A zero-forcing partial decode-and-forward scheme for the Gaussian MIMO relay channel

Gerdes, Lennart; Weiland, Lorenz; Utschick, Wolfgang

doi:10.1109/icc.2013.6655064

Cited by 11 publications

(15 citation statements)

References 16 publications

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“…These results have been improved by more advanced coding schemes (compress-forward and partial decode-forward) often with suboptimal decoding rules [10], [11], [12]. The usual focus of this line of work, however, has been on the optimization of resources (power and bandwidth) for practical implementations and on numerical computation of resulting capacity bound (see also [13]). Consequently, no clean capacity approximation result has been obtained in the literature.…”

Section: Introductionmentioning

confidence: 99%

The Approximate Capacity of the MIMO Relay Channel

Jin

Kim

2017

IEEE Trans. Inform. Theory

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Abstract-The capacity bounds are studied for the multipleantenna real full-duplex Gaussian relay channel with t1 transmitting antennas at the sender, r2 receiving and t2 transmitting antennas at the relay, and r3 receiving antennas at the receiver. It is shown that compress-forward and partial decode-forward achieve within (1/2)(min(t1 + t2, r3) + r2) bits and (1/2) min(t1, r2) bits, respectively, from the cutset bound. Unlike the single-antenna case, partial decode-forward can be arbitrarily better than optimal selection between decode-forward and direct transmission. Similar gap results for half-duplex models are briefly discussed. I. INTRODUCTIONThe relay channel, whereby point-to-point communication between a sender and a receiver is aided by a relay, is an important building block for cooperative wireless communication. Introduced by van der Meulen [1], this channel model has been studied extensively in the literature, including the now classical paper by Cover and El Gamal [2]. Nonetheless, even for the most basic Gaussian relay channel, the problem of characterizing the capacity in a computable form remains open under any nondegenerate channel gain and power constraint. Consequently, a large body of the literature has been devoted to the study of bounds on the capacity. Reminiscent of the maxflow min-cut theorem [3], the cutset bound was developed by Cover and El Gamal [2] that sets an upper bound on the capacity. There are myriads of coding schemes [4] and corresponding lower bounds on the capacity.The main focus of this paper is on two canonical coding schemes, compress-forward [2, Th. 6] and partial decodeforward [2, Th. 7] for Gaussian relay channels; see also [5, Ch. 16] for the detailed descriptions of the coding schemes. For the single-antenna full-duplex real Gaussian relay channel, compress-forward and partial decode-forward, respectively, achieve within half a bit from the cutset bound [6], [7], providing a half-bit approximation of the capacity. Moreover, partial decode-forward, which is superposition of decodeforward and direct transmission, reduces to the better of the two [8].Paralleling these results for the single-antenna model, we study the performance of compress-forward and partial decode-forward for multiple-antenna (also known as multipleinput multiple-output or MIMO) Gaussian relay channels. Capacity bounds for MIMO relay channels have been studied in numerous papers. For example, by convex programming techniques, Wang, Zhang, and Høst-Madsen [9] derived upper and lower bounds based on looser versions of cutset bound

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Section: Introductionmentioning

confidence: 99%

The Approximate Capacity of the MIMO Relay Channel

Jin

Kim

2017

IEEE Trans. Inform. Theory

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“…Note that these initializations correspond to the two special cases of the PDF scheme. The third initialization is given by the optimizer of the PDF scheme using ZF based on the singular value decomposition (SVD) of H SR [10]. The rate achieved with this initialization is denoted by R SVD IAA with R SVD IAA ≥ R SVD ≥ max{R DF ; R P2P }, cf.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…The rate achieved with this initialization is denoted by R SVD IAA with R SVD IAA ≥ R SVD ≥ max{R DF ; R P2P }, cf. [10], where R SVD denotes the rate achieved by the PDF scheme using ZF based on the SVD of H SR . Fig.…”

Section: Numerical Resultsmentioning

confidence: 99%

“…We hereby exemplarily demonstrate a valuable method for handling OPs with non-convex rate constraints which turns the original OP into a series of approximated convex OPs and provides a Karush-Kuhn-Tucker (KKT) solution of the original OP. Furthermore, initializing the IAA with the heuristic solutions of the original non-convex OP derived in [10] provides a valuable practical tool for the justification of the proposed suboptimal PDF scheme using ZF.…”

Section: * Corresponding Authormentioning

confidence: 99%

“…In our companion work [10], we derived a suboptimal PDF scheme using a zero-forcing (ZF) receiver at the relay. Assuming Gaussian channel inputs and perfect CSI at every node, the PDF rate using ZF can be evaluated by solving sets of convex OPs.…”

Section: * Corresponding Authormentioning

confidence: 99%

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Partial decode-and-forward rates for the Gaussian MIMO relay channel: Inner approximation of non-convex rate constraints

Weiland¹,

Gerdes²,

Utschick³

2013

2013 IEEE 14th Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

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In this paper, we consider achievable partial decodeand-forward (PDF) rates for the Gaussian multiple-input multipleoutput (MIMO) relay channel. The PDF scheme generalizes the decode-and-forward (DF) scheme as it allows to optimize the amount of information that is transmitted in cooperation with the relay. For the Gaussian channel case, the optimal channel inputs for the PDF scheme are unknown, and even if Gaussian channel inputs are used, the resulting optimization problem (OP) is non-convex. Thus, suboptimal approaches are necessary in order to efficiently evaluate PDF rates. In this paper, we apply the so-called Inner Approximation Algorithm (IAA). By successively refining an approximation of the original OP, we are able to evaluate PDF rates by solving a sequence of convex OPs. Our simulation results show that these suboptimal PDF rates are able to outperform the rates achieved by point-to-point (P2P) transmission and the DF scheme. For scenarios where the source is equipped with more antennas than the relay, the suboptimal PDF rates even approach the cut-set bound (CSB).

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Performance Analysis of Two Multi-antennas Relays Cooperation with Hybrid Relaying Scheme in the Absence of Direct Link for Cooperative Wireless Networks

Pattepu

2017

Advances in Intelligent Systems and Computing

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A zero-forcing partial decode-and-forward scheme for the Gaussian MIMO relay channel

Cited by 11 publications

References 16 publications

The Approximate Capacity of the MIMO Relay Channel

The Approximate Capacity of the MIMO Relay Channel

Partial decode-and-forward rates for the Gaussian MIMO relay channel: Inner approximation of non-convex rate constraints

Performance Analysis of Two Multi-antennas Relays Cooperation with Hybrid Relaying Scheme in the Absence of Direct Link for Cooperative Wireless Networks

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