Cross-layer optimization problems are formulated to enable both these possibilities in a multi-hop multi-channel cognitive radio network with the objective of maximizing the cumulative time incentive for the SUs.
Generalizing the above, and towards maximizing the cumulative system utility, every cell must transmit at its own maximum feasible power, or in reverse, vector P ¯ var = P ¯ 1, var, …, P ¯ w, var, …, P ¯ | C | − 1, var guarantees maximum total two-tier system cumulative utility.
If we define the communication between each unique participating PU transceiver, i.e., PU T x q −PU R x q, q ∈ Q, as a session, the problem entails solving a multi-session optimization with the objective of maximizing the cumulative time incentive, i.e., ∑ q λ t q.
Algebraic formulation: M a x i m i z e ∑ i, j W e i g h t i, j * X i, j s. t. ∑ j X i, j ≤ 1 (j = 1, 2, …, m ) ∑ i X i, j ≤ 1 (i = 1, 2, …, d ) In the above formulation, we are maximizing the cumulative sum of the edges, with the constraints that no miRNA or disease should be repeated.
The objective is to maximize the cumulative time incentive obtained from all the PUs.
The goal of the access point is to maximize the cumulative throughput or discounted cumulative throughput in the system.
The aim of the HLC is to jointly minimize the cumulative positive costs and maximize the cumulative rewards, which corresponds to minimize the negative costs.
The combination of cases (i) and (a) shows that there exists a p value that maximizes the cumulative gain after n iterations.
In BRR, only the PUs' own band-set M q is available within the SU network while trying to maximize the cumulative time incentive.
In this problem, work groups must be allocated to build nodes and arcs into a network in order to maximize the cumulative weighted flow in the network over a horizon.
Reinforcement learning (RL) plays a key role in the multi-agent domain, as it allows the agents to discover the situation and take actions using trial and error to maximize the cumulative reward as illustrated in Fig. 3.
