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Interference, in wireless networks, is a central phenomenon when multiple uncoordinated links share a common communication medium. The study of the interference channel was initiated by Shannon in 1961 and since then this problem has been thoroughly elaborated at the Information theoretic level but its characterization still remains an open issue. When multiple uncoordinated links share a common medium the effect of interference is a crucial limiting factor for network performance. In this work, using cross layer cooperative communication techniques, we study how to compensate interference in the context of wireless biomedical networks, where many links transferring biomedical or other health related data may be formed and suffer from all other interfering transmissions, to allow successful receptions and improve the overall network performance. We define the interference limited communication range to be the critical communication region around a receiver, with a number of surrounding interfering nodes, within which a successful communication link can be formed. Our results indicate that we can achieve more successful transmissions by adapting the transmission rate and power, to the path loss exponent, and the selected mode of the underline communication technique allowing interference mitigation and when possible lower power consumption and increase achievable transmission rates.

Recent advances in Information and Communications Technology (ICT) enable the acquisition, transmission and interpretation of different bio-signals, from fixed or mobile locations. This can support better prevention and well-being and provide valuable and prompt diagnostic tools in various application domains, ranging from home care to emergency care, or situations in which a second or a specialist opinion is required before making a clinical decision [

Wireless biomedical networks serve as the transport mechanism among devices or between devices and traditional backbone networks allowing the transmission of health related information [

The objective of this work is to present an in-depth analysis of how cross layer techniques can be used in the design and study of wireless biomedical networks. More specifically, we study how, by adapting various parameters of the telecommunication system, we can: (i) allow more concurrent transmissions; (ii) minimize interference; (iii) enhance network throughput; (iv) maximize individual link data rates; and (v) optimally utilize network resources for all competing transmissions. We present our theoretical analysis and simulation results taking into account the number of interfering nodes, and networking parameters of the underline telecommunication system. We investigate how a medium access mechanism, using cross layer cooperative communication techniques [

Most state-of-the-art systems deal with interference in one of the following two ways, even though both of them might be sub-optimal, either: (a) orthogonalize the communication links in time or frequency so that they do not interfere with each other at all; or (b) allow communication links to share the same degrees of freedom, but treat each other’s interference as adding to the noise floor. The first approach entails an a priori loss of degrees of freedom in both links, no matter how weak the potential interference is. The second approach treats interference as noise, while it actually carries information and has structure that can potentially be exploited in mitigating its effect. These considerations lead to the natural question of what is the best performance one can achieve without making any

A basic information theory model to study this question is the two-user Gaussian interference channel, where two point-to-point links with additive white Gaussian noise interfere with each other. Recent work on the interference channel [

Many known studies try to model and calculate interference levels for a wireless multi-hop

Our goal, in this work, is to control the amount of interference experienced by receivers and, in certain cases, to enforce concurrent transmissions, in order to maximize network performance by tightly coupling both physical and medium access layers. We examine a simplified wireless telecommunication system, which is used as the medium to transmit biomedical data, where randomly distributed networking nodes use a specific modulation scheme with a specific Bit-Error-Rate (BER) and a constant bandwidth assuming the absence of any error control coding or any multiuser decoding scheme or any cooperation between transmitters and/or receivers. This model reflects the situation of multiple low power/low complexity communication networks such as sensor networks, suitable to work in a networking biotelemetry system, and it enables us to make observations, with practical significance, for the operation of these networks. We study how interference, separation distance, power and rate adaptation, for biomedical telemetry wireless communications, can allow concurrent transmissions, enhance network capacity by maximizing, when possible, individual link data rates, minimize transfer time of a data packet and allow the best possible usage of network’s resources for all competing transmissions. Higher transmission rates can be achieved with higher levels of modulation (M-ary modulation), but higher transmission power is required to maintain acceptable bit error rate performance. If the total amount of interference at a receiver, during a reception of a packet, is high the network layer should decrease the transmission rate and/or increase the power to cope with it.

