Wireless communications has grown exponentially in the last few decades. Hence, the demand for more throughput and diverse communication standards (such as GSM, Bluetooth, WiFi, LTE), have increased over time. To increase the throughput and keep hardware costs low is necessary to allow for users keep up with new applications without affecting the business model for service providers (Carriers). Software-defined radio (SDR), which aims to be easily programmable, is a good candidate to meet current market demands. However, a number of technical challenges make a SDR receiver more practical than the architecture proposed by  - . In this work, contrasted to other results published in the literature, we not only provide a controlled continuous bias to the diodes, we also propose a novel algorithm that reduces the Error Vector Magnitude (EVM) by adaptively controlling the diode bias point. Another key feature of optimum diode bias control is the Local Oscillator (LO) power requirements decrease and the EVM which is not sensitive to LO power variation. Results presented show that a LO power variation of more than 10dB produces no EVM variation. In continuation to the blind algorithm presented, we also developed a novel methodology for estimating the initial diode bias voltage. Although we tested the methodology for four different Schottky diodes from different vendors (Win Corp, HP, Hitachi and Siemens) the process can be applied to any diode. The initial value is located at the inflection point of the first derivative of the I-V curve that fits polynomial obtained in Matlab and assures that the optimizer will not be trapped into local minima. To verify the results of this research we used a Simulink model that emulates the radio frequency (RF) and digital base band parts of a Six-Port receiver. This work also provides the behavior of the variation of the initial estimates of the diode voltage with the polynomial degree. Hence, the necessary degree for the fitting polynomial can be determined based on the initial point variation analysis.