Bojovi et al. [451]  propose a multilayer feed-forward Neural Network (NN) radio admission control for LTE networks. Their radio admission control  learns from the past experience how the admission of a new session would affect the QoS of all sessions in the future.
Wang et al. [452] introduce a call admission control (CAC)  for LTE femtocell network.  This the multi-service CAC strategy composes of two parts: subscriber authentication and queuing admission control.

§.§ Congestion Window

Reinforcement learning controllers have been applied to many different decision sequence control problems such as network and flow control.  Q-learning based RL controller is applied to design a TCP algorithm for memory constrained IoTs  [453]. QTCP [454] is another approach to control the TCP flow through Q-learning reinforcement learning. As the network status, QTCP uses its statistics such as: 1-avg_send: the average interval between sending two packets., 2- avg_ack: the average interval between receiving two consecutive ACKs, and 3- avg_rtt: the average RTT.  Three actions are designed in QTCP as Increase (10byte), decrease (-1byte) and no change. They tend to encourage the agent to quickly increase. In  addition, QTCP uses the difference between consecutive utility values as a reward function (Equation <ref>). QTCP uses the Q- learning algorithm to train the agent.

\begin{equation}
Utility = \alpha \times log(throughput) - \delta \times log(RTT)
\label{eq:utility_qttcp}
\end{equation}

where $\alpha$ and $\delta$ are two adjustable weights for throughput and RTT.
Kong et al. designed and implemented RL-TCP [27]. They take the network state as  an array of EWMA of the ACK interarrival time, EWMA of the packet inter-sending time, the ratio of current RTT and the minimum RTT, the slow start threshold, and cwnd size. They design reward function as the changes of utility U (Equation <ref>). The utility U is a function of throughput $t_p$, delay $d = RTT - RTT_{min}$, and packet loss rate p. Besides, discreet action is chosen as cwnd = cwnd + x, where x = -1, 0, +1, +3. They applied SARSA [455] as RL algorithms.

\begin{equation}
U=log(\frac{tp}{B})-\delta_1.log(d) + \delta_2.log(1-p)
\label{eq:utility_RL-TCP}
\end{equation}

where $\delta_1$ and $\delta_2$ are two adjustable weights for delay and packet loss rate. Authors in [456, 457] proposed other different reinforcement learning approaches to control the congestion of high-speed multimedia networks.

Jay et al. [458] applied DRL in Internet congestion control problem to distinguish non-congestion loss from congestion induced loss. Network state is defined as an array of three network statistics and their k step history : 1- latency gradient [416], the derivative of latency with respect to time; 2- latency ratio [415], the ratio of the current MI’s mean latency to minimum observed mean latency of any MI in the connection’s history; and 3- sending ratio, the ratio of packets sent to packets acknowledged by the receiver. In this study, reward is defined as $10 \times throughput - 1000 \times latency - 2000 \times  loss$. Action is selected as a real continuous value to fine control the rate and PPO [443] is used to train the agent.

TCP initial rate control is also a problem that DRL researchers are working to improve. Nie et al. propose  [275]  dynamic TCP initial windows and congestion control  through reinforcement learning. They utilize A3C for the congestion control and input states are $s_t = (Throughput_t, \: RTT_t,\: Loss_t)$, action is continuous space and reward is defined as  $r_t = log(\frac{Throughput_t}{RTT_t})$

§ LESSONS LEARNED AND RESEARCH OPPORTUNITIES

Although network traffic management and control have been substantially evolved to improved the latency and throughput of different networks,
, our studies and many others in the literature indicate that there is no existing single mechanism that optimally works in all situations. Besides, the set of situations are rapidly increasing. Therefore maybe machine learning based mechanisms would increase to cover different situations. Recent DRL algorithms such as imitation learning and meta learning would have more chance to develop in network flow, congestion control and traffic management.

Additionally, more flexible and high speed programmable networks would be growing.

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[]Amir Mirzaeinia

Received his Masters degree in Telecommunications from Isfahan University of Technology. Amir Mirzaeinia is currently a Computer Science PhD candidate in New Mexico institute of Mining and Technology. Amir Mirzaeinia had been working with Mobile Commmunication Company as access network design and otimization senior engineer from June 2006 to August 2015.

Mehdi Mirzaeinia
Biography text here.

Abdelmounaam Rezgui
Biography text here.