Abstract
The nonlinear vector precoding (VP) technique has been proven to achieve close-to-capacity performance in multiuser multiple-input multiple-output (MIMO) downlink channels. The performance benefit with respect to its linear counterparts stems from the incorporation of a perturbation signal that reduces the power of the precoded signal. The computation of this perturbation element, which is known to belong in the class of NP-hard problems, is the main aspect that hinders the hardware implementation of VP systems. To this respect, several tree-search algorithms have been proposed for the closest-point lattice search problem in VP systems hitherto. Nevertheless, the optimality of these algorithms has been assessed mainly in terms of error-rate performance and computational complexity, leaving the hardware cost of their implementation an open issue. The parallel data-processing capabilities of field-programmable gate arrays (FPGA) and the loopless nature of the proposed tree-search algorithms have enabled an efficient hardware implementation of a VP system that provides a very high data-processing throughput.
Highlights
Since the presentation of the vector precoding (VP) technique [1] for data transmission over the multiuser broadcast channel, many algorithms have been proposed in the literature to replace the computationally intractable exhaustive search defined in the original description of the algorithm
As a consequence to this, around 27% of the slice look-up tables (LUTs) are used as memory in the K-best implementation, as opposed to the 2% utilized by the fixed-complexity sphere encoder (FSE) for the same purpose
This paper has addressed the issues of a fully-pipelined implementation of the FSE and K-best tree-search approaches for a 4 × 4 VP system
Summary
Since the presentation of the vector precoding (VP) technique [1] for data transmission over the multiuser broadcast channel, many algorithms have been proposed in the literature to replace the computationally intractable exhaustive search defined in the original description of the algorithm To this respect, lattice reduction approaches have been widely used as a means to compute a suboptimum perturbation vector with a moderate complexity. The key idea of latticereduction techniques relies on the usage of an equivalent and more advantageous set of basis vectors to allow for the suboptimal resolution of the exhaustive search problem by means of a simple rounding operation This method is used in [2], where the Lenstra-Lenstra-Lovasz (LLL) reduction algorithm [3] is used to yield the Babai’s approximate closestpoint solution [4]. Many lattice reduction algorithms have a considerable computational complexity, which poses many challenges to a prospective hardware implementation
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