Interference Alignment is a promising technique for achieving higher rates by aligning interference at the receiver. To design such a system, the global channel state information at the transmitter (CSIT) as well as at the receiver is necessary. But in practice, it is hard to obtain this information, therefore, limited feedback is used to provide CSIT or the precoder design information to the transmitter. Conventionally, precoders are quantized at receiver by finding its best match in the codebook using chordal distance and its index is fedback to the transmitter. In this paper, instead of minimizing chordal distance, we propose algorithms with objectives that are derived from subspace alignment method, SINR maximization, or minimization of leakage interference power to measure the “goodness” of quantized vector. These algorithms achieve higher rates for small size codebooks. The rate loss has been analyzed for precoder quantization. We also find less computational intensive solution to find the desired vectors in the codebook. The simulation results show that for small codebooks, significant sumrate gains can be achieved for (2 × 2,1) for 2-6 bits of feedback per user, compared to quantization based on chordal distance, while for large codebooks, the chordal distance based quantization performs better.
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