This paper is organized as follows: first we present related work and the problem of wireless interference environments where many biomedical nodes share a common communication medium. In the following section we present our system model and the application scenario for our study. More specifically, we define an interference limited communication region around a receiver, exhibiting a large number of surrounding interfering transmission, within which a successful communication link can be formed. In Section 3, we present our results obtained from simulations that show the accuracy of our proposed model for the estimation of the interference and of the interference limited communication range, even when we relax our assumption on the receiver’s position at the center of the networking area. In addition we present how interference levels and the interference limited communication range values are affected by the number of the surrounding interfering transmitters, the path loss exponent and most importantly on the selected mode and rate of operation. Section 4 concludes this paper.

In this section, we describe the application scenario for biomedical telemetry wireless body/personal area networks. In our scenario the wireless biomedical networking ecosystem is formed by a collection of biomedical sensors, control/sink nodes and gateways (

The interference channel thus arises naturally in this situation and interference mitigation becomes critical for optimum network performance. In our previous work we compared the performance of a simplified network and found special cases of improvements in performance with transmission links operating concurrently [

In this study we use the Gaussian interference channel, where two point-to-point links with additive white Gaussian noise (AWGN) interfere with each other but in a more general setting where the interference channel refers to a network consisting of a number of transmitting and receiving nodes. A one to one correspondence between senders and receivers exists and each transmitter communicates only with its corresponding receiver, and each receiver is only interested in decoding the information from its corresponding transmitter. When many transmitter—receiver pairs share a wireless channel (common medium), each transmission is affected from the interference caused by all the other links and at the same time interferes with the operation of all other active links.

The network layer must be able to sense the amount of interference at a receiver, during the reception of a packet, and cope with it using power and/or rate adaptation (transmission rate adaptation can help maximize channel usage and power control can help lower the power consumption and interference). This can be accomplished using the Signal to Interference plus Noise Ratio (SINR) a performance metric that measures the ratio of the received signal power to the power of the undesired signals in the receiver. We calculate the Signal-to-Interference-plus-Noise ratio (SINR) at each receiver as the ratio of the power of the signal of interest (meaningful information of a link) to the combined power of the additive white Gaussian noise and the interference caused by the other active transmissions in range. Thus:
_{ij} is the path loss from transmitter _{(i}_{,}_{j)}_{i} is the transmitted power. White Gaussian noise is denoted as _{i}^{2} and consider _{i}_{i}G_{ii}_{j}_{≠}_{i} P_{j}G_{ij}_{ij} could be formulated to include fading effects, in this case it would have the expression
_{(}_{i}_{,}_{j}_{)} is the fading coefficient (a non-negative random variable).

A specific transmission _{(}_{i}_{)} and for a positive vector P = (P_{1}, …, P_{k}) of transmission powers.

For each transmission link _{(}_{i}_{)} depends on many design parameters and properties of the telecommunication system, such as the target BER, the modulation scheme, the error correction coding employed and the desired transmission bit-rate for each link. A set of simultaneous transmissions is considered successful when condition in

In this section we study the interference exhibited at the center of a circular networking area when interfering nodes (with equal transmitting power) are randomly distributed. Going a step further from previous works, we define the _{ILR}) regions for biomedical body/personal area networks to be the critical communication region around a receiver, surrounded by a large number of randomly placed interfering nodes, within which if a transmitter is present, a successful communication link can be established. The value of d_{ILR}, as we will show, depends on a number of parameters: the number and the density of the surrounding interfering transmitters, the exclusion region around each receiver, the interference power level, and most importantly the selected transmission rate and mode of operation of the receiver.

We illustrate the generalized concept in _{ILR} is shown (the light green shaded disk). In this example the receiver acting as control/sink node in WBAN 1 experiences interference from all other transmitters deployed in the same vicinity (even if they belong to the same or different WBAN) transmitting simultaneously in the same channel. At the same time all the transmitters/sensors of WBAN 1 are causing interference to all other receivers. So only a portion of the sensors in WBAN 1 will be able to successfully transmit their data to the receiver of interest. These transmitters (_{ILR} (

In the proposed model we make the approximation that interference behaves like white Gaussian noise which is equivalent to the applicability of the SINR criterion, and is equal to the sum of the received power, from all active transmitters I_{i} = (∑_{j≠i} P_{j}G_{ij}), at the receiver of interest _{j} is the transmitted power and the path loss from transmitter _{(i,j)} is the distance between nodes

We assume, as illustrated in _{t} transmitters-from all other wireless body/personal area networks, with equal transmission powers P, are placed randomly with uniform distribution inside an area bounded by the circles with radius r_{max} and r_{min} (r_{min} is the radius of the exclusion region around the receiver where no transmitters are allowed to operate).

