Tensor models incorporating constraints (sparsity; non‐negativity; smoothness; symmetry; column orthonormality of factor matrices; Hankel, Toeplitz, and Vandermonde structured matrix factors; allocation constraints...) have been the object of intensive works, during the last years.
As a result, the calculation of (X n )∗ can be cast as finding the maximal element in A n. Once A n is determined by solving (K + 1)M given in (16), the optimal binary assignment matrix and allocation power matrix ( X, P s n ) can be determined immediately.
Therefore, the choice of transmit beamformer matrices, power allocation and scheduling (i.e., the selection of the user to be served in each subchannel) strategies should be based on very limited knowledge about the channel conditions and easily be performed cooperatively.
After obtaining the subchannel allocation matrix U and the power assignment matrix P, calculate the sum rate R using R = ∑ k = 1 K R k = ∑ k = 1 K ∑ s ∈ Ω k r k, s. 4.
By dot-multiplying H K by the allocation matrix then adding thermal noise, the detected signal sample matrix Y K is obtained.
For these applications, the matrices Φ(n) are formed with 0's and 1's, and they can be interpreted as allocation matrices used for allocating some resources r n to the output mode‐ (N1+1).
In turn, we skillfully design flexible length DNA strands to represent elements of the allocation matrix, take appropriate biological experiment operations and get solutions of the task scheduling problem in proper length range with less than O(n2) time complexity.
For the n-queens problem, we reasonably design flexible length DNA strands representing elements of the allocation matrix, take appropriate biologic manipulations and get the solutions of the n-queens problem in proper length and O(n2) time complexity.
Link Rate Allocation Matrix.
