Fronthauling for Cloud-RAN and distributed antenna systems

Alister Burr, Dept of Electronic Engineering, University of York

alister.burr@york.ac.uk

Motivation▪ Next generation wireless networks (5G and beyond) are

likely to become ultra-dense

– serving very large numbers of users/devices via even larger numbers of access points

▪ This has led to the cloud-RAN concept

– where antennas are separated from baseband processing, which is moved to “the cloud”

▪ This simplifies access points, concentrates processing, brings antennas closer to users, enables cooperation between access points

▪ However it requires signals to be forwarded to BBU via a fronthaul network

BBU

RRU

Fronthaul transmission▪ The simplest option for fronthaul transmission is direct digitisation of

received signals

– use CPRI standard to transmit digitised signals

– however typically this results in a load on fronthaul network >10 Gbps, many times the total user data rate

▪ Can invoke different physical layer splits to reduce this load

– a “higher” split reduces fronthaul load but also reduces benefits of cooperation

FFT

Spatial

Pro

cessing

Demod

Demod

Decode

Decode

RF

RF

RF

RF

RRU BBU

Alternative approaches

a) Signal sampling: digitise after RF

b) Resource block sampling: digitise after FFT

c) Per-layer sampling: digitise after spatial processing

d) Soft-bit sampling: quantise soft output of demodulator

e) Compress-and-forward: exploit correlation of signals at RRUs to perform Slepian-Wolf or Wyner-Ziv distributed source compression

f) Physical layer network coding/compute-and-forward

FFT

Spatial

Pro

cessing

Demod

Demod

Decode

Decode

RF

RF

RF

RF

(a) (b) (c) (d)

Performance –different “splits”

▪ Digitise after RF

– various quantisation levels (4-10 ‘extra bits’)

▪ Digitise after RF compared with digitise after spatial processing

– 2 ‘extra bits’

Compress and Forward▪ Signal at two relays is correlated because it

originates from the same data source(s)

▪ We can exploit this to reduce the amount of data to send over fronthaul, using Wyner-Ziv quantisation:

▪ Encoder first quantises the received signal (red point) to closest of the 8 quantization centres j = 4

▪ Encoder sends j mod-M

– where M = 2 here, so send 0 (one bit)

– hence destination knows j {0, 2, 4, 6}

▪ Destination knows s1 and p(s2|s1)

– hence selects most probable j, i.e. j = 4

SR1

R2

0 1 2 3 4 5 6 7 s2

s2

p(s2|s1)

s1

s1

s2

Compute and Forward▪ In ultra-dense network multiple user signals

will be received at multiple APs (RRUs)▪ It is likely that no individual AP will be able to

decode a given user’s data– but it may be able to decode some

combined function of several users’ data symbols– we suppose that an AP first multiplies its received signal by

some factor , – then rounds it to an integer value and forwards the result

– D then recovers source data by combining these values:

S1

SK

R1

RL

x1

xK

h11

hL1

h1K

y1

hLK

D

yL

1y

Ly

1 1

round roundK K

l l lk k l lk k l

k k

y y h x z a x

1ˆ

y Ax ε

x A y

Performance▪ End-to-end FER performance using

physical layer network coding (PNC) of which C&F is one form

– compared with soft bit quantisation

– note total fronthaul load with C&F/PNC is the same as the total user data rate

0 5 10 15 20 25 3010

-3

10-2

10-1

100

SNR [dB]F

ER

Ideal CoMP (MU-MIMO+Joint ML): unlimited BH

Adaptive PNC (44 binary Matrix): 4bits BH

CoMP (Distri. ML+ 4bit Quant. LLR Comb.): 16bits BH

CoMP (Distri. ML+ 2bit Quant. LLR Comb.): 8bits BH

C&F and number theory▪ The rounding error at the APs in Compute and Forward depends on

how well the actual channel coefficients ℎ𝑙𝑘 can be approximated by the fraction 𝑎𝑙𝑘/𝛼

– this is a number-theoretic problem known as Diophantine approximation

▪ It also turns out that the optimum capacity in a C&F scheme (called the computation rate) is achieved using signals inspired by latticesin multiple dimensions

– these also have strong connections with algebraic number theory

– also related to lattice quantisation of received signals

Conclusions▪ In ultra-dense networks, cooperation of APs is very

important to provide high capacity-density

▪ Cloud-RAN is an appealing approach to achieve high capacity-density with simple APs/RRUs

– however is likely to require very high fronthaulcapacity

▪ We have reviewed approaches to reduce this load

– choice of “PHY split” is important

▪ Compute and Forward has potential to reduce total fronthaul load to the same level as total user throughput