Codebook-Based Lattice-Reduction-Aided Precoding for Limited-Feedback Coded MIMO Systems

Yang, Hyun Jong; Chun, Joohwan; Choi, Youngchol; Kim, Sung‐Soo; Paulraj, A.

doi:10.1109/tcomm.2011.122111.100489

Cited by 8 publications

(10 citation statements)

References 40 publications

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“…With

\overset{false~}{bold-italicF} = F T

, the transmit signal can be rewritten as

x = \sqrt{\frac{P}{\tilde{γ}}} F T f_{τ} false({bold-italicT}^{- 1} u false) = \sqrt{\frac{P}{\tilde{γ}}} \overset{false~}{bold-italicF} false({bold-italicT}^{- 1} u + τ l false)

where

f_{τ} false(\cdot false)

is the modulo function and τ is a positive real number, which is chosen according to the signal constellation [9]. By applying the modulo function, the size of

f_{τ} false({bold-italicT}^{- 1} u false)

is restricted in

(](, - τ / 2, τ / 2) + i (](, - τ / 2, τ / 2)

, which can be expressed as

f_{τ} false({bold-italicT}^{- 1} u false) = {bold-italicT}^{- 1} u + τ l

[24]. Here, l is an integer vector.…”

Section: Lra Precoding and Lr Algorithmsmentioning

confidence: 99%

Computationally efficient fixed complexity LLL algorithm for lattice‐reduction‐aided multiple‐input–multiple‐output precoding

Wang

et al. 2016

IET Communications

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“…With

\overset{false~}{bold-italicF} = F T

, the transmit signal can be rewritten as

x = \sqrt{\frac{P}{\tilde{γ}}} F T f_{τ} false({bold-italicT}^{- 1} u false) = \sqrt{\frac{P}{\tilde{γ}}} \overset{false~}{bold-italicF} false({bold-italicT}^{- 1} u + τ l false)

where

f_{τ} false(\cdot false)

is the modulo function and τ is a positive real number, which is chosen according to the signal constellation [9]. By applying the modulo function, the size of

f_{τ} false({bold-italicT}^{- 1} u false)

is restricted in

(](, - τ / 2, τ / 2) + i (](, - τ / 2, τ / 2)

, which can be expressed as

f_{τ} false({bold-italicT}^{- 1} u false) = {bold-italicT}^{- 1} u + τ l

[24]. Here, l is an integer vector.…”

Section: Lra Precoding and Lr Algorithmsmentioning

confidence: 99%

Computationally efficient fixed complexity LLL algorithm for lattice‐reduction‐aided multiple‐input–multiple‐output precoding

Wang

et al. 2016

IET Communications

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“…However, sacrificing this shaping gain by 1.53 dB in SNR, one can easily design lattice codes with practical non-binary codes such as low-density parity check codes [19], or binary multilevel turbo codes [20]. For more detailed discussion on the implementation of lattice codes, the readers are referred to [21] and references therein, or to [22] and references therein for the effort to implement practically-tailored lattice codes in two-way relay channels.…”

Section: B Proof Of Theorem 1 For Lc-cfmentioning

confidence: 99%

On the Degrees of Freedom of the Large-Scale Interfering Two-Way Relay Network

Yang

Shin

Jung

2016

IEEE Trans. Veh. Technol.

