  Maximum likelihood (ML) detection is an optimal signal detection scheme,
which is often difficult to implement due to its high computational complexity,
especially in a multiple-input multiple-output (MIMO) scenario. In a system
with $N_t$ transmit antennas employing $M$-ary modulation, the ML-MIMO detector
requires $M^{N_t}$ cost function (CF) evaluations followed by a search
operation for detecting the symbol with the minimum CF value. However, a
practical system needs the bit-error ratio (BER) to be application-dependent
which could be sub-optimal. This implies that it may not be necessary to have
the minimal CF solution all the time. Rather it is desirable to search for a
solution that meets the required sub-optimal BER. In this work, we propose a
new detector design for a SISO/MIMO system by obtaining the relation between
BER and CF which also improves the computational complexity of the ML detector
for a sub-optimal BER.
