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39
Reliable physical layer network coding
 Proceedings of the IEEE
, 2011
"... Abstract—When two or more users in a wireless network transmit simultaneously, their electromagnetic signals are linearly superimposed on the channel. As a result, a receiver that is interested in one of these signals sees the others as unwanted interference. This property of the wireless medium is ..."
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Cited by 55 (6 self)
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Abstract—When two or more users in a wireless network transmit simultaneously, their electromagnetic signals are linearly superimposed on the channel. As a result, a receiver that is interested in one of these signals sees the others as unwanted interference. This property of the wireless medium is typically viewed as a hindrance to reliable communication over a network. However, using a recently developed coding strategy, interference can in fact be harnessed for network coding. In a wired network, (linear) network coding refers to each intermediate node taking its received packets, computing a linear combination over a finite field, and forwarding the outcome towards the destinations. Then, given an appropriate set of linear combinations, a destination can solve for its desired packets. For certain topologies, this strategy can attain significantly higher throughputs over routingbased strategies. Reliable physical layer network coding takes this idea one step further: using judiciously chosen linear errorcorrecting codes, intermediate nodes in a wireless network can directly recover linear combinations of the packets from the observed noisy superpositions of transmitted signals. Starting with some simple examples, this survey explores the core ideas behind this new technique and the possibilities it offers for communication over interferencelimited wireless networks. Index Terms—Digital communication, wireless networks, interference, network coding, channel coding, linear code, modulation, physical layer, fading, multiuser channels, multiple access, broadcast. I.
Multilevel coding schemes for computeandforward,” see http://arxiv.org/abs/1010.1016
"... Abstract—We consider the design of coding schemes for the wireless twoway relaying channel when there is no channel state information at the transmitter. In the spirit of the compute and forward paradigm, we present a multilevel coding scheme that permits the recovery of a class of functions at the ..."
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Cited by 22 (3 self)
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Abstract—We consider the design of coding schemes for the wireless twoway relaying channel when there is no channel state information at the transmitter. In the spirit of the compute and forward paradigm, we present a multilevel coding scheme that permits the recovery of a class of functions at the relay. We define such a class of functions and derive rates that are universally achievable over a set of channel gains when this class of functions is used at the relay. We develop our framework with general modulation formats in mind, but numerical results are presented for the case where each node transmits using the QPSK constellation. Numerical results with QPSK show that substantially higher rates are achievable with our proposed approach than those achievable by always using a fixed function or adapting the function at the relay but coding over GF(4). Index Terms—Network coding, multilevel coding, twoway relaying, computeandforward I.
Successive ComputeandForward
"... Abstract—In prior work, we proposed the computeandforward framework for sending linear combinations of messages to relays. In this note, we extend the notion of successive interference cancellation to the computeandforward setting. We find that once a relay has decoded a linear combination, it ca ..."
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Cited by 18 (3 self)
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Abstract—In prior work, we proposed the computeandforward framework for sending linear combinations of messages to relays. In this note, we extend the notion of successive interference cancellation to the computeandforward setting. We find that once a relay has decoded a linear combination, it can mix it with its channel output to create a new effective channel output. The resulting effective channel can be tuned so that it is more suitable for decoding a second linear combination than the original channel. I.
Computeandforward network coding design over multisource multirelay channels
 IEEE Trans. Wireless Communications
, 2012
"... Abstract—Network coding is a new and promising paradigm for modern communication networks by allowing intermediate nodes to mix messages received from multiple sources. Computeandforward strategy is one category of network coding in which a relay will decode and forward a linear combination of sou ..."
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Cited by 12 (4 self)
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Abstract—Network coding is a new and promising paradigm for modern communication networks by allowing intermediate nodes to mix messages received from multiple sources. Computeandforward strategy is one category of network coding in which a relay will decode and forward a linear combination of source messages according to the observed channel coefficients, based on the algebraic structure of lattice codes. The destination will recover all transmitted messages if enough linear equations are received. In this work, we design in a system level, the computeandforward network coding coefficients by FinckePohst based candidate set searching algorithm and network coding system matrix constructing algorithm, such that by those proposed algorithms, the transmission rate of the multisource multirelay system is maximized. Numerical results demonstrate the effectiveness of our proposed algorithms. Index Terms—Computeandforward, network coding, linear network coding, lattice codes, cooperative, relay channel.