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Achievable degrees-of-freedom (DoF) of the large-scale interfering two-way relay network is investigated. The network consists of K pairs of communication nodes (CNs) and N relay nodes (RNs). It is assumed that K ≪ N and each pair of CNs communicates with each other through one of the N relay nodes without a direct link between them. Interference among RNs is also considered. Assuming local channel state information (CSI) at each RN, a distributed and opportunistic RN selection technique is proposed for the following three promising relaying protocols: amplifyforward, decode-forward, and compute-forward. As a main result, the asymptotically achievable DoF is characterized as N increases for the three relaying protocols. In particular, a sufficient condition on N required to achieve the certain DoF of the network is analyzed. Through extensive simulations, it is shown that the proposed RN selection techniques outperform conventional schemes in terms of achievable rate even in practical communication scenarios. Note that the proposed technique operates with a distributed manner and requires only local CSI, leading to easy implementation for practical wireless systems. Index TermsDegrees-of-freedom (DoF), interfering two-way relay channel, two-way K × N × K channel, local channel state information, relay selection. On the other hand, there have been few schemes that consider a general interfering TWR network which consists of K pairs of CNs and N RNs, also known as K × N × K interfering TWR networks. In [1], Rankov and Wittneben showed that the amplify-and-forward (AF) relaying protocol with interference-neutralizing beamforming can achieve the optimal 1 DoF of the half-duplex K × N × K interfering TWR network if N ≥ K(K − 1) + 1 for a given K. However, the scheme in [1] requires global CSI at all nodes and full collaboration amongst all RNs. The authors of [8], [9] considered the achievable degrees-of-freedom of K × K × K interfering OWR networks, where the number of CNs and RNs are the same. In particular, the interference neutralization technique of [1] was combined with the interference alignment technique to achieve the optimal DoF of the 2×2×2 interfering OWR network [8] . However, the scheme in [8] cannot be applied to the general K × N × K interfering TWR network with arbitrary numbers of K and N. In addition, the scheme in [8] works only with global CSI assumption at each node.The internet-of-things (IoT) concept has recently received much attection from wireless researchers, where an extremely large number of devices are expected to exist. In addition, the fifth generation (5G) cellular network is expected to support more than 10,000 devices, each of which can communicate directly with others or operate as a relay [10]. Among many devices, a small number of devices may transmit at a time due to sparse traffic pattern in the IoT scenario. Several studies have defined and studied the (N, K)-user interference channel (N ≫ K), in which K user pairs are selected to communicate at a time [11], [12].In...

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“…However, it is very difficult to be realized in massive MIMO systems due to the high complexity of successive encoding and decoding. To achieve the close-optimal capacity with reduced complexity, some other nonlinear precoding techniques, such as vector perturbation (VP) precoding [8], [9] and lattice-aided precoding [10], [11], have been proposed, but their complexity is still unaffordable when the dimension of the MIMO system is large or the modulation order is high [12] (e.g., 256 antennas at the BS with 64 QAM modulation). To make a trade-off between the capacity and complexity, one can resort to linear precoding techniques, which can also achieve the capacityapproaching performance in massive MIMO systems, where the channel matrix are asymptotically orthogonal [13].…”

Section: Introductionmentioning

confidence: 99%

Near-Optimal Linear Precoding with Low Complexity for Massive MIMO

Dai,

Gao,

Han

et al. 2014

Preprint

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Linear precoding techniques can achieve nearoptimal capacity due to the special channel property in downlink massive MIMO systems, but involve high complexity since complicated matrix inversion of large size is required. In this paper, we propose a low-complexity linear precoding scheme based on the Gauss-Seidel (GS) method. The proposed scheme can achieve the capacity-approaching performance of the classical linear precoding schemes in an iterative way without complicated matrix inversion, which can reduce the overall complexity by one order of magnitude. The performance guarantee of the proposed GS-based precoding is analyzed from the following three aspects. At first, we prove that GS-based precoding satisfies the transmit power constraint. Then, we prove that GS-based precoding enjoys a faster convergence rate than the recently proposed Neumann-based precoding. At last, the convergence rate achieved by GS-based precoding is quantified, which reveals that GS-based precoding converges faster with the increasing number of BS antennas. To further accelerate the convergence rate and reduce the complexity, we propose a zone-based initial solution to GS-based precoding, which is much closer to the final solution than the traditional initial solution. Simulation results demonstrate that the proposed scheme outperforms Neumannbased precoding, and achieves the exact capacity-approaching performance of the classical linear precoding schemes with only a small number of iterations both in Rayleigh fading channels and spatially correlated channels.

show abstract

Codebook-Based Lattice-Reduction-Aided Precoding for Limited-Feedback Coded MIMO Systems

Cited by 8 publications

References 40 publications

Computationally efficient fixed complexity LLL algorithm for lattice‐reduction‐aided multiple‐input–multiple‐output precoding

Computationally efficient fixed complexity LLL algorithm for lattice‐reduction‐aided multiple‐input–multiple‐output precoding

On the Degrees of Freedom of the Large-Scale Interfering Two-Way Relay Network

Near-Optimal Linear Precoding with Low Complexity for Massive MIMO

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