Gabriel E. Arrobo

In vivo wireless communication channels

In this paper we perform signal strength and channel impulse response simulations using an accurate human body model and we investigate the variation in signal loss at different RF frequencies as a function of position around the human body. It was observed that significant variations in received signal strength (RSS) occurred with changing position of the external receive antenna at a fixed distance from the internal antenna. The variations were even more profound at the highest frequency, where just a 5° of movement causes an increase of RSS up to 20 dB. Wideband scattering parameters were also obtained and the channel impulse response was calculated. A greater amount of dispersion through the abdomen has been observed than was expected based on human body geometry.

New approaches to reliable wireless body area networks

In this paper we present and contrast two approaches, Cooperative Network Coding (CNC) and Cooperative Diversity Coding (CDC) to achieving reliable wireless body area networks. CNC combines cooperative communications and network coding, while CDC combines cooperative communication and diversity coding. These approaches also provide enhanced throughput and transparent self-healing which are desirable features that Wireless Body Area Networks should offer. Additionally, these feed-forward techniques are especially suitable for real-time applications, where retransmissions are not an appropriate alternative. Although, these techniques provide similar benefits, simulation results show that CDC provides higher throughput than CNC because of the fact that the network topology is known and few hops between the source and destination. Moreover, CDC has lower complexity, since the source and destination nodes know the coding coefficients.

SAR and BER evaluation using a simulation test bench for in vivo communication at 2.4 GHz

We present a simulation method and results that utilizes accurate electromagnetic field simulations to study the maximum allowable transmitted power levels from in vivo devices to achieve a required bit error rates (BER) at the external node (receiver) while maintaining the specific absorption rate (SAR) under a required threshold. The BER of the communication can be calculated using the derived power threshold for a given modulation scheme. These results can be used to optimize the transmitted power levels while assuring that the safety guidelines in terms of the resulting SAR of transmitters placed in any location inside the human body are met. To evaluate the SAR and BER, a software-based test bench that allows an easy way to implement field solver solutions directly into system simulations was developed. To demonstrate the software-based test bench design, a complete OFDM-based communication (IEEE 802.11g) for the in vivo environment was simulated. Results showed that for cases when noise levels increase or the BER becomes more stringent, a relay network or the use of multiple receive antennas, such as in a MIMO system, will be become necessary to achieve high data rate communication.

Effect of link-level feedback and retransmissions on the performance of cooperative networking

Cooperative Networking is a new technology which exploits the massive deployment of nodes in wireless sensor networks. Cooperative Networking synergistically integrates Networking with cluster-based Cooperative Communications to improve reliability and enhance network performance. In this paper, we consider the effect of link-level feedback and retransmissions on the performance of wireless sensor networks using Cooperative Networking, and we present scenarios where link-level retransmission offers a significant improvement in network throughput. Generally, Cooperative Networking with link-level retransmission provides higher throughput when the network node density is low (i.e., sparse networks) or in environments with adverse conditions such as high probability of transmission loss and low connectivity among the nodes.

MIMO in vivo

We present the performance of MIMO for in vivo environments, using ANSYS HFSS and their complete human body model, to determine the maximum data rates that can be achieved using an IEEE 802.11n system. Due to the lossy nature of the in vivo medium, achieving high data rates with reliable performance will be a challenge, especially since the in vivo antenna performance is strongly affected by near-field coupling to the lossy medium and the signals levels will be limited by specified specific absorption rate (SAR) levels. We analyzed the bit error rate (BER) of a MIMO system with one pair of antennas placed in vivo and the second pair placed inside and outside the body at various distances from the in vivo antennas. The results were compared to SISO arrangements and showed that by using MIMO in vivo, significant performance gain can be achieved, and at least two times the data rate can be supported with SAR limited transmit power levels, making it possible to achieve target data rates in the 100 Mbps.

