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Limitations, performance and instrumentation of closed-loop feedback based distributed adaptive transmit beamforming in WSNs Stephan Sigg, Rayan Merched El Masri, Julian Ristau and Michael Beigl Institute of operating systems and computer networks, TU Braunschweig Muhlenpfordtstrasse¨ 23, 38106 Braunschweig, Germany fsigg,[email protected], fj.ristau,[email protected] Abstract—We study closed-loop feedback based approaches to Also, by virtual MIMO techniques in wireless sensor net- distributed adaptive transmit beamforming in wireless sensor works [9], [10], single antenna nodes may establish a dis- networks. For a global random search scheme we discuss the tributed antenna array to generate a MIMO channel. Virtual impact of the transmission distance on the feasibility of the synchronisation approach. Additionally, a quasi novel method MIMO is energy efficient and adjusts to different frequencies for phase synchronisation of distributed adaptive transmit beam- [11], [12]. forming in wireless sensor networks is presented that improves In all these implementations, a dense population of nodes the synchronisation performance. Finally, we present measure- is assumed. A large scale sensor network, however, might be ments from an instrumentation using USRP software radios at sparsely populated by nodes as additional nodes increase the various transmit frequencies and with differing network sizes. installation cost. Alternative solutions are approaches in which a synchronisation among nodes is achieved over a communica- I. INTRODUCTION tion with the remote receiver. This means that communication A much discussed topic related to wireless sensor networks among nodes is not required for synchronisation so that also is the energy consumption of nodes as this impacts the lifetime sparsely populated networks can be supported. and also dimension, weight and cost of the sensor nodes. Dependent on the communication scheme between the re- A major goal in the design of sensor nodes is therefore to ceiver and the network, we distinguish between closed-loop cut down the energy consumption of nodes. Ideally, a power and open-loop feedback based approaches. consumption at which power harvesting techniques provide In the former scheme, carrier synchronisation is achieved sufficient energy for operation of the node is desired. This in a master-slave manner. The receiver corrects the phase- aim requires nodes that operate at very low power levels of offset between the destination and a source node [13]. This not more than several ten microwatts. This means that also the approach is applicable only to small network sizes and requires transmission power and consequently the transmission range sophisticated processing capabilities at the source nodes. of individual nodes is strictly limited. For the feedback based closed-loop synchronisation scheme, A greater transmission distance can then be achieved when a computationally modest approach was presented in [13], a sufficient number of nodes superimpose their carrier signals. [14]. The authors propose an iterative process in which source This is especially useful in large scale sensor networks since nodes randomly, but guided by a one-bit feedback on the the number of potentially available nodes in such a network synchronisation quality adapt their carrier phases. is increased. By combining RF transmit signal components, a A preparatory requirement for closed-loop feedback based set of transmitting nodes in a sensor network can cooperate distributed adaptive beamforming in wireless sensor networks to increase the maximum transmission range of a network. is that the power level of the asynchronously received RF sum One approach to superimpose transmit signals is to utilise signal (but not necessarily the signal strength of one single neighbouring nodes as relays as proposed in [1]. Cooperative carrier) is above thermal noise and interference. As transmit transmission is then achieved by Multi-hop [2], [3], Data nodes do not communicate with each other, information about flooding [4] and cluster based [5], [6] approaches. the synchronisation process has to be obtained from received Alternatively, in round-trip synchronisation based tech- signal measurements. This is possible only when the received niques [7], [8], the destination sends beacons in opposed signal strength exceeds the noise level considerably. In sec- directions along a multi-hop circle in which each of the tion III we study the impact of the transmission distance on nodes appends its part of the overall message. Beamforming is the feasibility of distributed adaptive transmit beamforming achieved when the processing time along the multi-hop chain in wireless sensor networks. In section IV we introduce an is identical in both