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By Eduardo Castañeda

Radio Resource Management for

Millimeter Wave & Massive MIMO

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The mmWave Channel

Directional antennas compensate path loss (common assumption)

LoS (beam alignment) + NLoS (tracking), both functions of the length of the link (d) in/outdoor

Dominant LoS strong shadowing + outage

Cluster multipath structure, small number of clusters, <10 typically, (sparse - low rank channel)

SINR: array pattern, beamwidth, directivity gain, side/back lobe gain

Spatio-temporal directivity: AoD and AoA knowledge, inst. & statistics

𝑛𝑊 log2 1 + SINR : wideband increase spectral noise density

𝒅

𝒏

𝒏

3

The mmWave Channel

Blockage refers to high penetration loss due to obstacles and cannot be solved by increasing the power.

Indoor covergare > outdoor coverage

NLoS/LoS propagation laws for modeling links

Deafness occurs when the main lobes at both transmitter and receiver do not point to each other.

Narrow beams reduce inter-beam interferences and performance is noise limited - like (outdoor). Tx-Rx beam mismatch rapidly degrades performance

It’s mitigated by searching alternative beams or spatial directions. (overhead, codebook size, complexity)

𝝉 T-𝝉

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Heterogeneous Bands

Millimeter waves can be used for high data rates.

Microwave can be used for control and signaling, since they provide more reliability. Those services require low rates.

Robust services over microwaves, e.g., broadcasting and network synchronization.

Decoupling Control/User planes, or UL in microW and DL in mmW

Fall-back tradeoff: sending control messages over mm or microwaves.

Swtiching (avoiding) bands with min (max) RSSI/RSRP/RSRQ (received signal strength indicator, power,quality) distance and Tx power

6GHz 28GHz 72GHz

39GHz

Multipath + NLoS

EV

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Ultra Dense Networks

Characteristics

Cellular technology is roaming +

reuse ( OFDMA, frequency reuse

and resource partitioning )

Coverage (LoS for short-range &

NLoS for outdoor), low power and

offloading

Enhanced capacity: bandwidth,

directivity (sector/beamwidth

optimization), less interference

Backhaul

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Resource and Interference ManagementS

ch

ed

ulin

g/u

se

r a

sso

cia

tio

n • UEs < RF chains

• Coherent time/ bandwidth time-freq allocation

• RSS, QoS, load, etc.

• Non-uniform UE distribution

• Bias for load balancing tier-level (HetNets)

• UL-DL asymmetry

• Complexity vs optimality tradeoff, deployment-based performance

Be

am

form

ing • Channel Covariance

spatial allocation based on 2nd order statistics

• Analog beamforming: spatial resources to the best UEs

• UL-DL channel reciprocity pilots

• Beam selection and tracking (codebooks)

• Even IEEE 802.11ac TDD requires feedback for frame sync!

Inte

rfe

ren

ce • Centralized ICI

coordination for multi-tier nets

• On-demand interference management

• Omnidirectional control channels: broadcasting, sync, estimation

• HetNets: users, QoS, power, loads, antennas

• Centralized vs distributed schemes: tradeoff

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Beamforming SchemesC

SIT • Open loop: channel

sounding + codebooks

• Closed loop: i) Feedback BF matrix w/wo compression. ii) feedback CSI

• Compressive sensing and sparcity

• Massive MIMO: long term CSIT is needed

Ou

tdo

or

BF • Analog: phase shift +

constant gain; available commercialy!

• Digital: SVD, ZF, MF

• Hybrid: sub-array, full-array, and more architectures

• Hybrid is massive MIMO oriented

• ~10-200 m coverage in field trials W

LA

N -

WP

AN • Sector level sweep:

omni ↔ directional

• Beam refiment phase (SINR based)

• Beam tracking: continous channel estimation

• Multipath defines # layers per UE

• Alignment overhead -Throughput tradeoff

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Hybrid Bemaforming / Combining Architectures

Z. Gao, L. Dai, D. Mi, Z. Wang, M. A. Imran, and M. Z. Shakir, “MmWave massive-MIMO-based wireless backhaul for

the 5G ultra-dense network,” IEEE Wirel. Commun., vol. 22, no. 5, pp. 13–21, 2015.

