898 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
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
Interference-Aware Decoupled Cell Association in Device-to-Device based 5G Networks
Cell association in cellular networks is an important aspect that impacts
network capacity and eventually quality of experience. The scope of this work
is to investigate the different and generalized cell association (CAS)
strategies for Device-to-Device (D2D) communications in a cellular network
infrastructure. To realize this, we optimize D2D-based cell association by
using the notion of uplink and downlink decoupling that was proven to offer
significant performance gains. We propose an integer linear programming (ILP)
optimization framework to achieve efficient D2D cell association that minimizes
the interference caused by D2D devices onto cellular communications in the
uplink as well as improve the D2D resource utilization efficiency. Simulation
results based on Vodafone's LTE field trial network in a dense urban scenario
highlight the performance gains and render this proposal a candidate design
approach for future 5G networks.Comment: 5 pages, 5 figures. Accepted in IEEE VTC spring 201
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