directions. This approach, however, does algorithm that exploits this property and greatly improves the not scale well with the size of a network. synchronisation performance. Finally, in section V, we detail TABLE I CONFIGURATION OF THE SIMULATIONS CONDUCTED. PRX IS THE THE RECEIVED SIGNAL POWER, d IS THE DISTANCE BETWEEN TRANSMITTER AND RECEIVER AND λ IS THE WAVELENGTH OF THE SIGNAL Property Value Node distribution area 30m × 30m Mobility stationary nodes RF frequency fRF = 2:4 GHz Transmission power of nodes PTX = 1 mW Gain of the transmit antenna GTX = 0 dB Gain of the receive antenna GRX = 0 dB Iterations per simulation 10000 Identical simulation runs 10 Thermal noise power (AWGN) −103 dBm “ ”2 Pathloss calculation (P ) λ RX PTX 2πd GTX GRX Fig. 1. The proposed solution to closed-loop distributed adaptive transmit beamforming Beamforming can be utilised to increase the transmission range of cooperating nodes. When it is, however, implemented by a closed-loop phase synchronisation technique in which an instrumentation of distributed adaptive transmit beamform- transmit nodes do not cooperate directly but utilise the receiver ing in wireless sensor networks by USRP software radios. feedback for synchronisation, the high relative noise figure II. DISTRIBUTED ADAPTIVE BEAMFORMING at increased distance might prevent a useful feedback as improvements by phase alterations of individual RF signal Closed-loop feedback based distributed adaptive beamform- components can be obstructed. ing in wireless sensor networks is an iterative approach to We place a receiver at several transmit distances to the synchronise RF transmit signal components for their relative wireless sensor network in mathematical simulations. Table I phase offset. We assume that, for a network of size n, initially summarises the base configuration of the simulations. 100 the carrier phase offsets γ of transmit signal components i nodes have been placed uniformly at random on a space of < ej(2π(f+fi)t+γi) ; i 2 f1::ng are arbitrarily distributed. 30m × 30m. The receiver distance is altered in various simu- When a receiver requests a transmission from the network, lation runs from 100 to 300 meters. We assume a direct line carrier phases are synchronised in an iterative process. of sight between the network and a remote node. Interference 1) Each node i adjusts its carrier phase offset γ and i between the direct signal components is calculated as the sum frequency offset f randomly i of the distinct received carrier signal components. 2) Source nodes transmit simultaneously as a distributed Simulation results are depicted in figure 2. In 100 meters beamformer. distance, the aligned sum signal after 10000 iterations of 3) The receiver estimates the level of phase synchronisation the algorithm is well distinguishable from thermal noise (cf. of the received RF sum signal (e.g. by SNR or compar- figure 2(e)). Also, within about 3000 iterations, the phases ison to an expected signal). of all transmit signal components are within 0:2π (cf. fig- 4) The level of synchronisation is broadcast to the network. ure 2(f)). According to the Friis transmission equation utilised Nodes sustain their phase adjustments if the feedback to calculate the pathloss of individual signal components in has improved or else reverse them. our simulation setting, the received signal strength is equal These four steps are iterated repeatedly until a stop criteria to the noise level of −103dBm already at about 90 meters is met (e.g. maximum iteration count or sufficient synchroni- distance. sation). Figure 1 illustrates this procedure. Observe that this For distances of 150 meters and 200 meters, the relative approach has modest computational requirements for nodes in noise figure is already easily above the received signal strength the network. In each iteration, only a single random decision is of an individual signal component. An alignment of signal taken and possibly the phase offset of the carrier is altered by components is, however, still feasible by distributed adaptive an arbitrary amount. Furthermore, inter-node communication transmit beamforming. After 10000 iterations, the received is not required for the synchronisation process. It is even sum signal is easily distinguishable although the phase syn- possible to synchronise a set of nodes that are out of reach of chronisation between signal components is less complete (cf. each other (Although in this case a coordinated transmission figure 2(b) and figure 2(d)). of identical data subsequent to the synchronisation is not possible). At this synchronisation level we can imagine simple mod- ulation schemes to transmit data over the channel so that we III. IMPACT OF THE TRANSMISSION DISTANCE conclude that distributed adaptive transmit

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