Adaptive nonlinear interference suppressor for cognitive radio applications

To utilize the radio frequency spectrum efficiently a Cognitive Radio (CR) can operate as a secondary user in a frequency band which is licensed to a primary user. To this end, the CR must sense the spectrum continuously to find empty frequency channels for its transmission. The transmitted signal by the local transmitter of the CR, however, induces a strong local interference in the local receiver of the CR. Hence a half-duplex transceiver is used where the transmit and sense operations are done in separate time slots. The time-slotted operation though, reduces the throughput of the CR. This paper proposes application of an adaptive Nonlinear Interference Suppressor (NIS) to suppress this strong local interference to enable simultaneous transmit and sense. We present experimental results of a transceiver testbed that uses an implementation of the NIS, fabricated in 140 nm CMOS technology. These experiments show that the NIS can substantially suppress the local interference with low complexity and power consumption

Adaptive channel estimation using least mean squares algorithm for cyclic prefix OFDM systems

Orthogonal frequency division multiplexing (OFDM) delivers high data transmission rate and forms the basis of Beyond 3G. The channel estimation is imperative for the implementation of OFDM. Cyclic Prefix (CP) based block Recursive Least Squares (RLS) channel estimation algorithm has been proposed for OFDM systems but it increases computational complexity. In this paper, we propose a block LMS (Least Mean Squares) channel estimation algorithm which promises less computation but delivers comparable and promising results