Lattice-Reduction-Aided and
Integer-Forcing Equalization

Fischer, Robert F. H.; Stern, Sebastian; Huber, Johannes B.

doi:10.1561/0100000100

Cited by 11 publications

(26 citation statements)

References 65 publications

(86 reference statements)

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“…There are several optimal SMP algorithms [10], [12]- [15]. For conciseness, we briefly introduce the main ideas of the algorithms in the recent two papers [14], [15] only.…”

Section: B Related Workmentioning

confidence: 99%

“…There are several optimal SMP algorithms [10], [12]- [15]. For conciseness, we briefly introduce the main ideas of the algorithms in the recent two papers [14], [15] only. There are two SMP algorithms in [14] which are respectively for solving SMP's on real and complex lattices.…”

Section: B Related Workmentioning

confidence: 99%

“…Finally, this solution is left multiplied with the unimodular matrix generated by the LLL reduction to give A ⋆ . The first and last steps of the algorithm in [15] are the same as those of the algorithm in [14]. However, its second step is different.…”

Section: B Related Workmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Optimal Algorithm for the Successive Minima Problem

Wen

Tang

et al. 2019

IEEE Trans. Commun.

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In many applications including integer-forcing linear multiple-input and multiple-output (MIMO) receiver design, one needs to solve a successive minima problem (SMP) on an n-dimensional lattice to get an optimal integer coefficient matrix A ⋆ ∈ Z n×n . In this paper, we first propose an efficient optimal SMP algorithm with an O(n 2 ) memory complexity. The main idea behind the new algorithm is it first initializes with a suitable suboptimal solution, which is then updated, via a novel algorithm with only O(n 2 ) flops in each updating, until A ⋆ is obtained. Different from existing algorithms which find A ⋆ column by column through using a sphere decoding search strategy n times, the new algorithm uses a search strategy once only. We then rigorously prove the optimality of the proposed algorithm. Furthermore, we theoretically analyze its complexity. In particular, we not only show that the new algorithm is Ω(n) times faster than the most efficient existing algorithm with polynomial memory complexity, but also assert that it is even more efficient than the most efficient existing algorithm with exponential memory complexity. Finally, numerical simulations are presented to illustrate the optimality and efficiency of our novel SMP algorithm.

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“…There are several optimal SMP algorithms [10], [12]- [15]. For conciseness, we briefly introduce the main ideas of the algorithms in the recent two papers [14], [15] only.…”

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Optimal Algorithm for the Successive Minima Problem

Wen

Tang

et al. 2019

IEEE Trans. Commun.

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“…wheres = As is the symbol vector to be recovered at the receiver. Note that since C is chosen to be nearly orthogonal, recoverings instead of s has the advantage to avoid severe noise enhancement [9]. Hence, The corresponding MMSE equalization matrix B is given by…”

Section: Lattice-reduction-aided Mimo Equalizationmentioning

confidence: 99%

Lattice-Reduction-Aided Equalization for MIMO-FBMC Systems

Zakaria

Silva

Ruyet

2019

IEEE Wireless Commun. Lett.

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In this paper, we propose to use a lattice reduction (LR) approach to enhance the performance of spatial multiplexing (SM) multiple-input multiple-output (MIMO) systems using filter-bank multicarrier (FBMC) modulation. It is known that lattice-reduction-aided (LRA) MIMO receivers can achieve full spatial diversity. On the other hand, the presence of the intrinsic interference in FBMC is widely regarded as a factor that prevents the system from taking advantage of spatial diversity when combined with SM-MIMO. We will show that with an adequate adaptation to FBMC modulation, the LR approach can be performed in a straightforward manner. Simulation results show that an LRA equalizer allows an FBMC-MIMO system to benefit more from the available spatial diversity and offers an interesting performance gain compared to the existing solutions with reasonable complexity.

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“…It is worth mentioning that IF techniques are closely related to lattice reduction strategies [18], [19]. It is shown in [15] that uplink IF with uncoded transmission, i.e., using a one-dimensional lattice, is essentially equivalent to lattice-reduction-aided reception, which focuses on symbolby-symbol detection.…”

Section: B Related Workmentioning

confidence: 99%

On Integer-Forcing Precoding for the Gaussian MIMO Broadcast Channel

Silva

Pivaro

Fraidenraich

et al. 2017

IEEE Trans. Wireless Commun.

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Abstract-Integer-forcing (IF) precoding, also known as downlink IF, is a promising new approach for communication over multiple-input multiple-output (MIMO) broadcast channels. Inspired by the integer-forcing linear receiver for multiple-access channels, it generalizes linear precoding by inducing an effective channel matrix that is approximately integer, rather than approximately identity. Combined with lattice encoding and a preinversion of the channel matrix at the transmitter, the scheme has the potential to outperform any linear precoding scheme, despite enjoying similar low complexity.In this paper, a specific IF precoding scheme, called diagonallyscaled exact IF (DIF), is proposed and shown to achieve maximum spatial multiplexing gain. For the special case of two receivers, in the high SNR regime, an optimal choice of parameters is derived analytically, leading to an almost closed-form expression for the achievable sum rate. In particular, it is shown that the gap to the sum capacity is upper bounded by 0.27 bits for any channel realization. For general SNR, a regularized version of DIF (RDIF) is proposed. Numerical results for two receivers under Rayleigh fading show that RDIF can achieve performance superior to optimal linear precoding and very close to the sum capacity.

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Lattice-Reduction-Aided and Integer-Forcing Equalization

Cited by 11 publications

References 65 publications

An Efficient Optimal Algorithm for the Successive Minima Problem

An Efficient Optimal Algorithm for the Successive Minima Problem

Lattice-Reduction-Aided Equalization for MIMO-FBMC Systems

On Integer-Forcing Precoding for the Gaussian MIMO Broadcast Channel

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