Energy Efficient Effective Capacity for 5G Networks Eduard - - PowerPoint PPT Presentation

Energy Efficient Effective Capacity for 5G Networks

Eduard Jorswieck Communications Theory - 5G Lab Germany

Communications Theory

joint work with

• Dr. Martin Mittelbach (TU Dresden)
• Dr. Rami Mochaourab (KTH Access Center)
• Dr. Christian Isheden (Actix Dresden)
• Mahnaz Sinaie (guest PhD student, Tehran, Iran)
• Prof. Emil Björnson (Linköping, SE)
• Prof. Merouane Debbah (Supelec, FR)
• Prof. Björn Ottersten (KTH & SnT, UoL)

Cellular Roadmap of USPs

2G – 1992

Voice Messages

3G – 2002

+ Data + Positioning

4G – 2012

+ Video everything + 3D Graphics

5G – 2022

+ Tactile Internet + massive M2M

+ Tb/s

+ “carrier grade”

+ safe & secure

• G. Fettweis and Siavash Alamouti. “5G: Personal Mobile Internet beyond What Cellular

Did to Telephony”, IEEE Communications Magazine, 52.2 (2014): 140-145.

5G - Massive Requirements

State of the art

Massive reduction in latency Massive sensing Massive resilience Massive safety and security Massive fractal heterogeneity Massive throughput

>"10Gbit/s"per"user

<"1ms"RTT

>"10k"sensors"per"cell

<"​10↑−8 "outage" <"​10↑−12 "security"

10x10$heterogenity$

Problem Statement

• Conflicting performance metrics:
• Data rate / throughput
• Delay / latency
• Energy efficiency
• Multi-Objective Programming (MOP) problem
• E. Björnson, E. Jorswieck, M. Debbah, B. Ottersten, "Multi-Objective Signal Processing

Optimization: The Way to Balance Conflicting Metrics in 5G Systems", IEEE Signal Processing Magazine, vol. 31, no. 6, pp. 14-23, Nov. 2014.

Preliminary Results I

• MISO / MIMO average effective capacity maximization

with statistical channel state information at the transmitter

• E. Jorswieck, R. Mochaourab, and M. Mittelbach, “Effective Capacity Maximization in

Multi-Antenna Channels with Covariance Feedback”, IEEE Trans. on Wireless Communications, vol. 9, no. 10, pp. 2988 – 2993, Oct. 2010.

max

Q⌫0,tr(Q)P − 1

θT log E ⇥ exp

• −θ log det
• I + ρHQHH⇤

• Important result for solving the programming

problem: is concave in Q.

• The optimal eigenvectors diagonalize the transmit

correlation matrix.

• The remaining vector (eigenvalues) programming

problem can be solved efficiently.

Preliminary Results II

Φ(Q) = −E ⇥ det

• I + ρHQHH⇤

Preliminary Results III

Efficiency of Effective Capacity

• Efficiency is a measurable concept, quantitatively

determined by the ratio of output to input.

• Total energy consumed contains the contribution

from the active as well as passive RF transceiver parts. Effective Capacity Total Energy Consumed

• G. Miao, N. Himayat, Y. Li, and A. Swami, “Cross-layer optimization for energy-efficient wireless

communications: a survey,” Wireless Commun. and Mobile Computing, vol. 9, no. 4, pp. 529–542, 2009

Maximization of EEC in SISO

• Programming problem:
• Average can be computed often in closed form
• Characterization of optimal solution is possible
• X. Chen R. Q. Hu, G. Wu and Q. C. Li, “Tradeoff between energy efficiency and spectral efficiency in a

delay constrained wireless system”, Wireless Communications and Mobile Computing, 2014.

• W. Cheng, X. Zhang, H. Zhang, „Joint Spectrum and Power Efficiencies Optimization for Statistical QoS

Provisionings Over SISO/MIMO Wireless Networks“, IEEE Journal Selected Areas in Communications, vol. 31, no. 5, May 2013.

max

p≥0 −log E

⇥ (1 + ρpα)−θ⇤ θ(pc + p)

Tradeoff Delay and Energy Efficiency

−20 −15 −10 −5 5 10 15 20 −20 −15 −10 −5 5 10 15 20 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

inverse noise variance [dB] delay parameter θ [dB] Efficient Effective Capacity [bit/s/Hz]

Extension to EEC in MIMO

• Maximize Efficient Effective Capacity in MIMO with

statistical CSIT:

• Parametric convex program
• Optimum at —> Dinkelbach algorithm

max

Q⌫0,tr(Q)P −log E

⇥ exp(−θ log det

• I + ρHQHH

θT(tr(Q) + Pc)

f(λ) = max

Q⌫0,tr(Q)P − log E

⇥ exp(−θ log det

• I + ρHQHH

− λθT(tr(Q) + Pc)

f(λ∗) = 0

• C. Isheden, Z. Chong, E. Jorswieck, G. Fettweis, "Framework for Link-Level Energy Efficiency Optimization with

Informed Transmitter", IEEE Trans. on Wireless Communications, vol. 11, no. 8, pp. 2946-2957, Aug. 2012.

Impact of system parameters

• n the Energy / Delay tradeoff
• Spatial dimensions: number of antennas, impact
• f spatial correlation, hardware impairments

(massive MIMO), etc.

• Spectral dimensions: multi-carrier (OFDM),

generalized multi-carrier, correlated carriers, power delay profile

• Temporal dimensions: from fast-fading (iid) to

Markov models

Further (visionary) Challenges

• Multi-User Systems, e.g., multi-antenna multiple

access channel (MAC)

is , espec- all vectors ynes rates stability called

• f

ithout could

• uld

gion

Global Efficient Effective Sum-Capacity

• Maximize Efficient Effective Sum-Capacity
• Idea: Combine outer Dinkelbach algorithm with

inner statistical iterative water filling

• Implementation, interpretation, assessment open…

max

Qk⌫0,tr(Qk)Pk −

log E  exp(−θ log det ✓ I + ρ

K

P

k=1

HkQkHH

k

◆◆ θT(

K

P

k=1

tr(Qk) + Pc)

Further challenges

• Discuss operational meaning of efficient effective

sum capacity in the multiuser setup:

• MAC versus broadcast channel (BC) (versus

Interference Channel)

• Distributed implementation via game theory
• Standard function framework to show global

stability

Thank you for your attention

http://ifn.et.tu-dresden.de/tnt/

Backup Slides

Proof of Concavity

Proof of Optimality

• f Diagonalization

Single-stream BF

0.5 1 1.5 2 2.5 3 3.5 4 500 1000 1500 2000 2500 Instantaneous rate Number of realizations (out of 100.000)

50 100 150 200 10

−2

10

−1

10 10

1

time slot t instantaneous rate ergodic capacity maximization effective capacity maximization (θ=3)