Performance comparison of data-sharing and compression strategies for cloud radio access networks

Patil, Pratik; Dai, Binbin; Yu, Wei

doi:10.1109/eusipco.2015.7362826

Cited by 23 publications

(20 citation statements)

References 4 publications

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“…It is observed from Fig. 6 that in the multi-hop C-RAN, the sum-rate achieved by the data-sharing strategy is higher than that achieved by the compression-based strategy almost for all the values of C. Note that this is in sharp contrast to the previous results in [7], [8], which shows that if the routing strategy over the fronthaul network is not considered, in general the compression-based strategy outperforms the data-sharing strategy in terms of both spectral and energy efficiency. Specifically, in this numerical example, it is observed that in the single-hop C-RAN, the compression-based strategy can provide up to 25% performance gain over the data-sharing strategy.…”

Section: Comparison Between Data-sharing Strategy and Compression-contrasting

confidence: 55%

Cross-Layer Design for Downlink Multihop Cloud Radio Access Networks With Network Coding

Liu

2017

IEEE Trans. Signal Process.

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There are two fundamentally different fronthaul techniques in the downlink communication of cloud radio access network (C-RAN): the data-sharing strategy and the compression-based strategy. Under the former strategy, each user's message is multicast from the central processor (CP) to all the serving remote radio heads (RRHs) over the fronthaul network, which then cooperatively serve the users through joint beamforming; while under the latter strategy, the user messages are first beamformed then quantized at the CP, and the compressed signal is unicast to the corresponding RRH, which then decompresses its received signal for wireless transmission. Previous works show that in general the compression-based strategy outperforms the data-sharing strategy. This paper, on the other hand, points out that in a C-RAN model where the RRHs are connected to the CP via multi-hop routers, data-sharing can be superior to compression if the network coding technique is adopted for multicasting user messages to the cooperating RRHs, and the RRH's beamforming vectors, the user-RRH association, and the network coding design over the fronthaul network are jointly optimized based on the techniques of sparse optimization and successive convex approximation. This is in comparison to the compression-based strategy, where information is unicast over the fronthaul network by simple routing, and the RRH's compression noise covariance and beamforming vectors, as well as the routing strategy over the fronthaul network are jointly optimized based on the successive convex approximation technique. The observed gain in overall network throughput is due to that information multicast is more efficient than information unicast over the multi-hop fronthaul of a C-RAN.

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Section: Comparison Between Data-sharing Strategy and Compression-contrasting

confidence: 55%

Cross-Layer Design for Downlink Multihop Cloud Radio Access Networks With Network Coding

Liu

2017

IEEE Trans. Signal Process.

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“…In contrast, in the compression strategy, the precoding operation is implemented centrally at the CP, which then forwards a compressed version of the analog beamformed signal to the BSs through the backhaul/fronthaul links. The BSs then simply transmit the compressed beamforming signals to the users [9], [30], [33].…”

Section: Data-sharing Versus Compressionmentioning

confidence: 99%

“…However, practical quantizer may be far from the theoretically ideal quantizer. Similar to [33], we introduce a notion of gap to rate-distortion limit, denote as Γ q > 1, to account for the loss due to practical quantizer and formulate the backhaul capacity consumption for BS l as…”

Section: A Problem Formulationmentioning

confidence: 99%

Energy Efficiency of Downlink Transmission Strategies for Cloud Radio Access Networks

Dai

2016

IEEE J. Select. Areas Commun.

