## 1 Introduction  
Today's modern cellular communication networks are witnessing an ever increasing demand for higher data rates and link reliability. A major hindrance in meeting these demands is scarcity of the electromagnetic spectrum. One of the key solutions to effectively deal with these challenges is to spatially reuse the available spectrum. This is achieved by employing multiple antennas at the access point and/or user equipment (UE), thus enabling the propagation channel to provide more degrees of freedom. Such multiple-input multiple-output (MIMO) communication systems are generally categorized into point-to-point MIMO or single-user MIMO [1,2] and multi-user MIMO (MU-MIMO) [3,4]. MIMO systems make use of spatial multiplexing to provide an order of increase in the capacity while achieving link reliability by employing space-time coding techniques. MU-MIMO has been implemented in many wireless standards such as 802.11 (WiFi), 802.16 (WiMax), and long-term evolution (LTE), e.g., LTE-Advanced allows up to eight antenna ports at the base station (BS). Moreover, it is envisioned that the future 5G cellular networks will increase the system capacity by a thousand folds and energy efficiency by hundred folds, in addition to decreasing the system latency by ten folds [5]. Massive-MIMO, however, in contrast to the conventional MIMO, is a MU-MIMO technology that employs a large number (on the order of 100 or more) of physically small, low power and independently-controlled antennas at the BS in order to serve a number of single-antenna terminals using the same time and frequency resources [6]. Equipping the BS with a large number of antennas results in manifolds increase in spectral and energy efficiency as compared to conventional MIMO systems. The increase in spectral efficiency is a result of aggressive spatial multiplexing to transmit data streams for a desired user [7]. Whereas the increase in energy efficiency is a consequence of producing sharp beams with the help large number of antennas at the BS, such energy is focused into smaller spatial regions i.e., for intended users only [7]. Furthermore, employing a large number of antennas at the BS results in a favorable propagation channel because the channel vectors and users become pair-wise orthogonal; thus, linear processing becomes optimal [8]. Moreover, in order to benefit from a large amount of under-utilized millimeter wave (mmWave) spectra, an mmWave based massive-MIMO system has also been proposed for the backhaul of future 5G ultra-dense networks (UDN) [5]. Therefore, massive-MIMO is foreseen as a potential technology for future 5G cellular communication networks. It has been established in literature that the channel impulse response CIR in several wireless communication scenarios tends to exhibit a sparse structure that gets pronounced with growing signal dimensions [9,10]. For example, a sparse structured CIR is exhibited by underwater acoustic communication channels [11]; wideband high frequency channels [12]; high-definition television (HDTV) channels [13], and cellular communication channels, where spatially spread distant scatterers correspond to the arrival of signals [14]. It has been demonstrated in [15] that in propagation environments with an insufficient number of scatterers, the physical MIMO channels tend to exhibit a sparse structure in CIR. Furthermore, in [16], it has been shown that in MIMO systems the time of arrival (ToA) at different antennas is similar. Therefore, the different uplink channels exhibit a CIR that tends to possess a common support. Moreover, MIMO communication channels exhibit joint sparsity across the channel components because of the smaller antenna spacings as compared to the propagation paths' lengths [17]. In massive-MIMO, several experimental studies have established the fact that CIR tends to be jointly sparse due to the shared common local scattering clusters [18]. In [19], authors have explored the joint sparsity of massive-MIMO channels in virtual angular domains.