M-QAM Modulation Symbol-Level Precoding for Power Minimization: Closed-Form Solution

Abstract

In this paper, we derive a closed-form algorithm of the computationally efficient Symbol-Level Precoding (SLP) for power efficient communications when using M-QAM modulated waveforms. The channel state information (CSI) based and dataaided SLP technique optimizes power efficiency by solving a non-negative convex quadratic optimization problem per time frame of transmitted symbols. The optimization combines constructive inter-user interference to minimize the sum power of precoded symbols at the transmitter side under constraints for minimum SNR at the receiver side. The SLP implementation incurs extra computational complexity of the transmitter. We propose a convex quadratic optimization problem for M-QAM constellations and derive a closed-form algorithm with a fixed number of iterations to solve the problem.

Satellite Communications in the New Space Era: A Survey and Future Challenges

“…The transmitter uses this CSI to compute the precoding signals. This infrastructure has been used to experiment with novel optimized precoding techniques, such as symbol-level precoding treated in Section V-C2 [300]- [303].…”

Boosting SWIPT via Symbol-Level Precoding

“…where Γ is the per terminal minimal SINR requirements (as in [13]) and Ξ is the minimal EH demands respectively, defined as…”

“…The proposed Direct Demand-based SLP (DD-SLP) approach will build upon the ZF precoder and will improve the energy efficiency by optimizing every transmitted set of symbols. The optimal symbol mapping is derived in [13].…”

“…. , 4) are identified on the complex plain regardless of the modulation order M considered and the constraints on the perturbation vector based on the position of a symbol are summarized in Table I of [13]. We replace the perturbation vector u with a new vectorũ = [ũ 1 ,ũ 2 , .…”

“…, N u . By substituting (17) in (18) and by employing suitable transformations, we can obtain and efficiently solve a convex problem (as in [13]). In general, the transformed optimization problem can be solved using CVX tools for MATLAB [15].…”