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Massive MIMO: performance f(SINR), BF, interference

𝑅𝑘,𝑗 = 1 −𝜏

𝑇log2 1 + SINR𝑘,𝑗

SINR𝑘,𝑗𝑍𝐹 =

1 − 𝑣𝑗 𝑔𝑘,𝑗2 SNR𝑗/𝑣𝑗

𝜂 + 𝜎2𝑔𝑘,𝑗2 SNR𝑗 + 𝑙∈𝒥:𝑙≠𝑗 𝑔𝑘,𝑙

2 SNR𝑙 + 𝑙∈𝒥 𝑞(𝑘) :𝑙≠𝑗

1 − 𝑣𝑙 𝑔𝑘,𝑙2 SNR𝑙 /𝑣𝑙

SINR𝑘,𝑗𝑀𝐹 =

𝑔𝑘,𝑗2 SNR𝑗/𝑣𝑗

𝜂 + 𝑙∈𝒥 𝑔𝑘,𝑙2 SNR𝑙 +

𝑙∈𝒥 𝑞(𝑘) :𝑙≠𝑗𝑔𝑘,𝑙

2 SNR𝑙 /𝑣𝑙 SNR𝑗 =𝑃𝑗

𝑁0

𝑣𝑗 =𝑆𝑗

𝑀𝑗

Symbols per slot for UL

pilots (overhead)

Symbols per coherent block

user

BS

Number of BS antennas

(# of RF chains)

Streams at BS j

Spatial

load

𝑔𝑘,𝑗2

Pathloss and

shadowing

𝜂

BS power

normalizer

𝒥 𝒥 𝑞(𝑘)

Set of BSs

BSs with same

Pilot sequence q(k)

1/𝜎2

UL SNR

Tx SNR at BS j

Omnidirectional

Antennas: microWave

𝑔𝑘,𝑗2 = 𝑔𝑘,𝑗

2 ∙ 𝑓(AoD, AoA)

Directional

Antennas: mmWave

Data Rate

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Access Mechanisms PHY-CCS

yn

ch

ron

iza

tio

n a

nd

ce

ll searc

h • Primary and secondary signals / Sync signals and cell ID

• Microwaves use omni/semi-directionalbeams, but mmWaves require full-directionalbeams

• SINR can be modeled according to directivity (geometry) S

yste

m in

form

atio

n

extr

actio

n • Parameters: Bandwidth, freq. Bands, Tx antennas, access scheme

• Sinlge or multiple bands

• Dedicated mmWavechannel for signaling and estimation ~28GHz

• On-demand spatial sync: beams

Acce

ss • Contention based

(collition domain) or contention-free based access (dedicated CCH)

• V2V, WLAN, WPAN, e.g. 802.11ad (55-68 GHz) and 802.11.15.3

• Example: backoff due to deafness resources waste

• Directivity #beams vs overhead : tradeoff

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Mobility ManagementH

andover • Handovers in dense

deployments may be frequent

• Limitations of RSS, local load neglected

• Beam mismatch triggers HO

• Dedicated backhaul resources for the BSs in soft or hard HON

ois

e+

De

lay • Increase of

overhead/delay due to reassociation, beam refiment and CSI acquisition

• Cloud cell (centralized arch.) low latency

• Phase noise and Doppler effect increases with 𝑓𝑐

Ro

bu

stn

ess • UE association with

multiple BS.

