An uplink-downlink duality for cloud radio access network

Liu, Liang; Patil, Pratik; Yu, Wei

doi:10.1109/isit.2016.7541570

Cited by 22 publications

(19 citation statements)

References 11 publications

(9 reference statements)

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“…Alternatively, one may consider finding the worst-case noise under given input distributions, e.g., Gaussian, and rate-constrained compression at the relays. Also, although it is still not clear whether the known multiaccess/broadcast (MAC/BC) duality extends to one between uplink and downlink CRAN models in general [43], it is expected that the approach of this paper be instrumental towards characterizing the effect of the relay nodes being oblivious to the actual codebooks used by the users in the downlink setting, especially in the case in which the connection between the CP and the relay nodes are not wired.…”

Section: Discussionmentioning

confidence: 99%

On the Capacity of Cloud Radio Access Networks With Oblivious Relaying

Aguerri

Zaidi

Caire

et al. 2019

IEEE Trans. Inform. Theory

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We study the transmission over a network in which users send information to a remote destination through relay nodes that are connected to the destination via finite-capacity error-free links, i.e., a cloud radio access network. The relays are constrained to operate without knowledge of the users' codebooks, i.e., they perform oblivious processing. The destination, or central processor, however, is informed about the users' codebooks. We establish a single-letter characterization of the capacity region of this model for a class of discrete memoryless channels in which the outputs at the relay nodes are independent given the users' inputs. We show that both relaying à-la Cover-El Gamal, i.e., compress-and-forward with joint decompression and decoding, and "noisy network coding" are optimal. The proof of the converse part establishes, and utilizes, connections with the Chief Executive Officer source coding problem under logarithmic loss distortion measure. Extensions to general discrete memoryless channels are also investigated. In this case, we establish the inner and outer bounds on the capacity region. For memoryless Gaussian channels within the studied class of channels, we characterize the capacity region when the users are constrained to time-share among Gaussian codebooks. Furthermore, we also discuss the suboptimality of separate decompression and decoding and the role of time sharing.

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Section: Discussionmentioning

confidence: 99%

On the Capacity of Cloud Radio Access Networks With Oblivious Relaying

Aguerri

Zaidi

Caire

et al. 2019

IEEE Trans. Inform. Theory

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“…For various choices of σ 2 (P ), (16), (18), (19), and (20) enable us to make several statements about the behaviors of the ratios R NCF sum /R ∞ sum and C * /R ∞ sum at high SNR. These are summarized in Table II.…”

Section: Comparisons With Fronthaul-unlimited Uplinkmentioning

confidence: 99%

Capacity Scaling for Cloud Radio Access Networks with Limited Orthogonal Fronthaul

Ganguly

Kim

2019

2019 IEEE International Symposium on Information Theory (ISIT)

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Uplink and downlink cloud radio access networks are modeled as two-hop K-user L-relay networks, whereby small base-stations act as relays for end-to-end communications and are connected to a central processor via orthogonal fronthaul links of finite capacities. Simplified versions of network compressforward (or noisy network coding) and distributed decode-forward are presented to establish inner bounds on the capacity region for uplink and downlink communications, that match the respective cutset bounds to within a finite gap independent of the channel gains and signal to noise ratios. These approximate capacity regions are then compared with the capacity regions for networks with no capacity limit on the fronthaul. Although it takes infinite fronthaul link capacities to achieve these "fronthaul-unlimited" capacity regions exactly, these capacity regions can be approached approximately with finite-capacity fronthaul. The total fronthaul link capacities required to approach the fronthaul-unlimited sum-rates (for uplink and downlink) are characterized. Based on these results, the capacity scaling law in the large network size limit is established under certain uplink and downlink network models, both theoretically and via simulations. A. Uplink C-RANSeveral coding schemes have been proposed in the literature for the uplink C-RAN with K users (senders) and L relays. Zhou and Yu [5] applied the network compress-forward relaying scheme [7] to this model and showed, by optimizing over quantizers, that under some symmetry assumptions, this scheme achieves the optimal sum-rate within L/2 bits per real dimension uniformly over all K and all channel parameters. Sanderovich, Someskh, Poor, and Shamai [8] used the same scheme and analyzed the large-user asymptotics (i.e., the scaling law) of symmetric achievable rates when all fronthaul links have equal capacities. Zhou, Xu, Yu, and Chen [6] subsequently showed that under a sum-capacity constraint on the fronthaul links, the coding scheme in [5] and [8] can be simplified through successive cancellation decoding, generalizing an earlier result for the single-sender multiple relay network [9]. Aguerri and Zaidi [10] proposed a hybrid coding scheme of network compress-forward and compute-forward [11], and demonstrated that it outperforms the better of the two in general. Aguerri, Zaidi, Caire, and Shamai [12] specialized the noisy network coding scheme [13] to the uplink C-RAN, the achievable rate region of which coincides with that of network compress-forward [5], [8]. The most general outer bound on the capacity region of the uplink C-RAN can be obtained by specializing the cutset bound [14]; see, for example, [5] and the references therein, as well as Proposition 2 in this paper. The cutset bound has been further tightened under additional assumptions. Aguerri, Zaidi, Caire, and Shamai [12] studied the uplink C-RAN in which the relays are oblivious of the codebooks of the senders, and demonstrated that network compress-forward (or noisy network coding) achieves the capacit...

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“…See [37], [38] for further details. Very recent work has also established uplinkdownlink duality for compression-based strategies for cloud radio access networks [39].…”

Section: A Related Workmentioning

confidence: 99%

Uplink-Downlink Duality for Integer-Forcing

Nazer

Shamai

2018

IEEE Trans. Inform. Theory

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Abstract-Consider a Gaussian multiple-input multiple-output (MIMO) multiple-access channel (MAC) with channel matrix H and a Gaussian MIMO broadcast channel (BC) with channel matrix H T . For the MIMO MAC, the integer-forcing architecture consists of first decoding integer-linear combinations of the transmitted codewords, which are then solved for the original messages. For the MIMO BC, the integer-forcing architecture consists of pre-inverting the integer-linear combinations at the transmitter so that each receiver can obtain its desired codeword by decoding an integer-linear combination. In both cases, integerforcing offers higher achievable rates than zero-forcing while maintaining a similar implementation complexity. This paper establishes an uplink-downlink duality relationship for integerforcing, i.e., any sum rate that is achievable via integer-forcing on the MIMO MAC can be achieved via integer-forcing on the MIMO BC with the same sum power and vice versa. Using this duality relationship, it is shown that integer-forcing can operate within a constant gap of the MIMO BC sum capacity. Finally, the paper proposes a duality-based iterative algorithm for the non-convex problem of selecting optimal beamforming and equalization vectors, and establishes that it converges to a local optimum.

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An uplink-downlink duality for cloud radio access network

Cited by 22 publications

References 11 publications

On the Capacity of Cloud Radio Access Networks With Oblivious Relaying

On the Capacity of Cloud Radio Access Networks With Oblivious Relaying

Capacity Scaling for Cloud Radio Access Networks with Limited Orthogonal Fronthaul

Uplink-Downlink Duality for Integer-Forcing

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