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236

216

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This paper studies the energy efficiency of the cloud radio access network (C-RAN), specifically focusing on two fundamental and different downlink transmission strategies, namely the data-sharing strategy and the compression strategy. In the data-sharing strategy, the backhaul links connecting the central processor (CP) and the base-stations (BSs) are used to carry user messages -each user's messages are sent to multiple BSs; the BSs locally form the beamforming vectors then cooperatively transmit the messages to the user. In the compression strategy, the user messages are precoded centrally at the CP, which forwards a compressed version of the analog beamformed signals to the BSs for cooperative transmission. This paper compares the energy efficiencies of the two strategies by formulating an optimization problem of minimizing the total network power consumption subject to user target rate constraints, where the total network power includes the BS transmission power, BS activation power, and load-dependent backhaul power. To tackle the discrete and nonconvex nature of the optimization problems, we utilize the techniques of reweighted ℓ1 minimization and successive convex approximation to devise provably convergent algorithms. Our main finding is that both the optimized data-sharing and compression strategies in C-RAN achieve much higher energy efficiency as compared to the non-optimized coordinated multipoint transmission, but their comparative effectiveness in energy saving depends on the user target rate. At low user target rate, data-sharing consumes less total power than compression, however, as the user target rate increases, the backhaul power consumption for data-sharing increases significantly leading to better energy efficiency of compression at the high user rate regime.Index Terms-Cloud radio access network (C-RAN), datasharing strategy, compression strategy, energy efficiency, power minimization, base-station activation, base-station clustering, beamforming, backhaul power.

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“…By iteratively updating {δ t }, {λ k,t }, and µ with (21), we can obtain the optimal solution to the dual problem (16). Correspondingly, the optimal {V g,t , η g,t } and {C k } to the primal problem (15) is given by substituting the optimal {δ t }, {λ k,t }, and µ into (17)- (19). We summarize this procedure for solving problem (15) in Algorithm 1, which is guaranteed to converge to the global optimum of (16) at a rate of O (1/s), if the step size β s is smaller than the inverse of the Lipschitz constant of ∇D [31].…”

Section: B First-order Algorithm In Dual Domainmentioning

confidence: 99%

“…Despite the challenges mentioned above, this paper optimizes the cache allocation at BSs for a C-RAN with multicluster multicast backhaul, aiming to maximize the content downloading sum-rate of the wireless backhaul under a total cache budget constraint. Since cache placement impacts a much larger timescale than that of channel variations [4], [18], [19], the cache allocation should be optimized based on a large number of potential channel realizations. Furthermore, to maximize the content downloading sum-rate, various channel realizations requires tailored optimal beamformers, which are coupled in the optimization of cache sizes.…”

Section: Introductionmentioning

confidence: 99%

Caching at Base Stations With Multi-Cluster Multicast Wireless Backhaul via Accelerated First-Order Algorithms

Xia

2020

IEEE Trans. Wireless Commun.

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Cloud radio access network (C-RAN) has been recognized as a promising architecture for next-generation wireless systems to support the rapidly increasing demand for higher data rate. However, the performance of C-RAN is limited by the backhaul capacities, especially for the wireless deployment. While C-RAN with fixed BS caching has been demonstrated to reduce backhaul consumption, it is more challenging to further optimize the cache allocation at BSs with multi-cluster multicast backhaul, where the inter-cluster interference induces additional non-convexity to the cache optimization problem. Despite the challenges, we propose an accelerated first-order algorithm, which achieves much higher content downloading sum-rate than a second-order algorithm running for the same amount of time.Simulation results demonstrate that, by simultaneously delivering the required contents to different multicast clusters, the proposed algorithm achieves significantly higher downloading sum-rate than those of time-division single-cluster transmission schemes. Moreover, it is found that the proposed algorithm allocates larger cache sizes to the farther BSs within the nearer clusters, which provides insight to the superiority of the proposed cache allocation.Index Terms-Caching, cloud radio access network (C-RAN), first-order algorithm, large-scale nonsmooth nonconvex optimization, multi-cluster multicast beamforming (MCMB), wireless backhaul.

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Performance comparison of data-sharing and compression strategies for cloud radio access networks

Cited by 23 publications

References 4 publications

Cross-Layer Design for Downlink Multihop Cloud Radio Access Networks With Network Coding

Cross-Layer Design for Downlink Multihop Cloud Radio Access Networks With Network Coding

Energy Efficiency of Downlink Transmission Strategies for Cloud Radio Access Networks

Caching at Base Stations With Multi-Cluster Multicast Wireless Backhaul via Accelerated First-Order Algorithms

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