Modeling the wireless in vivo path loss

Our long-term research goal is to model the in vivo wireless channel. As a first step towards this goal, in this paper we performed in vivo path loss measurements at 2.4GHz and make a comparison with free space path loss. We calculate the path loss by using the electric field radiated by a Hertzian-Dipole located inside the abdominal cavity. The simulations quantify and confirm that the path loss falls more rapidly inside the body than outside the body. We also observe fluctuations of the path loss caused by the inhomogeneity of the human body. In comparison with the path loss measured with monopole antennas, we conclude that the significant variations in Received Signal Strength is caused by both the angular dependent path loss and the significantly modified in vivo antenna effects. Index Terms — In vivo propagation, ex vivo communication, path loss model, Hertzian-Dipole, angular dependentOur long-term research goal is to model the in vivo wireless channel. As a first step towards this goal, in this paper we performed in vivo path loss measurements at 2.4 GHz and make a comparison with free space path loss. We calculate the path loss by using the electric field radiated by a Hertzian-Dipole located inside the abdominal cavity. The simulations quantify and confirm that the path loss falls more rapidly inside the body than outside the body. We also observe fluctuations of the path loss caused by the inhomogeneity of the human body. In comparison with the path loss measured with monopole antennas, we conclude that the significant variations in Received Signal Strength is caused by both the angular dependent path loss and the significantly modified in vivo antenna effects.

A novel vectorcardiogram system

This paper presents the proof-of-concept investigation for a miniaturized vectorcardiogram [VCG] system for ambulatory on-body applications that continuously monitors the electrical activity of the heart in three dimensions. We investigate the minimum distance between a pair of leads in the X, Y and Z axes such that the signals are distinguishable from the noise. The target dimensions for our VCG are 3×3×2 cm and, based on our preliminary results, it is possible to achieve these dimensions. The next step in our research is to build the miniaturized VCG system that includes processing, learning and communication capabilities.

Performance evaluation for MIMO in vivo WBAN systems

In this paper we present the performance evaluation for a MIMO in vivo WBAN system, using ANSYS HFSS and the associated complete Human Body Model. We analyzed MIMO system capacity statistically and FER performance based upon an IEEE 802.11n system model, with receiver antennas placed at various angular positions around the human body. We also analyzed MIMO system capacity with receiver antennas at the front of the body at various distances from transmitter antennas. The results were compared to SISO arrangements and we demonstrate that by using 2×2 MIMO in vivo, better performance can be achieved, and significantly higher system capacity can be achieved when receiver antennas are located at the back of the body and in front of the body.

Effect of the connectivity on the performance of Cooperative Network Coding

Cooperative Network Coding is a novel technology that synergistically integrates Network Coding with cluster-based Cooperative Communications to produce enhanced network reliability and security features, while improving the throughput. In this paper, we consider the effect of the connectivity on the performance of wireless sensor networks using Cooperative Network Coding, and we present scenarios where the values of the connectivity optimize the throughput. Generally, Cooperative Network Coding provides its optimal throughput when the number of nodes in the first cluster connected to the source node is equal to the number of source packets, the number of nodes in cluster i+1 connected to the node (i, j) is at least 4 and the destination node is connected to all the nodes in the last cluster. However, if for any reason the connectivity of nodes between two adjacent clusters is reduced, Cooperative Network Coding can increase its performance by connecting all the nodes in the first cluster to the source node.

Minimizing energy consumption for cooperative network and diversity coded sensor networks

In this paper, we present an approach to minimize the energy consumption of multihop wireless packet networks, while achieving the required level of reliability. We consider networks that use Cooperative Network Coding (CNC), which is a synergistic combination of Cooperative Communications and Network Coding. Our approach is to optimize and balance the use of forward error control, error detection, and retransmissions at the packet level for these networks. Additionally, we introduce Cooperative Diversity Coding (CDC), which is a novel means to code the information packets, with the aim of minimizing the energy consumed for coding operations. The performance of CDC is similar to CNC in terms of the probability of successful reception at the destination and expected number of correctly received information packets at the destination. However, CDC requires less energy at the source node because of its implementation simplicity. Achieving minimal energy consumption, with the required level of reliability is critical for the optimum functioning of many wireless sensor and body area networks. For representative applications, the optimized CDC or CNC network achieves ≥ 25% energy savings compared to the baseline CNC scheme.