Fundamental Limits of Communication Systems Using Renewable Energy

Fundamental Limits for Communication Systems with
Renewable Energy Sources

Vinod Sharma
Dept of Electrical Communication Engineering,
Indian Institute of Science
Bangalore, India

Joint work with Utpal Mukherji, R. Rajesh, Vinay Joseph, P. Viswanath
and Deekshith K

April 5, 2012

Vinod Sharma, Indian Institute of Science, ECE ()
Fundamental Limits for Communication Systems with Renewable Energy2012
April 5, Sources 1 / 27

Outline

Introduction
Green Communications
Green Communications in India
Communication system design with renewal energy
Single node: Point to Point
Information theoretic
Queuing theoretic
MAC
Multihop
Conclusions


Introduction

2% of total electrical energy globally consumed in data centers and
communication equipment.
Predominant ICT energy consumed by Wireless networks.
BS consumes 50% of overall power consumed in wireless networks.
One BS consumes 2Kwatt
50 − 80% to RF
5 − 15% SP
10 − 25% Air conditioner
A medium sized (12 − 15K cell sites) cellular network consumes
equivalent of 1, 70, 000 homes.
Every year 1, 20, 000 new BSs added world wide.
Total energy consumed by one cell phone is 0.1Watt.
Manufacturing and disposal of cell phones consume similar amount.
This will have signiﬁcant environmental impact.

Green Communication

Saving Energy for specific throughput and QoS satisfaction.

Energy saving should be done at each level
Chip level (hardware): different operating power saving modes, careful
circuit design.
Energy efficient RF: More efficient design of power amplifiers, saving
power leakage in transmission to antenna.
Software: Operating system, compiler design.
Phy layer: power control, AMC.
Power saving modes under low utilization.


Green Communication

MAC: Power aware scheduling.
Cross layer design: Phy, MAC, routing.
Smarter design of topology: Cell sites, BS size saves upto 40% energy
Femto cells: Decreases BS and cell phone transmit power
MIMO antennas: to increase capacity, diversity.
Interference coordination
Spectral reuse
Opportunistic scheduling
Energy efficient router design.
1 1
Should have smaller buffers: Buffer consumes 2 of board space and 3
of power.
Energy efficient TCP


Green Communication

Alternative Energy sources:
Solar
Wind
Fuel cell, hybrid


Solar/Wind powered Base Station


Indian Cellular Scenario

2, 50, 000 Telecom towers
70% energy consumed by Towers
50% Towers in rural India
Each tower consumes 1K- 3KWatt
Operated by Diesel generators
≥ 4400 ton/hr CO2 emission.



Solar powered village BS for
GSM- World GSM by VNL
Low cost, low powered BS
Connected to large BS
VBS handles hundreds of users
Uses ∼ 100 Watt
2 − 8m2 solar panels required
50 VBS installed in Rajasthan
Can provide connectivity at
remote places with no electric
supply: saves on diesel.
Other makes: Alcatel- Lucent,
Ericsson, Nokia Siemens.



Not enough sun light in Monsoon.
Combining Sun and Wind Energy a solution.
Flexenclosure design of BS
Wind generator atop the tower supporting antenna.
Solar panel on roof of shelter housing switching equipment.

Initial installation cost more than traditional BS but operating cost
much less
No oil/ diesel.
No transport cost of oil.
Low maintenance.
New TRAI Recommendation: 50% of rural and 20% of Urban BS to
use hybrid power by 2015.

Fundamental Limits for Communication Systems with Renewable Energy Sources 10 / 27
April 5, 2012

System design with Energy Harvesting sources

Using Solar, wind energy to
supplement regular electric
supply can be effective
BS with Energy Harvesting
sources

Downlink
Given (Xk , hk , Ek ) find PK and
the queue to serve so as to
satisfy QoS of different users.
Pk ≤ EK (1) + Ek (2)

Uplink problem
cell phones having solar cells.
Key Concern: Unpredictable, random energy generation.
April 5, 2012

Our Research on Energy Harvesting Communication
Systems

Single Node

AWGN channel with var σ 2 .
{Yk }iid
√
RK = received from channel = Tk Xk + Wk
Wk ∼ N(0, σ 2 )

April 5, 2012

Single Node: Capacity

Theorem:
When energy is consumed only in transmission,
the capacity = 0.5log (1 + E [Y ]/σ 2 )

Comments
1 Limiting capacity achieving distribution is iid N(0, E [Y ])
2 Capacity is same as that of an AWGN channel with average power
constraint E [Y ].

