Doctor of Philosophy / Department of Electrical and Computer Engineering / Don M. Gruenbacher / Future technologies such as cognitive radio require flexible and reliable hardware architectures that can be easily configured and adapted to varying coding parameters. The objective of this work is to develop a flexible hardware encoder and decoder for low-density parity-check (LDPC) codes. The design methodologies used for the implementation of a LDPC encoder and decoder are flexible in terms of parity-check matrix, code rate and code length. All these designs are implemented on a programmable chip and tested. Encoder implementations of LDPC codes are optimized for area due to their high complexity. Such designs usually have relatively low data rate. Two new encoder designs are developed that achieve much higher data rates of up to 844 Mbps while requiring more area for implementation. Using structured LDPC codes decreases the encoding complexity and provides design flexibility. The architecture for an encoder is presented that adheres to the structured LDPC codes defined in the IEEE 802.16e standard. A single encoder design is also developed that accommodates different code lengths and code rates and does not require re-synthesis of the design in order to change the encoding parameters. The flexible encoder design for structured LDPC codes is also implemented on a custom chip. The maximum coded data rate of the structured encoder is up to 844 Mbps and for a given code rate its value is independent of the code length. An LDPC decoder is designed and its design methodology is generic. It is applicable to both structured and any randomly generated LDPC codes. The coded data rate of the decoder increases with the increase in the code length. The number of decoding iterations used for the decoding process plays an important role in determining the decoder performance and latency. This design validates the estimated codeword after every iteration and stops the decoding process when the correct codeword is estimated which saves power consumption. For a given parity-check matrix and signal-to-noise ratio, a procedure to find an optimum value of the maximum number of decoding iterations is presented that considers the affects of power, delay, and error performance.