MIMO compute-and-forward

Zhan, Jiening; Nazer, Bobak; Gastpar, Michael; Erez, Uri

doi:10.1109/isit.2009.5205264

Cited by 52 publications

(57 citation statements)

References 11 publications

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“…As a result, we would decode the aggregate interference [x 2 + x 3 + ... + x K ] Mod Λ without any quantization approximation. By solving the optimization problem in (27), we can obtain the optimal first stage decorrelator,…”

Section: A Performance Of the Proposed Methods For Symmetric Interfermentioning

confidence: 99%

“…(27) For perfect CSI (ǫ = 0,Ĥ = H), closed form solutions exist for (27) and the optimal solutions are given by [27]:…”

Section: B Optimization Of {U U C} Under Fixed {V A}mentioning

confidence: 99%

“…and • Step 2: For the given {v, a}, solve the convex optimization problem (27) to obtain the corresponding optimal first stage decorrelator u.…”

Section: B Optimization Of {U U C} Under Fixed {V A}mentioning

confidence: 99%

“…This is because the subproblems to be solved in each step of the proposed algorithm (e.g. Problem (27) and Problem (36)) are convex and hence, there exists efficient algorithms (such as interior-point method [28]). …”

Section: Remark 5: Qualitative Comparison Of the Algorithm With Convementioning

confidence: 99%

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Robust Lattice Alignment for $K$-User MIMO Interference Channels With Imperfect Channel Knowledge

Huang

Lau

et al. 2011

IEEE Trans. Signal Process.

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Abstract-In this paper, we consider a robust lattice alignment design for K-user quasi-static MIMO interference channels with imperfect channel knowledge. With random Gaussian inputs, the conventional interference alignment (IA) method has the feasibility problem when the channel is quasi-static. On the other hand, structured lattices can create structured interference as opposed to the random interference caused by random Gaussian symbols. The structured interference space can be exploited to transmit the desired signals over the gaps. However, the existing alignment methods on the lattice codes for quasi-static channels either require infinite SNR or symmetric interference channel coefficients. Furthermore, perfect channel state information (CSI) is required for these alignment methods, which is difficult to achieve in practice. In this paper, we propose a robust lattice alignment method for quasi-static MIMO interference channels with imperfect CSI at all SNR regimes, and a two-stage decoding algorithm to decode the desired signal from the structured interference space. We derive the achievable data rate based on the proposed robust lattice alignment method, where the design of the precoders, decorrelators, scaling coefficients and interference quantization coefficients is jointly formulated as a mixed integer and continuous optimization problem. The effect of imperfect CSI is also accommodated in the optimization formulation, and hence the derived solution is robust to imperfect CSI. We also design a low complex iterative optimization algorithm for our robust lattice alignment method by using the existing iterative IA algorithm that was designed for the conventional IA method. Numerical results verify the advantages of the proposed robust lattice alignment method compared with the TDMA, two-stage ML decoding, generalized Han-Kobayashi (HK), distributive IA and conventional IA methods in the literature.

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Section: A Performance Of the Proposed Methods For Symmetric Interfermentioning

confidence: 99%

“…(27) For perfect CSI (ǫ = 0,Ĥ = H), closed form solutions exist for (27) and the optimal solutions are given by [27]:…”

Section: B Optimization Of {U U C} Under Fixed {V A}mentioning

confidence: 99%

“…and • Step 2: For the given {v, a}, solve the convex optimization problem (27) to obtain the corresponding optimal first stage decorrelator u.…”

Section: B Optimization Of {U U C} Under Fixed {V A}mentioning

confidence: 99%

Section: Remark 5: Qualitative Comparison Of the Algorithm With Convementioning

confidence: 99%

See 2 more Smart Citations

Robust Lattice Alignment for $K$-User MIMO Interference Channels With Imperfect Channel Knowledge

Huang

Lau

et al. 2011

IEEE Trans. Signal Process.

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“…Compute-andforward (C&F) [13] is a parallel work to PNC that also proposes to compute a function of the collided packets to further transmit in digital form. MIMO compute-and-forward [14] studies the theoretical achievable rates of a many-to-one transmissions, with multiple antennas at each node. Assuming all senders employ the same lattice code for modulation, the authors demonstrate that the idea of demodulating a linear combination can improve the achievable rates.…”

Section: Previous Researchmentioning

confidence: 99%

Signal Alignment: Enabling Physical Layer Network Coding for MIMO Networking

Zhou

et al. 2013

IEEE Trans. Wireless Commun.

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Abstract-We apply signal alignment (SA), a wireless communication technique that enables physical layer network coding (PNC) in multi-input multi-output (MIMO) wireless networks. Through calculated precoding, SA contracts the perceived signal space at a node to match its receive capability, and hence facilitates the demodulation of linearly combined data packets. PNC coupled with SA (PNC-SA) has the potential of fully exploiting the precoding space at the senders, and can better utilize the spatial diversity of a MIMO network for higher system degrees-of-freedom (DoF). PNC-SA adopts the idea of 'demodulating a linear combination' from PNC. The design of PNC-SA is also inspired by recent advances in IA, though SA aligns signals not interferences. We study the optimal precoding and power allocation problem of PNC-SA, for SNR (singal-tonoise-ratio) maximization at the receiver. The mapping from SNR to BER is then analyzed, revealing that the DoF gain of PNC-SA does not come with a sacrifice in BER. We then design a general PNC-SA algorithm in larger systems, and demonstrate general applications of PNC-SA, and show via network level simulations that it can substantially increase the throughput of unicast and multicast sessions, by opening previously unexplored solution spaces in multi-hop MIMO routing.

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