The Complexity–Rate Tradeoff of Centralized Radio Access Networks

Rost, Peter; Talarico, Salvatore; Valenti, Matthew C.

doi:10.1109/twc.2015.2449321

Cited by 56 publications

(68 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Like in the previous section, the scheduler starts by selecting those users as in a regular mode of operation and checks if the amount of required resources to perform this decoding (ie, iterations), denoted as

\overset{C}{true}

, is less than the capacity C . This estimation of

\overset{C}{true}

can be done following, eg, the model introduced in the work of Rost et al, that provides the computational complexity as a function of the signal‐to‐noise ratio (SNR) and the selected MCS. If

\overset{C}{true} < C

, it proceeds as a regular scheduler would do; otherwise, a different scheduling decision has to be taken (in the previous section, elasticity was achieved by downgrading all the MCS).…”

Section: Cloud‐aware Vnfs In Practicementioning

confidence: 99%

“…To this aim, we simulate a C‐RAN scenario with 5 base stations (BSs) serving 5 users each, where user i (with i ∈1,…,5) is located at a distance i × d 0 , with d 0 being the distance at which the SNR is 17.6 dB. The wireless channel follows the Rayleigh model with γ =3 and σ =6 dB, whereas the MCS complexity model follows the ones presented in the work of Rost et al…”

Section: Cloud‐aware Vnfs In Practicementioning

confidence: 99%

“…To this aim, we simulate a C-RAN scenario with 5 base stations (BSs) serving 5 users each, where user i (with i ∈ 1, … , 5) is located at a distance i × d 0 , with d 0 being the distance at which the SNR is 17.6 dB. The wireless channel follows the Rayleigh model 25 with = 3 and = 6 dB, whereas the MCS complexity model follows the ones presented in the work of Rost et al 23 We first evaluate the degradation function of this scheduling algorithm, as discussed in Section 2.3. To this aim, we first assume an unconstrained scenario to compute the reference performance and required capacity.…”

Section: Scheduling Under Resource Constraintsmentioning

confidence: 99%

See 2 more Smart Citations

The path toward a cloud‐aware mobile network protocol stack

Serrano

Gramaglia

Bega

et al. 2018

Trans Emerging Tel Tech

View full text Add to dashboard Cite

We are currently observing the softwarization of communication networks, where network functions are translated from monolithic pieces of equipment to programs running over a shared pool of computational, storage, and communication resources. While it is clear that almost any softwarization improves flexibility (eg, the ability to instantiate more servers to cope with increasing traffic demand), in this paper, we advocate for a complete redesign of the communications protocol stack, instead of a mere translation of hardware functions into software. We discuss 2 drivers for this cloud‐aware redesign: (1) relaxing the tight interactions between functions and (2) supporting a graceful degradation of the service when resources become scarce. The potential benefits of this redesign are illustrated with the numerical evaluation of one use case.

show abstract

\overset{C}{true}

, is less than the capacity C . This estimation of

\overset{C}{true}

can be done following, eg, the model introduced in the work of Rost et al, that provides the computational complexity as a function of the signal‐to‐noise ratio (SNR) and the selected MCS. If

\overset{C}{true} < C

, it proceeds as a regular scheduler would do; otherwise, a different scheduling decision has to be taken (in the previous section, elasticity was achieved by downgrading all the MCS).…”

Section: Cloud‐aware Vnfs In Practicementioning

confidence: 99%

Section: Cloud‐aware Vnfs In Practicementioning

confidence: 99%

Section: Scheduling Under Resource Constraintsmentioning

confidence: 99%

See 1 more Smart Citation

The path toward a cloud‐aware mobile network protocol stack

Serrano

Gramaglia

Bega

et al. 2018

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…If either of these situations arise, a computational outage is said to occur. These computational outages are just as detrimental to wireless systems as outages caused by fading and interference [10].…”

Section: Overview Of C-ranmentioning

confidence: 99%

Complexity-aware scheduling for an LDPC encoded C-RAN uplink

Whetzel

Valenti

2017

2017 51st Annual Conference on Information Sciences and Systems (CISS)

Self Cite

View full text Add to dashboard Cite

Abstract-Centralized Radio Access Network (C-RAN) is a new paradigm for wireless networks that centralizes the signal processing in a computing cloud, allowing commodity computational resources to be pooled. While C-RAN improves utilization and efficiency, the computational load occasionally exceeds the available resources, creating a computational outage. This paper provides a mathematical characterization of the computational outage probability for low-density parity check (LDPC) codes, a common class of error-correcting codes. For tractability, a binary erasures channel is assumed. Using the concept of density evolution, the computational demand is determined for a given ensemble of codes as a function of the erasure probability. The analysis reveals a trade-off: aggressively signaling at a high rate stresses the computing pool, while conservatively backing-off the rate can avoid computational outages. Motivated by this tradeoff, an effective computationally aware scheduling algorithm is developed that balances demands for high throughput and low outage rates.

show abstract

“…One issue of the centralized decoding is that it requires significant computation complexity. For example, if computation resources in the BBU cloud are statically multiplexed, sharing huge amount of data between BBUs may cause a computational outage [3]. For a user that wants high-rate communication, this shortcoming is worth to endure since it compensates high data rate.…”

Section: Introductionmentioning

confidence: 99%

Low Complexity Antenna Selection for Low Target Rate Users in Dense Cloud Radio Access Networks

Park

Heath

2016

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

We propose a low complexity antenna selection algorithm for low target rate users in cloud radio access networks. The algorithm consists of two phases: In the first phase, each remote radio head (RRH) determines whether to be included in a candidate set by using a predefined selection threshold. In the second phase, RRHs are randomly selected within the candidate set made in the first phase. To analyze the performance of the proposed algorithm, we model RRHs' and users' locations by a homogeneous Poisson point process, whereby the signal-tointerference ratio (SIR) complementary cumulative distribution function is derived. By approximating the derived expression, an approximate optimum selection threshold that maximizes the SIR coverage probability is obtained. Using the obtained threshold, we characterize the performance of the algorithm in an asymptotic regime where the RRH density goes to infinity. The obtained threshold is then modified depending on various algorithm options. A distinguishable feature of the proposed algorithm is that the algorithm complexity keeps constant independent to the RRH density, so that a user is able to connect to a network without heavy computation at baseband units.

show abstract

The Complexity–Rate Tradeoff of Centralized Radio Access Networks

Cited by 56 publications

References 17 publications

The path toward a cloud‐aware mobile network protocol stack

The path toward a cloud‐aware mobile network protocol stack

Complexity-aware scheduling for an LDPC encoded C-RAN uplink

Low Complexity Antenna Selection for Low Target Rate Users in Dense Cloud Radio Access Networks

Contact Info

Product

Resources

About