Use of Smart Antennas in Ad Hoc Networks

1. Use of Smart Antennas in Ad Hoc Networks

2. What are Directional Antennas and Why use them ? • Omni-directional antennas are those antennas that radiates or receives energy equally well in all directions (also called isotropic antennas). • Directional antennas have the capability to receive/transmit more energy in one direction as compared to others – in some sense the amount of energy in directions other than the direction of communication are quelled. • Can potentially provide larger frequency re-use – reduce collisions – increase battery ! • We can use this in conjunction with power control.

3. Pictorial Representation Main Lobe B B A Side Lobe A Directional transmission An omni-directional transmission A reduction in the radiated power in the direction of the primary lobe is caused due to the presence of the side lobes.

4. Smart versus Directional Antennas • Smart Antennas (or MIMO) do more! • They can null out interference from other nodes. • Contains an array of antennas elements • Complex signal processing techniques to decide on which elements to receive signals (or transmit on) from and how much power to use on each element. • Each element has an associated “weight” that determines this. • Spatial diversity

5. [1] R.Ramanathan, “On the Performance of Ad Hoc Networks with Beamforming Antennas”, ACM MOBIHOC 2001.

6. Some Definitions • Gain of the directional antenna – hU(d)/Uave, where h is the efficiency of the antenna (accounts for losses), U(d) is the gain in direction d and Uave is the average power density – that of an omni-directional antenna. • Main lobe – lobe with maximum gain, side lobes are due to the spill over of the energy. • Antenna beamwidth : Typically referred to as the 3 dB beamwidth – the angle subtended by the two directions on either side of the peak gain that are 3 dB down in gain. • Note that a 3dB gain reduction is in fact equivalent to reducing the gain by 1/2.

7. Units • The Gain of the antenna is measured in dBi – which refers to the amount of dB improvement over an (ideal) isotropic antenna.

8. Models Used • Flat Topped Radiation Pattern – Idealized angular response such that the gain is constant within the beamwidth and there are no side-lobes. – In 3D it is like a pie! • If the beamwidth is q in the azimuth, gain is G = 2P/q. • Cone+Sphere radiating pattern – Used in [6]. A sphere is used to account for the effects of the sidelobes. There is a cone in the direction of the peak gain – within the cone the gain value is assumed constant.

9. Switched Beam and Steered Beam • In Switched Beam systems there are multiple fixed beams. Only fixed directions possible. Also called “Sectorized Antennas”. • Cannot track moving nodes – choose nearest direction to that of neighbor. • In a Steered Beam System, the main lobe can be pointed in virtually any direction. The direction of arrival information obtainable from packet received from a neighbor can be used to steer the beam in the direction of that neighbor – typically the simplest of smart antennas • dynamic phased arrays – maximize gain towards target • adaptive arrays – produce nulls towards interferers.

10. Applicability • Higher frequencies will cause a shrinkage in the size of the antenna. • Typically separation between elements should be of the order of the wavelength l. • As this wavelength decreases, smaller antennas. • In the 5.8 Ghz ISM band, an eight element cylindrical array will have a radius of only 3.3 cm and for a 24 Ghz band, the radius would reduce to a mere 0.8 cm.

11. Read paper [1] on how one might compute the gain in the side-lobes given a desired gain in the direction of the main-lobe and the beamwidth.

12. Basic Channel Access -- MAC • The transmissions are assumed to be directional. • The receptions are all omni-directional. • When S wants to transmit a packet to R, it determines the direction D that R is relative to itself. • Find the direction Dj such that |D – Dj| is minimum. • Point beam in direction Dj. • Send packet and set the pattern to omni-directional mode after transmission.

13. The Channel Access Algorithm • Use of CSMA-CA. • What are the problems ? • C senses channel to be busy (RTS) and/or CTS and decides against transmitting to D while it clearly can. • In this example, RTS is assumed to be omni-directional. A B C D

14. A B C • In this example, let the RTS and CTS be sent directionally. • C is unaware and hence initiates a transmission to D. • Again RTS and CTS are directional. • When A is done, it is not aware of C’s transmission. • So it transmits to E resulting in a collision at D. D E

15. What do these examples tell us ? • The receipt of an RTS or CTS does not imply that you are blocked • The non-receipt does not imply that you can transmit. • This is a high level analysis. • There have been other papers that follow [6] that try to address this problem to some extent. • Important thing to note: Deployment of directional or smart antennas involves additional complexity – although complex, there are gains typically to be had in throughput and power.

16. Approaches • Omni-directional transmission of RTS/CTS messages • Aggressive Approach: A node is never blocked upon receiving RTS or CTS. Can cause collisions. RTS/CTS merely used to ensure that receiver is not in the process of transmitting. • Conservative Approach: Always defer transmissions upon hearing an RTS or a CTS – like in the omni case. • The authors find in their simulations that the conservative approach results in huge latency penalties or drops (with limited queue sizes) since nodes keep deferring transmissions even if they were possible.

17. Link Power Control • A Variant of the scheme that we discussed earlier is tried out (from the Monks paper). • The authors find that such a power control scheme helps !

