## 1 Introduction  
Wireless sensor networks (WSNs) have attracted wide attention in recent years, and have been applied to the areas of environmental monitoring, remote healthcare assistance, battlefield surveillance, intelligent homes, industrial control, _etc._[1]. In particular, there is an increasing demand for WSNs to be capable of producing multimedia information such as image and video. In addition, the limited battery capacity of the sensors imposes strict constraints on the energy consumption of WSNs. Therefore, high data rates and high energy efficient transmission methods are required by modern WSNs. The multiple-input multiple-output (MIMO) technique represents a promising method which can significantly increase the system throughput and energy efficiency [2], therefore, MIMO has become a prospective communication scheme for WSNs. Although MIMO exhibits significant advantages in terms of sum rate and energy efficiency, it is still limited by the interference from the unintended users, especially when there are plenty of users. Interference alignment (IA) is a promising technique which can effectively mitigate the interference in the MIMO interference channel, where different users might transmit messages simultaneously via the same frequency band. The main idea of IA is to confine the interference to one half of the signal subspaces, and to recover the desired signal through the other half that is free from interference by designing proper precoding and decoding matrices [3]. [PERSON] and [PERSON] have come to the astonishing conclusion that the sum rate of the MIMO interference channel can increase proportionally with the number of users by applying IA in the high signal-to-noise ratio (SNR) regime [4]. The degrees of freedom (DoFs) of the MIMO interference channel were further studied theoretically in [5], which showed that IA can achieve the maximal DoFs. In addition, the conclusions above were verified experimentally over the measured MIMO orthogonal frequency division-multiplexing (OFDM) channels in [6], and the viability of IA was substantiated. Due to its excellent performance in suppressing interference and increasing the throughput, IA has been applied to relay networks [7], cognitive radio [8], _ad-hoc_ networks [9], _etc._ In particular, [PERSON] _et al._ have validated the feasibility of IA in sensor networks in [10]. Although IA can effectively suppress the interference, the number of users \(K\) in the networks with \(M\) transmitting antennas, \(N\) receiving antennas and \(d\) DoFs is still limited by the IA feasibility constraint, _i.e._, \(K\leq(M+N)/d-1\)[11]. In other words, the scale of the antennas should become larger when the number of users in the networks increases. Unfortunately, the number of antennas is strictly restricted by many limitations such as the equipment volume, power consumption and price. When the amount of users exceeds the IA feasibility constraint, the interference leakage will become so strong that the quality of service (QoS) will degrade significantly and may become unacceptable. To the best of our knowledge, most of the current works on IA were conducted under the assumption that the IA feasibility condition is satisfied, and the methods herein may not perform well when such a constraint cannot be met. Therefore, we focus on this problem in the large-scale networks with limited antennas, where the number of users exceeds that IA can accept. The idea of clustering [12, 13] may provide an alternative approach to address this problem. The users in the networks are divided into several small groups, and IA is applied to each group independently. [PERSON] and [PERSON] [12] proposed a clustered IA scheme by time division multiplexing. Although it can accommodate more users, real-time transmission cannot be guaranteed when there are plenty of users. A scheduled clustered IA for the asymmetric interference networks was proposed by [PERSON] and [PERSON] in [13]. Unfortunately, this method cannot be applied to the conventional symmetric interference channel.