Inside the ring from r to r + dr there are on average

The total mean interference at the center of the circle then would be equal to:

In [

For path loss exponent

where for r_{max} ≫ r_{min} we have
_{max} (and consequently on the transmitter density
_{min} (the size of the exclusion region).

For path loss exponent

where for r_{max} ≫ r_{min} we have:
_{max} (and consequently on the transmitter density
_{min}.

We distinguish two different cases, which are described below for realistic “real-world” links and Shannon capacity links.

We consider the receiver at the center of the circular area to be part of a wireless network with “real-world” links that use a specific modulation scheme, a specific target BER and constant bandwidth. We assume the absence of any error control coding or any multiuser decoding scheme or any cooperation between transmitters and/or receivers and make the approximation that the interference behaves like white Gaussian noise, which combined with the thermal noise results in a combined total noise power spectral density N_{0}/2 in bandwidth BW. In this case there is a linear relationship between the achieved rate and the Signal to Interference plus Noise Ratio (SINR). We assume that the system’s bandwidth does not depend on the symbol duration of either the desired signal or the interfering signals. Thus the total power spectral density would be:
_{int} and P_{noise} is the total power of interference and surrounding thermal noise respectively) and the energy per symbol for a link would be equal to E_{s} = P_{i}G_{ii}/R_{s} which results in an energy per symbol to interference-plus-noise density ratio equal to:
_{i} and k = log_{2}M is the number of bits per symbol. The transmission bit rate is given by R_{b} = k·R_{s} and the values for the E_{s}/N_{0} can be found from the Symbol Error Rate (SER) or BER requirements. Based on the interference calculation, as presented earlier, and assuming that (i) all transmitters in the area operate with transmission power P and that (ii) the interference power is much larger than the background noise (I ≫ n), we see that the “_{ILR}, as defined above, is given by:

Again we distinguish two cases:

In _{ILR} is a decreasing function of the number of interfering nodes and asymptotically falls to zero. When we have a high value for the path loss exponent (a > 2), we see that we obtain lower values for the d_{ILR,} when the number of transmitting nodes is relatively small. As N_{t} increases, the rate of convergence of d_{ILR} to zero is:

If the receiver of interest is part of a wireless network with Shannon capacity links, then R_{b} = BW · W i_{2}(1 + γ) and thus the SINR threshold γ, for R_{b} is equal to is

We distinguish two cases:

_{ILR} has the same behavior as before (_{ILR} with a = 2 is larger than the d_{ILR} with a > 2. The rate of convergence to zero is the same as before.

In order to validate our theoretical estimations, for the computation of the interference values and the interference limited communication range, we implemented extensive Monte-Carlo simulations [

Our interference simulation experiments are presented in _{min} and r_{max} = 100 m. The receiver of interest is positioned at the center of the area where the highest amount of interference is exhibited. The transmission power is assumed the same for all transmitting nodes and is equal to 1mW. In the simulations we calculate the distance of each transmitting node from the center of the networking area where the receiver of interest is located. Path losses due to fading and shadowing are not considered. The path loss exponent ^{7}Hz and all transmitting nodes employ a BPSK modulation scheme (thus k in

The results from the proposed theoretical model are in a very good agreement with the values of the mean interference levels obtained by simulations. _{min} (the distance around the receiver where a transmitter cannot be placed, _{min} leads to lower interference.

In _{max}]. When a receiver is close to the borders of the networking area the interference level decreases. This given that the number of the nearby interfering transmitters decreases near the borders, is natural since the separation distance of interfering transmitters becomes larger than r_{max} and thus their interference diminishes.

The difference, between the interference levels at the center of the networking area, calculated with

The interference level on the border of the circular area (100% offset) is approximately half of the level at the center. Therefore we can assume that, for the largest part of the networking area, the interference level as given by _{ILR}, given by

In this section we present our simulation results for the d_{ILR} which is the maximum distance from which a transmitter can successfully send data to the centrally positioned receiver with a specific rate. We compare our simulation results with the results from calculations based on the theoretical analysis presented previously.