Multistage computeandforward with multilevel lattice codes based on product constructions
 in Proc. IEEE ISIT
, 2014
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On the Ergodic Rate for ComputeandForward
"... Abstract—A key issue in computeandforward for physical layer network coding scheme is to determine a good function of the received messages to be reliably estimated at the relay nodes. We show that this optimization problem can be viewed as the problem of finding the closest point of Z[i] n to a l ..."
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Cited by 4 (4 self)
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Abstract—A key issue in computeandforward for physical layer network coding scheme is to determine a good function of the received messages to be reliably estimated at the relay nodes. We show that this optimization problem can be viewed as the problem of finding the closest point of Z[i] n to a line in the ndimensional complex Euclidean space, within a bounded region around the origin. We then use the complex version of the LLL lattice basis reduction (CLLL) algorithm to provide a reduced complexity suboptimal solution as well as an upper bound to the minimum distance of the lattice point from the line. Using this bound we are able to find a lower bound to the ergodic rate and a union bound estimate on the error performance of a lattice constellation used for lattice network coding. We compare performance of the CLLL with a more complex iterative optimization method as well as with a simple quantized search. Simulations show how CLLL can trade some performance for a lower complexity. Index Terms—Ergodic rate, computeandforward, CLLL algorithm, quantized error, successive refinement. I.
On complex LLL algorithm for integer forcing linear receivers
 in Proc. of 2013 Australian Communications Theory Workshop (AusCTW13
, 2013
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Integerforcing MIMO linear receivers based on lattice reduction
 IEEE Trans. Wireless Commun
, 2013
"... Abstract—A new architecture called integerforcing (IF) linear receiver has been recently proposed for multipleinput multipleoutput (MIMO) fading channels, wherein an appropriate integer linear combination of the received symbols has to be computed as a part of the decoding process. In this paper, ..."
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Cited by 3 (2 self)
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Abstract—A new architecture called integerforcing (IF) linear receiver has been recently proposed for multipleinput multipleoutput (MIMO) fading channels, wherein an appropriate integer linear combination of the received symbols has to be computed as a part of the decoding process. In this paper, we propose a method based on HermiteKorkineZolotareff (HKZ) and Minkowski lattice basis reduction algorithms to obtain the integer coefficients for the IF receiver. We show that the proposed method provides a lower bound on the ergodic rate, and achieves the full receive diversity. Suitability of complex LenstraLenstraLovasz (LLL) lattice reduction algorithm (CLLL) to solve the problem is also investigated. Furthermore, we establish the connection between the proposed IF linear receivers and lattice reductionaided MIMO detectors (with equivalent complexity), and point out the advantages of the former class of receivers over the latter. For the 2 × 2 and 4 × 4 MIMO channels, we compare the codedblock error rate and bit error rate of the proposed approach with that of other linear receivers. Simulation results show that the proposed approach outperforms the zeroforcing (ZF) receiver, minimum mean square error (MMSE) receiver, and the lattice reductionaided MIMO detectors. Index Terms—MIMO, integerforcing, lattice reduction, Minkowski reduction, HermiteKorkineZolotareff reduction, complex LenstraLenstraLovasz lattice reduction, linear receivers. I.
Phase precoded computeandforward with partial feedback
"... Abstract—In this work, we propose phase precoding for the computeandforward (CoF) protocol. We derive the phase precoded computation rate and show that it is greater than the original computation rate of CoF protocol without precoder. To maximize the phase precoded computation rate, we need to ‘jo ..."
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Abstract—In this work, we propose phase precoding for the computeandforward (CoF) protocol. We derive the phase precoded computation rate and show that it is greater than the original computation rate of CoF protocol without precoder. To maximize the phase precoded computation rate, we need to ‘jointly ’ find the optimum phase precoding matrix and the corresponding network equation coefficients. This is a mixed integer programming problem where the optimum precoders should be obtained at the transmitters and the network equation coefficients have to be computed at the relays. To solve this problem, we introduce phase precoded CoF with partial feedback. It is a quantized precoding system where the relay jointly computes both a quasioptimal precoder from a finite codebook and the corresponding network equations. The index of the obtained phase precoder within the codebook will then be fedback to the transmitters. A “deep hole phase precoder ” is presented as an example of such a scheme. We further simulate our scheme with a lattice code carved out of the Gosset lattice and show that significant coding gains can be obtained in terms of equation error performance. Index Terms—Computeandforward, lattice codes, phase precoding. I.