a specified user combination is (9.11a) s.t. 0,k (9.11b) where p = (pk, Vk E IC) is the power vector, 4x(p)= and (p) = for all k, the problem then can be re-formulated as max [[(2,)" max (9.12a) (9.12b) (9.12c) Notice that an increasing function for all positive ek, where e is the col- lection of all ek. Besides, for two vectors e and em, if e >em where > means element-wise greater than, we have T(1) > T(m). Clearly, the optimal solution occurs where and Pk in the feasible set. This can be regarded as a multiplicative linear fractional programming (MLFP) problem, where K linear equations are formulated as below: (9.13) Notice that (9.13) contains random channel gain components; hence, those K linear equations are independent with probability 1, which suggests a unique optimal power allo- cation p*. To solve (9.13) efficiently, however, requires constructing of feasible polyblock and sequentially reduce its size, see [149] for the detailed algorithm. 9.6 Numerical Results Numerical Results In this section, we present the experimental FL update results for both TDMA-based origi- nal FedAvg [128] and NOMA compression-based FedAvg schemes by using diverse learning models and federated datasets. The channel parameters are given as follows. The uplink bandwidth is B = 5 MHz, path loss exponent a = 3, additive noise power density o2 = -174 dBm/Hz. The number of the selected users is set as two values, K = 10 and K = 20, for dif- ferent runs. All the users are randomly distributed in a disk region with a radius 500m and they have the same transmission power Pk = 0.1 watts. Uplink transmission time slot is 0.5 S. Downlink transmission from the PS to all the users uses broadcast and is uncompressed. The transmission time is calculated as = max , where P is the number of the Balog2(1+Payk) total updated parameters, Bd is the downlink bandwidth at 10 MHz. Pd = 2 watts is the PS power, Yk is the SNR from the PS to k-th user. To prove the generality of the proposed scheme, we use convex loss function on image classification pr