Review of Adaptive Cell Selection Techniques in LTE-Advanced Heterogeneous Networks

Table 1

A comparative summary of the adaptive cell association schemes.

Proposed scheme	Algorithm based on	Cell load metric representation	Inputs to cell association algorithm	Cell-edge UE throughput gain	Average UE throughput gain	Advantages	Drawbacks
Gu et al., 2013 [6]	Practical adaptation based on cell-edge UE capacity	Number of pieces of UE based RBUR	Network acquired	Nearly optimal with 70% gain over static at 6 dB	Not provided	Simplicity and immune to feedback delays and errors	No criteria for selecting the cell load threshold
Gu et al., 2014 [7]	Practical adaptation based on predicted overall capacity	CBR traffic based RBUR	Network acquired based on system capacity prediction	Not provided	−9.4% gain compared to the optimal	Can achieve nearly optimal performance in all cell load conditions	The algorithm updating mechanism is based on trial and error
Kudo and Ohtsuki, 2013 [8]	Q-learning based on the amount of outage reduction	Set of ratio of RBs and UE distribution	Distributed: pieces of UE learn their optimal bias values	61% gain over no learning scheme at 20% PRB	200% gain compared to optimal at 40% PRB	Pieces of UE learn its bias values from past experience to maximize network throughput	Long convergence time makes it not suitable for real systems. Effect of UE mobility not considered.
Koizumi and Higuchi, 2013 [9]	Simple adaptation based on expected minimum average UE throughput	A combination of MeNB index and resource index that maximizes UE throughput	Decentralized: no need for coordination among MeNBs	1.3-fold gain compared to the no ICIC case	1.3-fold compared to the no eICIC case	Faster convergence, further enhanced performance with eICIC, and adapting according to the variation of UE distribution	Large overheads due to feedback.; Ping-pong handover problem with multiple pieces of UE
Kikuchi and Otsuka, 2013 [10]	Adaptive control CRE based on SINR	Ratio of the number of pieces of UE connected to PeNBs and MeNBs	Centralized based on the feedback from UE	Near-optimal performance	Slightly above-optimal performance: 3.3 Mbps compared to optimal with 3.2 Mbps	Simple algorithm. It has the ability to solve the trade-off between cell-edge UE throughput and the average UE throughput	Delay due to feedback from UE. Number of pieces of UE cannot accurately estimate the cell’s load condition