## 2 Related Work  
In this section, we present a further survey on the application of the MIMO technique in the WSNs. Moreover, some key issues on IA are further investigated. As shown in the Introduction, the MIMO technique is one promising method to improve the performance of WSNs. The introduction of multi-antenna transmission to WSNs can further explore the channel capacity. A threshold-based medium access control strategy was developed in [14], which can exploit the diversity gain of the MIMO WSNs. Since the energy consumption of sensor nodes plays a critical role on their lifetime, it is important to increase the energy efficiency to prolong the endurance of the WSNs. Recently, a large number of researchers have employed the MIMO technique to achieve energy efficient WSNs. [PERSON] _et al._[15] developed a multi-hop virtual MIMO (VMIMO) scheme to improve the energy efficiency and QoS of the WSNs. The cooperative MIMO and data-aggregation techniques were leveraged to jointly reduce the energy consumption of the sensor nodes in [16]. The random WSNs using cooperative MIMO technique were considered in [17], where packet error rate, nodes active rate and energy consumption were jointly analyzed. [PERSON] and [PERSON] [18] analyzed the performances of two kinds of VMIMO schemes in the WSNs, _i.e._, space-time block coding and spatial multiplexing, and developed a new switching algorithm between them to achieve energy efficient transmission. In addition, relay communication has been cooperated with the MIMO technique to further enhance the performance of WSNs. A multi-hop hybrid VMIMO scheme was developed in [19], where a route with minimum energy consuming was designed, while satisfying the given data rate and BER constraints. In addition, a number of testbeds for WSNs have been developed such as EasiTest, Emstar, Kansei and MoteLab, which can facilitate the development of the advanced WSNs [20]. Although the MIMO technique has shown bright prospects in WSNs, many problems still exist in this systems. As the scale of WSNs becomes larger and larger, the interference becomes a severe impediment to the increasing demand for communications. Against this background, IA has emerged as a promising technology which can effectively eliminate the interference. One of the major challenges in IA is the design of precoding and decoding matrices, which has significant impact on the levels of interference leakage and sum rate that IA can achieve. The closed-form solutions to IA were provided in [4] for the MIMO interference channel with three users, one DoF, two transmitting and receiving antennas, and were extended to a more general situation of \(N+1\) users and \(N\) antennas in [21]. Unfortunately, the problem of designing the optimal precoding and decoding matrices has been proved to be NP-hard [22], and the number of solutions increases rapidly with the scale of the system. Therefore, numerical methods have to be employed to search for the suboptimal solutions. [PERSON] _et al._ proposed a minimizing interference leakage (MinIL) algorithm in [23], which highlights the advantage of relatively low signaling overhead by utilizing the channel reciprocity. However, the MinIL algorithm might degrade significantly in the low SNR regime for the reason that it only focuses on suppressing the interference from the unintended users, without considering the power loss of the desired signal. Hence, [PERSON] _et al._ modified the method and progressed to the maximizing signal-to-interference-plus-noise (Max-SINR) algorithm in [24], which can improve the throughput especially when the SNR is low. A low-complexity modified alternating minimization algorithm was proposed in [25], which leverages the courant penalty function to jointly optimize the interference leakage and intended signal. The concept of signal leakage was introduced by [PERSON] _et al._ in [26], and the IA solutions were searched under the maximal signal-to-leakage-plus-noise ratio criterion.