Interference alignment under limited feedback for MIMO interference channels

Krishnamachari, Rajesh Tembarai; Varanasi, Mahesh K.

doi:10.1109/isit.2010.5513544

Cited by 122 publications

(44 citation statements)

References 14 publications

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“…Entries of the channel matrices are generated according to CN (0, 1) and the performance results are averaged over the channel realizations. Our proposed method is compared to the naive method where the interfering channel matrices toward each receiver are independently vectorized and quantized based on the idea of composite Grassmann manifold [9] and finally the indices of the quantized vectors are fed back to the transmitters (denoted by "Naive" in the figures). Figure 1 shows the achievable sum rate versus transmit SNR for N f = 5 and 10 feedback bits when the precoders are designed based on the quantized feedback.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Limited feedback for interference alignment in the K-user MIMO Interference Channel

Rezaee¹,

Guillaud²

2012

2012 IEEE Information Theory Workshop

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Abstract-A simple limited feedback scheme is proposed for interference alignment on the K-user Multiple-Input-MultipleOutput Interference Channel (MIMO-IC). The scaling of the number of feedback bits with the transmit power required to preserve the multiplexing gain that can be achieved using perfect channel state information (CSI) is derived. This result is obtained through a reformulation of the interference alignment problem in order to exploit the benefits of quantization on the Grassmann manifold, which is well investigated in the single-user MIMO channel. Furthermore, through simulations we show that the proposed scheme outperforms the naive feedback scheme consisting in independently quantizing the channel matrices, in the sense that it yields a better sum rate performance for the same number of feedback bits.

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Section: Simulation Resultsmentioning

confidence: 99%

“…In the interference channel the problem is explored in the context of interference alignment over frequency selective channels for single-antenna users in [8] and for multipleantenna users in [9]. Both references provide DoF-achieving quantization schemes and study the required scaling of the number of feedback bits.…”

Section: Introductionmentioning

confidence: 99%

Limited feedback for interference alignment in the K-user MIMO Interference Channel

Rezaee¹,

Guillaud²

2012

2012 IEEE Information Theory Workshop

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“…However, this assumption is not unrealistic since recent results such as [13] and [14] show that under certain mild assumptions, limited feedback can achieve the full sum-rate degrees-of-freedom of the K-user MIMO/SISO IC.…”

Section: Discussionmentioning

confidence: 99%

Interference Alignment: A One-Sided Approach

Ghauch

Papadias

2011

2011 IEEE Global Telecommunications Conference - GLOBECOM 2011

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Abstract-Interference Alignment (IA) is the process of designing signals in such a way that they cast overlapping shadows at their unintended receivers, while remaining distinguishable at the intended ones [1]. Our goal in this paper is to come up with an algorithm for IA that runs at the transmitters only (and is transparent to the receivers), that doesn't require channel reciprocity, and that alleviates the need to alternate between the forward and reverse network as is the case in [2], thereby inducing significant overhead in certain environments where the channel changes frequently. Most importantly, our effort is focused on ensuring that this one-sided approach does not degrade the performance of the system w.r.t. [2] (since it cannot improve it). As a first step, we model the interference in each receiver's desired signal as a function of the transmitters' beamforming vectors. We then propose a simple steepest descent (SD) algorithm and use it to minimize the interference in each receiver's desired signal space. We mathematically establish equivalences between our approach and the Distributed IA algorithm presented in [2] and show that our algorithm also converges to an alignment solution (when the solution is feasible).

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“…Here, energy is analyzed with feedback optimization using Stiefel manifolds. In feedback, all available manifolds are considered to increase the overall performance of basic MIMO [12]. In order to optimize the energy performance, the Stiefel manifold can be used in MIMO, channel estimations and some specific modulations schemes.…”

Section: Basic Mimo With Manifoldsmentioning

confidence: 99%

Relevance of Energy Efficiency Gain in Massive MIMO Wireless Network

Alzahrani¹,

Thayananthan²,

Qureshi³

2017

ijacsa

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Abstract-The massive MIMO and energy efficiency (EE) analysis for the next generation technology are the hottest topics in wireless network research. This paper explains about massive MIMO wireless networks and EE for manifold channel which is an evolution massive MIMO. This research will help to design and implement a practical system of next generation network based on massive MIMO where efficient processing provides EE gain. In order to approach this research, different types of manifolds are considered with efficient techniques that depend on the rank of the channel matrix. Employing the specific manifold that helps to analyze the rate of the feedback increases not only the overall performance of the MIMO system but also the EE. We studied the convergence techniques used for optimizing quantization errors which have influences with manifold feedback. Here, we have focused on relevant areas which are very important to analyze EE gain in the future massive network. According to the selected results obtained in this research, many challenges will be possible to make useful proposals.

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Interference alignment under limited feedback for MIMO interference channels

Cited by 122 publications

References 14 publications

Limited feedback for interference alignment in the K-user MIMO Interference Channel

Limited feedback for interference alignment in the K-user MIMO Interference Channel

Interference Alignment: A One-Sided Approach

Relevance of Energy Efficiency Gain in Massive MIMO Wireless Network

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