Multi-user Multi-stream Vector Perturbation Precoding

  • Authors:
  • Rui Chen;Jiandong Li;Changle Li;Wei Liu

  • Affiliations:
  • Broadband Wireless Communications Laboratory, Information Science Institute, State Key Laboratory of Integrated Service Networks, Xidian University, Xian, China;Broadband Wireless Communications Laboratory, Information Science Institute, State Key Laboratory of Integrated Service Networks, Xidian University, Xian, China;Broadband Wireless Communications Laboratory, Information Science Institute, State Key Laboratory of Integrated Service Networks, Xidian University, Xian, China;Broadband Wireless Communications Laboratory, Information Science Institute, State Key Laboratory of Integrated Service Networks, Xidian University, Xian, China and School of Electronics and Compu ...

  • Venue:
  • Wireless Personal Communications: An International Journal
  • Year:
  • 2013

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Abstract

Vector perturbation (VP) is a prominent precoding technique attracted a lot of attention in recent years. Till now, however, various extended VP techniques are almost restricted to one antenna configuration of each user, termed as multi-user single-stream VP, which contradicts with the multi-antenna terminals of the next generation wireless systems. So, the popular block diagonal (BD) algorithm and VP was naturally combined and proposed to solve this problem. However, the so-called BD-VP completely suppresses multi-user interference at the expense of noise enhancement and results in performance degradation. To overcome the shortcoming of BD-VP, we propose three criteria based multi-user multi-stream VP (MUMS-VP) algorithms with different performance and complexity tradeoffs: zero-forcing expanded channel inversion (ZF ECI)-VP algorithm, minimum-total-mean-square-error criterion (MTMSE) based two MUMS-VP algorithms and maximum signal-to-leakage-and-noise-ratio (MSLNR) criterion based MUMS-VP algorithm. The general expression of achievable rates of MUMS-VP algorithms is derived. Analysis and simulation results show that the proposed ZF MUMS-VP is equivalent to BD-VP, while MTMSE MUMS-VP II has the maximum achievable sum rates among these algorithms. MTMSE MUMS-VP I, II and MSLNR MUMS-VP greatly outperform BD-VP in BER performance for both fixed modulation scenario and adaptive modulation scenario. Furthermore, all proposed algorithms have much lower complexity than BD-VP.