The weights of users are assumed to be equal, i.e., u1=u2=u3=u4=0.25.
Then, the beam weight of user k is determined by w k =f k /∥f k ∥.
In this work, the weight of user-1 is fixed to u1=1.
The results show that the MAS is capable of fully balancing the weights of user's upper limb and the device during the full range of motion.
Thus, given users u and v, T ̂ u T ̂ v = H u H v, then the PF weight of user u in time slot ν can be defined as F ν u = c y ν u H u. The transition probability matrix can be constructed as in the max-rate example.
It follows that the beam weights of user k based on MRT and ZFBF are obtained by ({mathbf {w}_{k}^{text {MRT}} = mathbf {f}_{k}^{text {MRT}}/left | {mathbf {f}_{k}^{text {MRT}}} right |}) and ({mathbf {w}_{k}^{text {ZF}} = mathbf {f}_{k}^{text {ZF}}/left | {mathbf {f}_{k}^{text {ZF}}} right |}), respectively.
In addition, by changing the weights of different users, we can differentiate users' throughput performance which would be useful in provisioning wireless networks with nonhomogeneous services.
Namely, it is known that users should be decoded on the MAC in the reverse order of the size of the weights of the users, such that the user with the largest weight is decoded last [24, 25].
where D u = h ̃ ( u ) h ̃ ( u ) H. The autocorrelation matrix D u is of rank one, with only one non-zero eigenvalue ξ, the optimal weight vector of user u is given by [26], w u, o p t = 1 ξ h ̃ ( u ) (10).
However, I am concerned, for example, that the current designs of these products might not take fully into consideration the different weights of different users, potentially leading to the units speeding up or lurching in a manner that a user would not have reason to anticipate, especially a first-time user.
The weights of the users are for and for.
