## 2 System Model  
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 (Figure 1). We assume that all medical related sensors are \"_light_\" in terms of computational resources and capable for wireless transmission of acquired medical data. These sensors are competing to gain access to a common medium and to transmit their data to a control/sink node forming wireless body/personal area networks. In the general case of our model we can easily assume that many such networks can co-exist in the same vicinity and thus we may have many competing transmission links operating simultaneously (from the same or different body/area personal networks). 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 [31]. 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  
Figure 1: Biomedical wireless network: application scenario. combined power of the additive white Gaussian noise and the interference caused by the other active transmissions in range. Thus: \(SINR_{(i)}=\frac{P_{i}G_{ii}}{(\sum_{j\
eq i}P_{j}G_{ij})+n_{i}}\), where \(G_{ij}\) is the path loss from transmitter \(j\) to receiver \(i\) and is equal to \(G_{ij}=(\ \lambda\ /4\ \pi\ )^{2}\cdot d_{(i,j)}^{-a}\), where \(\lambda\) is the wavelength, \(a\) is the path loss exponent and has typical values from 2 to 5, \(d_{(i,j)}\) is the distance between nodes \(i\) and \(j\), and \(P_{i}\) is the transmitted power. White Gaussian noise is denoted as \(n_{i}\). Another way of looking at the definition of the SINR, is to combine the transmission power of each transmitter with the quantity \((\ \lambda\ /4\ \pi\ )^{2}\) and consider \(P_{i}\) as the transmitted power at the distance of 1 meter and \(G_{ij}=d_{(i,j)}^{-a}\).The product of \(P_{i}G_{ii}\) is the received power of interest for the signal for interest and \(\sum_{j\
eq i}P_{j}G_{ij}\) is the total interfering power of all other transmissions received at the receiver \(i\). In the general case \(G_{ij}\) could be formulated to include fading effects, in this case it would have the expression \(G_{ij}=(\ \lambda\ /4\ \pi\ )^{2}\cdot d_{(i,j)}^{-a}\cdot f_{(i,j)}\), where \(f_{(i,j)}\) is the fading coefficient (a non-negative random variable).