April 5, 2012

Capacity with Processing Energy
Zk = energy spent in processing and computations.
∗{Zk }iid.
Capacity achieving dist. Gaussian iid with possibly sleep mode.

Figure: Capacity with sleep mode.

April 5, 2012

Single Node with Data Buﬀer

{Xk } stationary, ergodic
{YK } stationary, ergodic

TK = min(EK , E [Y ] − ), > 0 (1)

Theorem:
If E [X ] < g (E [Y ] − ), g cont., non decreasing, concave then data queue
is stable.

(1) is throughput optimal policy.
But it is not delay optimal
April 5, 2012

Single Node

Greedy Policy
Tk = min(Ek , g −1 (qk )) (2)

Theorem
If E [X ] < E [g (Y )] and energy buﬀer is ﬁnite, then under (2) data queue
is stable.

Theorem
If g is linear then (2) is delay optimal and throughput optimal.

April 5, 2012

Figure: Comparison of policies with Figure: Comparison of policies with
Fading and linear g Fading; g (x) = log (1 + x)

April 5, 2012

Combining Queing Theory and Information Theory

Theorem
Reliable Communication with stable data queue is possible iﬀ
E [A] < 1 log 1 + Eσ2 ] .
2
[Y

April 5, 2012

MAC Policies

April 5, 2012

Information Theoretic Capacity

1 E [Y (1)]
R1 log 1+
2 σ2
1 E [Y (2)]
R2 log 1+
2 σ2
1 E [Y (1) + E [Y (2)]
R1 + R2 log 1+
2 σ2

April 5, 2012

Opportunistic Scheduling for Fading Channels: Orthogonal
Channels

Hk (i) = channel gain of Qi in slot k

Throughput optimal policy:
Choose queue with index
∗
ik = argmax(qk (i)gi (Hk (i)(fracE [Y (i)] − α(i))))
∗
E [Y (ik )]−
and use Tk = ∗
α(ik )
∗ ∗
α(ik ) = fraction of time slots assigned to ik estimated via LMS.

April 5, 2012

Opportunistic Scheduling: CDMA

Zigbee, WIFI use CSMA.
Choose backoﬀ timer of Qi as

f (qk (i)gi (hk (i) E [Y (i)]− ))
α(i)

f non-increasing

April 5, 2012

Figure: Orthogonal Channels: Figure: CSMA: Mean Delay, Symmetric
Symmetric, 3 Queues. 10 Queues.

April 5, 2012

Multihop Model

N stationary nodes
t sink nodes
Slotted system with slot length T
Sensor nodes sense a random ﬁeld
Dn set of sink nodes for node n. (Multicasting)
A node can be in sleep or wake mode
Nodes generate energy via a harvesting source.
April 5, 2012

Aim

Obtain a Joint Power Control Link Scheduling, Routing and Sleep-wake
policies to maximize the throughput in a fair manner.

Approaches considered
1 APP R : Multicommodity ﬂow model
2 APP T : Using Steiner Tree
3 APP Nc: Using Network Coding

April 5, 2012

Figure: Layout of the network : 20
sensors, 3 sinks, 10 sensor nodes Figure: Performance of ALGO-M : Used
multicast to sinks 1 and 2; 10 to sinks 2 to solve OPT-R and OPT-NC
and 3

ALGO-M can provide solution for comparatively larger network.
OPT-NC provides signiﬁcant improvement once OPT-R

April 5, 2012

Conclusions

Communication infrastructure has heavy cost of energy consumption,
high carbon footprint.
Careful design can reduce energy and carbon footprint substantially.
Green communications requires redesign at each level.
Research Opportunities at each level.
Communication systems with energy harvesting can be designed with
minimal eﬀect of random, unreliable energy sources.

April 5, 2012

Fundamental Limits of Communication Systems Using Renewable Energy

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