18. Neighbor Discovery • Omni-directional transmissions of control messages help. • With directional transmissions and receptions this is not that trivial – transmitter and receiver must somehow point their antennas towards each other. • The authors also suggest directional discovery where hello messages are transmitted in each direction periodically – assumption is again omni-directional receptions.

19. Miscellaneous Issues • A simple link state routing protocol is used – essentially effects of routing are not studied. • OPNET – simulation software. • Simple path loss propagation model. • Refer to paper for other parameters used in simulations. • They study density, antenna gain and beamwidth.

20. What do the results show ? • With simple CSMA and no power control not much to be had!! • Increased beamwidth and simple CSMA causes nodes to attempt to transmit to nodes that are in the process of transmitting themselves (deafness). • Still some gains in performance. • Aggressive Approach outdoes the Conservative approach – delays tend to increase since in the latter nodes defer transmissions even when they could have in fact transmitted.

21. Performance with beamforming antennas improves with density • To be expected – more the nodes, more the contention – spatial re-use helps. • Power control with beamforming antennas can lead to dramatic improvements. • Without power control energy injected into the network in the two cases is the same. • It is also observed with power control that performance improvement is best at medium densities – at higher densities side lobes increase interference.

22. Directional Neighbor discovery helps – increased range • It is especially helpful in sparser networks – reduces possibilities of network partitions. • Switched beam systems can provide benefits similar to steerable beam systems – with a minimum number of antenna elements.

23. Energy Efficient Communications using Directional Antennas – Spyropoulos and Raghavendra

24. Objective of the Paper • Energy Efficient Routing • Find the minimum energy paths (given that there are directional antennas) • Schedule transmissions on the paths (note that this is necessary since the directional transmissions and receptions ought to be synchronized). • Reduction in energy cost incurred in routing.

25. Steps Taken • Compute the Shortest Cost Paths • Compute the traffic flow matrix – the amount of traffic that has to traverse each link the network; fij represents the amount of traffic that has to go across from a source node i to a destination node j. • Construct a topology (minimize energy) and find if the topology can handle the traffic generated. If not, use a heuristic to alter topology and go back to Step 1. • Schedule the flows.

26. How can Energy be Saved ? • ETx = Eelec + Eamp* da, where • Eelec is the energy consumed in the electronics per bit. • Eamp is the energy consumed in the power amplifier per bit. This is proportional to the transmission power needed to reach the receiver and thus is inversely proportional to the gains achieved by the antennas. • d is the distance between the transmitter and the receiver and a is the path loss index. • If we choose power optimal links, Eamp is reduced and thus, we have energy savings. • NOTE: Mechanical steering of antennas is not an option – it pretty much eats up the node’s battery.

27. Power Aware Routing • The metrics from the Power Aware Routing paper by Singh, Woo and Raghavendra [4] are used. • Metric 1 – which minimizes the energy consumed per packet and Metric 4 – Maximizes Network Lifetime by choosing routes which have largest residual power are used.

28. Assumptions • Traffic is slowly varying – the flow patterns are known. • Mobility is ignored – static scenarios or a priori known trajectories.

29. Flow Matrix Calculation • fij = Flow from node i to node j. • f’ij = Flow that is to be sent on the link from node i to node j and is given by • Sk,l = 1 fkl * Bij (k,l) • where, Bij (k, l) = 1 if there is traffic from k to l routed on the link from i to j and zero otherwise. • Each node has to switch the antenna in a particular direction in order to establish a connection with a neighbor. • The amount of time that a link is active is called the uptime for the link. N

30. The uptime of a link is chosen to be the ratio of the traffic to be routed on that link to the total traffic to be handled by the node. • Thus, if the total traffic handled by node i is li, then the link from i to j is maintained for a time equal to tij = f’ij/ li. • However, note – for directional communications, not only should the sender point the antenna but so should the receiver and the uptime for link i-j may be different as seen by nodes i and j. • Thus, Tup (i,j) = min(tij, tji).

31. Scheduling • Goal is to minimize latency of transmissions. • Formulate as a graph theoretic problem. • Edge coloring – minimum number of colors needed to color a graph such that two edges with the same color have no vertices in common is between dmax and dmax+1; dmax is the maximum node degree in the graph. • Edge weights are not considered yet. • However this leads to multiple possibilities (see paper) – various edge colorings possible.

32. Maximum Weight Matching • Schedule links together that have equal or similar weights. • Then remove the links from the graph and redo the maximum weight matching. • Look at paper for lower bounds on how well we can do. • Nodes with high degrees are the bottlenecks.

33. Can we implement this ? • Reconfiguration / Rescheduling needed as traffic patterns change. • The authors propose alternating initialization and communication phases. • During initialization, link state and the schedules are disseminated. • During steady communication phases, the schedules are followed. • Only good if traffic patterns slowly change and if nodes are relatively static. • Otherwise, overhead in initialization would be expensive.

34. The final say • Interesting approach with limited applicability. • Simulations seem to show considerable energy savings in the scenarios considered. • Scheduling antenna directions to get the best out of the network is still not a solved problem by any means.