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Table 14 Summary of ML-based Admission Control

From: A comprehensive survey on machine learning for networking: evolution, applications and research opportunities

Ref. ML Technique Network Dataset Features Output Evaluation
       Settings Results
Hiramatsu [189, 190] Supervised: NN ATM Simulation · Link capacity ·Observed call generation rate · Call loss rate 2-10-1a Improved call loss rate
Cheng and Chang [95] Supervised: MLP-NN ATM Simulation · Congestion-status · Cell-loss probability · Peak bitrate · Average bitrate · Mean peak-rate duration Acceptance or rejection 30-30-1a 20% system utilization improvement over [189]
Piamrat et al. [359] Supervised: · RandNN Wireless Videos (distorted) generated by streaming application Codec, bandwidth, loss, delay, and jitter · MOS N/A N/A
Baldo et al. [36] Supervised: · MLP-NN Wireless LAN ns-3 simulator and testbed Link load and frame loss Service quality 9-10-1a 98.5% (offline) 92% (online)
Liu et al. [281] Supervised: · MLP-NN Cellular (CDMA) Simulation of cellular networks · Network environment · User behavior · Call class · Action GoS 5-10-1a Performs better than the static algorithms
Bojovic et al. [66] Supervised: · MLP-NN Cellular (LTE) ns-3 network simulator · Application throughput · Average packet error rate · Average size of packet data unit QoS fulfillment ratio N/A Accuracy: 86%
Vassis et al. [452] Supervised: · MLP · Probabilistic RBFNN · LVQ-NN · HNN ·SVM network Ad hoc networks Pamvotis WLAN simulator · Network throughput · Packet generation rate Average packet delays N/A Correctness: ·77% - 88% (Probabilistic RBFNN) Others do not converge
Ahn et al. [8] Un-Supervised: · HNN Wireless network Simulation · Usable QoS levels QoS assignment matrix for each connection N × M, where N and M are the number of connections and the number of QoS levels Minimized connection blocking and dropping probabilities
Blenk et al. [63] Supervised: · RNN VN Simulation · Different graph features Acceptance or rejection of VN 18 different Recurrent NNs 89% - 98%
Bojovic et al. [67] Supervised: · NN ·BN Cellular (LTE) network ns-3 simulator · Channel quality indicator R-factor Two layers with Number of nodes in the hidden layer: 10 and 20 Accuracy: 98% (BN)
Quer et al. [372] Supervised: · BN Wireless LAN ns-3 simulator · Link Layer conditions Voice call quality Nodes: 9, Links: 14 Accuracy: 95%
Mignanti et al. [311] RL: · Q-learning NGN OMNET simulator States · Environment state based on number of active connections of each traffic class Action · Accept or reject (ε-greedy) Not provided 10%-30% better than a greedy approach
Wang et al. [458] RL: · Q-learning LTE femtocell networks Simulation States · Queue length of handoff and new calls Action · Maintain, degrade, or upgrade proportion levels RRl ×3, where l is QoS proportion levels Reduction in blocking probability
Tong et al. [446] RL: · Q-learning Multimedia networks Simulation States · The number ongoing calls of each class · Call arrival or termination event · QoS and capacity constraints Action · Accept or reject or no action K × 2, where K is number of constraints Improvement in rejection rates
Marbach et al. [295] RL: · TD(0) Integrated service networks Simulation States · The number active calls of each class · Routing path of each active call Action · Accept with a route or reject States 1.4 ×10256 2.2% improvement in rewards