**1. Introduction**

Data traffic over wireless communication networks has experienced a tremendous growth in the last decade, and it is predicted to exponentially increase in the next decades [1]. Enabling future wireless networks to fulfill this expectation is a challenging task both due to the scarcity of radio resources (e.g. spectrum and energy), and also the inherent characteristics of the wireless transmission medium. Wireless transmission is in general subject to two phenomena: *fading* and *interference*. The former is a consequence of reflectors scattered in the environment surrounding a transmitter and a receiver such that the receiver observes a superposition of multiple copies of the transmitted signal. The superposition of the signals can be either constructive or destructive depending on the phase shift and attenuation of the received signals from different paths. The randomness of fading may degrade communication quality. Several effective techniques have been developed to overcome the adverse effects of random fading. For instance, multiple antennas transmission techniques are proposed to realize spatial diversity and to improve the performance of wireless systems in the presence of fading [2]. In addition, in multi-user networks because of the broadcast nature of wireless transmission medium, each user's communication is interfered by other users. Inter-user interference can severely degrade the communication quality and makes communication of different users interrelated; thus, finding the optimum interference management strategy becomes a chal‐ lenging problem.

Conventional interference management strategies including time-division multiple access (TDMA) and frequency-division multiple-access (FDMA) avoid the inter-user interference by

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allocating orthogonal resources in time and frequency to different users, respectively. Inter‐ ference is consequently avoided at the cost of low spectral efficiency. Thus, it was believed that the performance of wireless networks is limited by interference in general. However, the elegant *interference alignment* concept [3], [4] reveals that with proper transmission signalling design, different interference signals can in fact be aligned together, such that more radio resources can be assigned to the desired transmission. For instance, in the case of a multi-user interference network with more than two source–destination pairs, the interference signals at each destination can be aligned such that maximally half of the signal space can be left to its desired signal [4]. Therefore, each user may achieve half of the interference-free transmission rate no matter how many interferers exist [4]. Although interference alignment can achieve a larger data rate compared to orthogonal transmission strategies, several challenges should be addressed to enable the deployment of this technique in future wireless networks [1], [5]. For instance, to perform interference alignment, normally, global channel state information (CSI) is required to be perfectly known at all terminals. Clearly, acquiring such channel knowledge is a challenging problem in practice and proper channel training and channel state feedback techniques need to be deployed. In addition, since the channels are time-varying proper adaptive transmission is needed.

To investigate whether the outstanding performance of signal processing algorithms inspired by interference alignment can be preserved in real environment, practical verifications is needed. Wireless test-beds (e.g. the ones based on USRP or WARP) can be used as a platform for the experimental verification of the novel interference management algorithms.

This chapter review recent advances in practical aspects of interference alignment. It also presents recent test-bed implementations of signal processing algorithms for the realization of interference alignment. In Section 2 we give a brief overview on the interference alignment concept. Section 3 presents the structure of a canonical transmitter and receiver to realize interference alignment, and discuss channel training and channel state feedback for these systems. A brief review on test-bed implementations of interference alignment solutions is presented in Section 4. Section 5, introduces hardware and software setup of the test-bed used in this chapter for implementation of interference alignment. The test-bed implementation of iterative transceiver design and power control algorithm is presented in Section 6. We discuss the test-bed implementation of compressed feedback scheme for interference alignment scheme in Section 7. Finally, Section 8 concludes the chapter.
