130,881 research outputs found
Capacity Analysis of LTE-Advanced HetNets with Reduced Power Subframes and Range Expansion
The time domain inter-cell interference coordination techniques specified in
LTE Rel. 10 standard improves the throughput of picocell-edge users by
protecting them from macrocell interference. On the other hand, it also
degrades the aggregate capacity in macrocell because the macro base station
(MBS) does not transmit data during certain subframes known as almost blank
subframes. The MBS data transmission using reduced power subframes was
standardized in LTE Rel. 11, which can improve the capacity in macrocell while
not causing high interference to the nearby picocells. In order to get maximum
benefit from the reduced power subframes, setting the key system parameters,
such as the amount of power reduction, carries critical importance. Using
stochastic geometry, this paper lays down a theoretical foundation for the
performance evaluation of heterogeneous networks with reduced power subframes
and range expansion bias. The analytic expressions for average capacity and 5th
percentile throughput are derived as a function of transmit powers, node
densities, and interference coordination parameters in a heterogeneous network
scenario, and are validated through Monte Carlo simulations. Joint optimization
of range expansion bias, power reduction factor, scheduling thresholds, and
duty cycle of reduced power subframes are performed to study the trade-offs
between aggregate capacity of a cell and fairness among the users. To validate
our analysis, we also compare the stochastic geometry based theoretical results
with the real MBS deployment (in the city of London) and the hexagonal-grid
model. Our analysis shows that with optimum parameter settings, the LTE Rel. 11
with reduced power subframes can provide substantially better performance than
the LTE Rel. 10 with almost blank subframes, in terms of both aggregate
capacity and fairness.Comment: Submitted to EURASIP Journal on Wireless Communications and
Networking (JWCN
Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks
In heterogeneous cellular networks (HCNs), it is desirable to offload mobile
users to small cells, which are typically significantly less congested than the
macrocells. To achieve sufficient load balancing, the offloaded users often
have much lower SINR than they would on the macrocell. This SINR degradation
can be partially alleviated through interference avoidance, for example time or
frequency resource partitioning, whereby the macrocell turns off in some
fraction of such resources. Naturally, the optimal offloading strategy is
tightly coupled with resource partitioning; the optimal amount of which in turn
depends on how many users have been offloaded. In this paper, we propose a
general and tractable framework for modeling and analyzing joint resource
partitioning and offloading in a two-tier cellular network. With it, we are
able to derive the downlink rate distribution over the entire network, and an
optimal strategy for joint resource partitioning and offloading. We show that
load balancing, by itself, is insufficient, and resource partitioning is
required in conjunction with offloading to improve the rate of cell edge users
in co-channel heterogeneous networks
Fulcrum: Flexible Network Coding for Heterogeneous Devices
Producción CientíficaWe introduce Fulcrum, a network coding framework that achieves three seemingly conflicting objectives: 1) to reduce the coding coefficient overhead down to nearly n bits per packet in a generation of n packets; 2) to conduct the network coding using only Galois field GF(2) operations at intermediate nodes if necessary, dramatically reducing computing complexity in the network; and 3) to deliver an end-to-end performance that is close to that of a high-field network coding system for high-end receivers, while simultaneously catering to low-end receivers that decode in GF(2). As a consequence of 1) and 3), Fulcrum has a unique trait missing so far in the network coding literature: providing the network with the flexibility to distribute computational complexity over different devices depending on their current load, network conditions, or energy constraints. At the core of our framework lies the idea of precoding at the sources using an expansion field GF(2 h ), h > 1, to increase the number of dimensions seen by the network. Fulcrum can use any high-field linear code for precoding, e.g., Reed-Solomon or Random Linear Network Coding (RLNC). Our analysis shows that the number of additional dimensions created during precoding controls the trade-off between delay, overhead, and computing complexity. Our implementation and measurements show that Fulcrum achieves similar decoding probabilities as high field RLNC but with encoders and decoders that are an order of magnitude faster.Green Mobile Cloud project (grant DFF-0602-01372B)Colorcast project (grant DFF-0602-02661B)TuneSCode project (grant DFF - 1335-00125)Danish Council for Independent Research (grant DFF-4002-00367)Ministerio de Economía, Industria y Competitividad - Fondo Europeo de Desarrollo Regional (grants MTM2012-36917-C03-03 / MTM2015-65764-C3-2-P / MTM2015-69138-REDT)Agencia Estatal de Investigación - Fondo Social Europeo (grant RYC-2016-20208)Aarhus Universitets Forskningsfond Starting (grant AUFF-2017-FLS-7-1
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