This measure enables the discussion for maximizing throughput in a variable rate system.
Similar to maximizing throughput, in [5 8], simultaneous wireless information and power transfer is studied to maximize the achievable information rate.
Since K is fixed, maximizing throughput in Eq. (4) is equal to maximizing k z LC k,ω), which can be transformed into the following maximum problem: begin{array}{*{20}l} mathop{text{max}}limits_{k,boldsymbol{omega}} qquad & kz_{text{LC}} k,boldsymbol{omega}) textrm{s.t.} qquad &omega_{1}+omega_{2}+omega_{3}=1 &0<K.end{array{array} (10).
The optimal policy that maximizes throughput in an energy harvesting multi-pair relay channel can be found by solving the convex problem in Equation 3 with K=2.
(4) A sensitivity analysis of different parameters and scenarios in order to identify the best routing heuristic, storage assignment and order type that maximizes utilization, minimizes cycle time and maximizes throughput in multi-aisle order picking systems.
Consequently, at each run, different combinations of secondary links coexisting with the primary link are evaluated to find out the best solution, i.e., the combination of secondary links that maximizes throughput in the heterogeneous system.
Figure 5 shows the optimal offline policy that maximizes throughput in a symmetric two-way relay channel with unit channel gains, (E_{0}^{ rm max)} = 7) J, (E_{1,i}^{ rm max)} = 7) J, (B_{1,i}^{ rm max)} = 2~text {kbit}), (bar {B}_{1,i}^{ rm max)} = 1~text {kbit}), i=1,2 and random epoch lengths, l n.
The optimal policy that maximizes throughput in a single-user channel with stochastic data arrivals at the transmitter is the solution of Equation 3 with: L=1 and K=2, (h_{1,2}^{II} = infty ) to merge nodes T 0 and T 1,2 into a receiver node, ({bar B}_{1,1}^{ rm max)} = infty ) to remove the buffer size constraints at the relay, (E_{1,2}^{(n)} = 0), n∈I N to prevent node T 1,2 from transmitting.
Figure 3 shows the optimal offline policy to maximize throughput in a multi-way relay channel with L=2 clusters that each contain K=3 users with unit channel gains, random epoch lengths (l_{n}, E_{0}^{ rm max)} = 5 J, E_{j,i}^{ rm max)} = 5 J,B_{j,i}^{ rm max)} = 10) kbit, and (bar {B}_{j,i}^{ rm max)} = 10) kbit, for j=1,2 and i=1,2,3.
As a special case of the multi-way relay channel, Figure 4 shows the optimal offline policy to maximize throughput in a multi-pair relay channel with L=3 pairs that each contain K=2 users with unit channel gains, random epoch lengths l n, (E_{0}^{ rm max)} = 5) J, (E_{j,i}^{ rm max)} = 5) J, (B_{j,i}^{ rm max)} = 10) kbit, and (bar {B}_{j,i}^{ rm max)} = 10) kbit, for j=1,2,3 and i=1,2.
Accordingly, when we are interested in maximizing throughput by using a variable rate, it is possible that we do not gain by using all the candidate RSs for relaying in a multinode relaying system.
