The algorithm used to separate the two signals S1 and S2 of the two subscribers in NOMA is the commonly successive interference cancellation where one detects and decodes the strongest received signal and subtracts it from the total received signal, then the remaining received signal will be that of the weaker subscriber signal which can decoded.
Assume that the received signal can be modeled by the equation:
r= sqrt P1 h1 S1+ sqrt P2 h2S2 + sigman = r1+r2+ sigman the parameters have their usual meaning
In order to separate the signal in NOMA if P1=P2 as the of users can be considered equal, then h1 must be much greater than h2 which means that subscriber one is much nearer to the base station.
Therefore r1>>r2, and r2 can be considered as noise and S1 can be detected and decoded. And consequently r1 can be calculated and cancelled. So, it is clear that h1 and h2 must be known for signal separation in NOMA.
The algorithm used to separate the two signals S1 and S2 of the two subscribers in NOMA is the commonly successive interference cancellation where one detects and decodes the strongest received signal and subtracts it from the total received signal, then the remaining received signal will be that of the weaker subscriber signal which can decoded.
Assume that the received signal can be modeled by the equation:
r= sqrt P1 h1 S1+ sqrt P2 h2S2 + sigman = r1+r2+ sigman the parameters have their usual meaning
In order to separate the signal in NOMA if P1=P2 as the of users can be considered equal, then h1 must be much greater than h2 which means that subscriber one is much nearer to the base station.
Therefore r1>>r2, and r2 can be considered as noise and S1 can be detected and decoded. And consequently r1 can be calculated and cancelled. So, it is clear that h1 and h2 must be known for signal separation in NOMA.