• Dual connectivity -association but one Tx per UE reduce complexity

• Dynamic cell setting + user centric RRM

[ Athanasiou2015 ]

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Metrics for Cloud Cell FormationU

E t

raffic

de

ma

nd • QoS

• Throughput vs Fairness

• Association to several groups, classess, BSs

• Combinatorial problems and coupled SINRs

BS

-UE

Channel

• Voronoi regions based on RSRP / RSSI not appropriate if load or QoS are considered

• Serving area defined in the angular domain DoA

• Complexity of the objective functions

BS

s loads • Based on the number

of antennas

• Based on the number of users per Tx

• Based on total bits to be delivered

• Energy efficiency becomes relevant if BSs are switched On/Off

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Offloading: User Density + Traffic

Distance-based matching

BS-UE is not effcieint for

HetNets + mmWaves:

power disparity and load

imbalance per transmitters

Cell boundaries are not clear

in hetnets operating in

mmWaves HO beyond

coverage area

1. D2D: proximity services

2. Small cells: operate at high freq.,

e.g. HomeNB. 80% of traffic is

indoor!

3. I-WLAN architecture: WiFi + 3GPP

mobile networks

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Summary

Identified characteristics of the wireless channel in mmWave

Candidate frequency bands for cellular communications and

services

Discussion of several process RM for mmWave:

Scheduling: SINR modeling and parametrization

Beamforming: channel estimation and hybrid architectures

Interference management

Mobility + Handover

Dynamic Cloud Cell formation

Offloading Techniques

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References

1. H. Shokri-Ghadikolaei, C. Fischione, G. Fodor, P. Popovski, and M. Zorzi, “Millimeter Wave Cellular Networks: A MAC Layer

Perspective,” IEEE Trans. Commun., vol. 63, no. 10, pp. 3437–3458, Oct. 2015.

2. S. Kutty and D. Sen, “Beamforming for Millimeter Wave Communications: An Inclusive Survey,” IEEE Commun. Surv.

Tutorials, vol. 18, no. 2, pp. 949–973, Jan. 2016.

3. D. Liu, L. Wang, Y. Chen, M. Elkashlan, K.-K. Wong, R. Schober, and L. Hanzo, “User Association in 5G Networks: A Survey

and an Outlook,” IEEE Commun. Surv. Tutorials, vol. 18, no. 2, pp. 1018–1044, Jan. 2016.

4. R. Baldemair, T. Irnich, K. Balachandran, E. Dahlman, G. Mildh, and Y. Selén, “Ultra-Dense Networks in Millimeter-Wave

Frequencies,” IEEE Commun. Mag., no. January, pp. 202–208, 2015.

5. G. Athanasiou, P. C. Weeraddana, C. Fischione, and L. Tassiulas, “Optimizing Client Association for Load Balancing and

Fairness in Millimeter-Wave Wireless Networks,” IEEE/ACM Trans. Netw., vol. 23, no. 3, pp. 836–850, Jun. 2015.

6. N. Saquib, E. Hossain, Long Bao Le, and Dong In Kim, “Interference management in OFDMA femtocell networks: issues and

approaches,” IEEE Wirel. Commun., vol. 19, no. 3, pp. 86–95, Jun. 2012.

7. S. Kutty and D. Sen, “Beamforming for Millimeter Wave Communications: An Inclusive Survey,” IEEE Commun. Surv.

Tutorials, vol. 18, no. 2, pp. 949–973, Jan. 2016.

8. A. Adhikary, E. Al Safadi, M. K. Samimi, R. Wang, G. Caire, T. S. Rappaport, and A. F. Molisch, “Joint Spatial Division and

Multiplexing for mm-Wave Channels,” IEEE J. Sel. Areas Commun., vol. 32, no. 6, pp. 1239–1255, Jun. 2014.

9. D. Bethanabhotla, O. Y. Bursalioglu, H. C. Papadopoulos, and G. Caire, “Optimal User-Cell Association for Massive MIMO

Wireless Networks,” IEEE Trans. Wirel. Commun., vol. 15, no. 3, pp. 1835–1850, Mar. 2016.

10. S. Yost, “mmWave : Battle of the Bands,” National Instruments, White Paper, 2016.

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