off redundant small cell BSs. The high complexity dual
decomposition technique is employed to achieve optimal solutions for the
problem. A low complexity iterative algorithm is also proposed and its
performances are compared to those of the optimal technique. The particularly
interesting case of semi-closed FAPs, in which the FAPs accept to serve
external users, achieves the highest energy efficiency due to increased degrees
of freedom. In this paper, a cooperation scheme between FAPs and mobile
operator is also investigated. The incentives for FAPs, e.g., renewable energy
sharing and roaming prices, enabling cooperation are discussed to be considered
as a useful guideline for inter-operator agreements.Comment: 15 pages, 9 Figures, IEEE Transactions on Vehicular Technology 201
User Transmit Power Minimization through Uplink Resource Allocation and User Association in HetNets
The popularity of cellular internet of things (IoT) is increasing day by day
and billions of IoT devices will be connected to the internet. Many of these
devices have limited battery life with constraints on transmit power. High user
power consumption in cellular networks restricts the deployment of many IoT
devices in 5G. To enable the inclusion of these devices, 5G should be
supplemented with strategies and schemes to reduce user power consumption.
Therefore, we present a novel joint uplink user association and resource
allocation scheme for minimizing user transmit power while meeting the quality
of service. We analyze our scheme for two-tier heterogeneous network (HetNet)
and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms
compared to 20 dBm in state-of-the-art Max reference signal received power
(RSRP) and channel individual offset (CIO) based association schemes
On/Off Macrocells and Load Balancing in Heterogeneous Cellular Networks
The rate distribution in heterogeneous networks (HetNets) greatly benefits
from load balancing, by which mobile users are pushed onto lightly-loaded small
cells despite the resulting loss in SINR. This offloading can be made more
aggressive and robust if the macrocells leave a fraction of time/frequency
resource blank, which reduces the interference to the offloaded users. We
investigate the joint optimization of this technique - referred to in 3GPP as
enhanced intercell interference coordination (eICIC) via almost blank subframes
(ABSs) - with offloading in this paper. Although the joint cell association and
blank resource (BR) problem is nominally combinatorial, by allowing users to
associate with multiple base stations (BSs), the problem becomes convex, and
upper bounds the performance versus a binary association. We show both
theoretically and through simulation that the optimal solution of the relaxed
problem still results in an association that is mostly binary. The optimal
association differs significantly when the macrocell is on or off; in
particular the offloading can be much more aggressive when the resource is left
blank by macro BSs. Further, we observe that jointly optimizing the offloading
with BR is important. The rate gain for cell edge users (the worst 3-10%) is
very large - on the order of 5-10x - versus a naive association strategy
without macrocell blanking
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