Computation of Interference Alignment Transceivers

In this paper, we investigate the computation of transceivers for interference alignment. Interference alignment is a technique that enhances throughput and reliability in wireless networks. The fundamental concept involves using linear processing at both transmitter and receiver ends to minimize the impact of interference signals. Therefore, computing interference alignment transceivers is crucial as it helps us better understand how to achieve interference alignment and make wireless networks more efficient and reliable.

From an implementation perspective, this typically involves solving optimization problems to find precoding matrices at transmitters and receive filters at receivers. Key algorithms include minimum interference leakage, max-SINR (Signal-to-Interference-plus-Noise Ratio), and alternating minimization approaches. The computation often utilizes matrix decomposition techniques like singular value decomposition (SVD) and eigenvalue decomposition to align interference subspaces. MATLAB implementations commonly employ functions such as svd(), eig(), and optimization solvers like fmincon() for constrained optimization problems.

The iterative nature of these algorithms requires careful convergence analysis, where each iteration updates transceiver matrices until interference terms become linearly dependent in the signal space. Code implementation typically measures performance metrics like sum-rate capacity and interference leakage to validate the alignment quality.