## 1 Introduction  
Wearable sensor systems have been attracting intense research interest over the recent years due to their potential in practical applications such as healthcare monitoring, sports training, and interactive gaming [1, 2]. A wearable sensor system can be also referred to as body sensor network (BSN), which comprises multiple sensor nodes and a coordinator worn on a human body. The physiological information of the human body collected by the sensor nodes is first delivered to the coordinator, which then forwards the information to a remote server through a network interface for further processing [3, 4]. For the rest of the paper, we will denote a BSN for a wearable sensor system. Due to the presence of the human body, BSNs have some stringent requirements for communication systems:  
* **Quality of service (QoS)**: QoS measures the overall performance of a wireless network in terms of throughput, transmission delay, error rates, bandwidth, _etc._[5, 6]. In BSNs, where health and motion information are monitored in real-time, QoS requirements are strict. * **Energy efficiency**: Sensor nodes in BSNs are typically battery-powered, and difficult to charge especially for implanted sensor nodes [7]. An energy efficient communication protocol conserves energy by reducing protocol overhead, retransmission, and collisions, and thus extends the lifetime of a BSN [8, 9]. * **Heterogeneous data rate**: Heterogeneous sensor nodes are employed in BSNs with various data rate requirements (e.g., 5 kbps for Electrocardiograph (ECG) and Electroencephalography (EEG), and 1 kbps for temperature sensor, respiratory sensor, and pulse sensor [10]). A flexible communication system for BSNs should accommodate the traffic with heterogeneous data rate requirement. The performance of BSNs may deteriorate due to a number of factors including inter-user interference, which refers to the interference caused by the simultaneous transmissions from wireless BSNs in close proximity to each other. When the sensor nodes transmit data in their respective BSNs at the same time in vicinity, interference is introduced resulting in lost or erroneous reception. As a result, throughput is reduced and energy consumption is increased in BSNs. There has been extensive research works on inter-user interference recently. [PERSON] _et al_. [11] highlighted the effect of the inter-user interference on BSNs from the perspective of network architectures. They found that packet delivery rate is reduced by 35% in the presence of eight or more interfering BSNs, and thus the inter-user interference problem has to be addressed carefully. Some existing works on the design of mitigation methods for inter-user interference can be found in [12, 13]. [PERSON] _et al_. [13] proposed a method where each BSN measures the interference from other BSNs and then selects a suitable channel and transmission power. However, the computational complexity of this algorithm is high which makes it difficult to implement in realistic BSN systems due to the low available computing power in such systems. [PERSON] _et al_. [12] presented a clear method to reduce the inter-user interference, where BSNs are reallocated channels by a fixed infrastructure when they move into the radio range of each other. This scheme reduces interference effectively if the number of congregated BSNs within the interference range is fewer than the number of available channels. However, besides the infrastructure cost, this approach leads to frequent channel switching, which incurs much overhead and is thus unsuitable for occasional and short-term interference. To develop an effective inter-user interference mitigation method for a certain scenario, it is important to have a thorough and comprehensive understanding of the prevalence and severity of such interference in the realistic BSN deployment scenario, as well as the factors affecting such interference.