_{min} = 0.4 m and path loss exponent equal to 2 and 4 in plots (a) and (b) respectively. By adapting the transmission rate (lowering in this case) we can control the size of the region (enlarge it) and allow more transmitters to be able to successfully communicate with the receiver of interest. This result implies that we can use an upper layer communication parameter to easily adapt (using the proposed method) to the experienced interference. The theoretical and simulation results are in better agreement as the number of interfering nodes increases. We can see that using a higher path loss exponent assumption reveals how much the exhibited interference affect communication and that is the reason that the region is restricted to smaller sizes. We observe (_{ILR}.

A different approach for measuring the value of d_{ILR}, is to first measure the mean interference level, repeating the experiment (random placement of transmitting nodes) a large number of times, and then compute the d_{ILR} using this mean value of interference. In this case (_{ILR}.

In the final set of simulations the procedure described at the beginning of this section is employed (we measure the value of the d_{ILR} for each placement of the transmitting nodes and then calculate its mean value).

Changing the distance r_{min} (exclusion region) the interference limited communication range is not significantly affected, as shown in _{ILR}, which is reduced as the number of interfering nodes increases. This agrees with the results presented in [_{ILR} values are smaller than those for a = 2.

Finally, we compare the calculated values of the mean interference limited communication region and the results from our simulations, for path loss exponent equal to 2 and 4, _{max}], as we have done previously for the interference study. We can see that when the receiving node moves to the border of the networking area the value of the d_{ILR} is increasing (since the interference is decreasing), but it remains very close to its value at the center for a 60% or 70% (for a = 2) and for a 80% or 90% (for a = 4) offset from the center of the circle. The calculated values (black line) are always smaller than the simulation results; that is, they correspond to a worst-case scenario.

Interference suppression in biomedical wireless medical networks is a critical issue that would allow successful and reliable communication in critical medical data networking environments. In this paper we presented a detailed analysis on interference calculation for a receiving node placed in the vicinity of the center of a networking area when interfering transmitters are randomly placed around it using a uniform distribution. Our results indicate ways to compensate the degree of interference exhibited in each receiver with respect to network topology, transmission rate and individual maximum power constrains of each transmitter. The presented theoretical analysis provides a very good approximation of the mean interference found via simulations. We extended previous works by introducing the interference limited communication range (d_{ILR}) as the critical region around a receiver within which a transmitter, despite the presence of the other interfering nodes, can successfully send its data to the receiver of interest with a specific rate. We verified our results with simulations and presented the rate of convergence of the interference-limited region for wireless biomedical networks. Finally, we validated the accuracy of our model even when we relax our assumptions on the receiver’s position in respect with the interfering transmitters. No matter the receiver’s position, within a wireless biomedical area network, our model remains valid.

This work has been partly supported by the EC project: MyHealthAvatar (

The authors declare no conflict of interest.

All authors collaborated and contributed extensively to the work presented in this paper. More specifically: Emmanouil G. Spanakis and Apostolos Traganitis designed the system model and analysis; Emmanouil G. Spanakis executed the simulation; Emmanouil G. Spanakis, Apostolos Traganitis and Vangelis Sakkalis wrote the paper; Kostas Marias and Apostolos Traganitis had the general overview of the work presented in this paper. All authors have read and approved the final published manuscript.

Biomedical wireless network: application scenario.

Uniform random node placement. Receiver at the center of the networking area.

Interference limited communication range (r_{max} = 100 m).

Interference limited communication range for Shannon capacity link.

Interference power comparison for (_{min}.

Calculated interference at the center (_{max} = 100 m, path loss exponent a = 2 (_{min} = ranges from 0.4 to 2 m).

d_{ILR} for different transmission rates. Comparison of simulation and theoretical results.

d_{ILR} values calculated using the mean interference value found by Monte-Carlo simulations. Plots (

d_{ILR} for different r_{min} and different path loss exponent values.

Mean d_{ILR} values calculated (_{max} = 100 m, and r_{min}=ranges from 0.4 to 2 m).