Table 14 Summary of ML-based Admission Control
Ref. | ML Technique | Network | Dataset | Features | Output | Evaluation | |
|---|---|---|---|---|---|---|---|
| Â | Â | Â | Â | Â | Â | Settings | Results |
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 |