248 research outputs found
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
Analysis of LTE-A Heterogeneous Networks with SIR-based Cell Association and Stochastic Geometry
This paper provides an analytical framework to characterize the performance
of Heterogeneous Networks (HetNets), where the positions of base stations and
users are modeled by spatial Poisson Point Processes (stochastic geometry). We
have been able to formally derive outage probability, rate coverage
probability, and mean user bit-rate when a frequency reuse of and a novel
prioritized SIR-based cell association scheme are applied. A simulation
approach has been adopted in order to validate our analytical model;
theoretical results are in good agreement with simulation ones. The results
obtained highlight that the adopted cell association technique allows very low
outage probability and the fulfillment of certain bit-rate requirements by
means of adequate selection of reuse factor and micro cell density. This
analytical model can be adopted by network operators to gain insights on cell
planning. Finally, the performance of our SIR-based cell association scheme has
been validated through comparisons with other schemes in literature.Comment: Paper accepted to appear on the Journal of Communication Networks
(accepted on November 28, 2017); 15 page
A Data-Aided Channel Estimation Scheme for Decoupled Systems in Heterogeneous Networks
Uplink/downlink (UL/DL) decoupling promises more flexible cell association
and higher throughput in heterogeneous networks (HetNets), however, it hampers
the acquisition of DL channel state information (CSI) in time-division-duplex
(TDD) systems due to different base stations (BSs) connected in UL/DL. In this
paper, we propose a novel data-aided (DA) channel estimation scheme to address
this problem by utilizing decoded UL data to exploit CSI from received UL data
signal in decoupled HetNets where a massive multiple-input multiple-output BS
and dense small cell BSs are deployed. We analytically estimate BER performance
of UL decoded data, which are used to derive an approximated normalized mean
square error (NMSE) expression of the DA minimum mean square error (MMSE)
estimator. Compared with the conventional least square (LS) and MMSE, it is
shown that NMSE performances of all estimators are determined by their
signal-to-noise ratio (SNR)-like terms and there is an increment consisting of
UL data power, UL data length and BER values in the SNR-like term of DA method,
which suggests DA method outperforms the conventional ones in any scenarios.
Higher UL data power, longer UL data length and better BER performance lead to
more accurate estimated channels with DA method. Numerical results verify that
the analytical BER and NMSE results are close to the simulated ones and a
remarkable gain in both NMSE and DL rate can be achieved by DA method in
multiple scenarios with different modulations
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