This paper addresses the problem of channel estimation in multi-cell interference-limited cellular networks. We consider systems employing multiple antennas and are interested in both the finite and large-scale antenna number regimes (socalled "massive MIMO"). Such systems deal with the multi-cell interference by way of per-cell beamforming applied at each base station. Channel estimation in such networks, which is known to be hampered by the pilot contamination effect, constitute a major bottleneck for overall performance. We present a novel approach which tackles this problem by enabling a low-rate coordination between cells during the channel estimation phase itself. The coordination makes use of the additional second-order statistical information about the user channels, which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences. Importantly, we demonstrate analytically that in the large-number-of-antennas regime, the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance. Gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes.

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Device-to-device (D2D) communication is considered as a promising technology for improving both the spectral and energy efficiencies of cellular networks by reusing the resources of conventional cellular users (CUs) for direct communication of two nearby devices in a spatial manner. When the channel between the two D2D devices is highly attenuated, it is necessary to use an intermediate relay to achieve reliable and flexible relay-aided D2D communication. In order to motivate the cooperative relays to participate, it is assumed that they can harvest energy from radio frequency (RF) signals based on the power splitting (PS) protocol as well as renewable energy (RE) sources. However, resource sharing between the cellular and relay-aided D2D links leads to mutual interference that degrades their sum rate. Considering the energyharvesting relays (EHRs) and downlink (DL) resource sharing, this paper aims to maximize the sum rate of both the links without degrading the quality of service (QoS) requirements of all users. Our maximization problem is formulated as a mixed-integer nonlinear programming (MINLP) problem that cannot be solved in a straightforward manner. Therefore, we propose a low complexity algorithm, namely the resource and power allocation with relay selection EH-aided algorithm (RPRS-EH), which determines the reuse partners, the PS factor sub-optimal value with optimal links power allocation, and provides two different strategies for optimal relay selection. The numerical results show the behavior of the proposed algorithm under various parameters as well as its considerable performance when compared to one of the most recent algorithms in terms of the links sum rate and relay energy efficiency.

It can be predicted that the infrastructure of the existing wireless networks will not fill the requirement of the fifth generation (5G) wireless network due to the high data rates and a large number of expected traffic. Thus, a novel deployment method is crucial to satisfy 5G features. Meta-heuristic is expected to be a promising method for the complex deployment optimization problem of the 5G network. This work presents an implementation of a meta-heuristic algorithm based on swarm intelligence, to minimize the number of base stations (BSs) and optimize their placements in millimeter wave (mmWave) frequencies (e.g., 28 GHz and 38 GHz) in the context of the 5G network while satisfying user data rates requirement. Then, an iterative method is applied to remove redundant BSs. We formulate an optimization problem that takes into account multiple 5G network deployment scenarios. Further, a comparative study is conducted with the well-known simulated annealing (SA) using Monte Carlo simulations to assess the performance of the developed model. In our simulation results, we divide the region of interest into two subareas with different user distributions for different network scenarios while considering the intercell interference. The results demonstrate that the proposed approach has better network coverage with low percentage users in outage. In addition, the developed approach has less computational times to reach the desired target network quality of service (QoS). base station (BS) and user for urban micro-cells (UMi) street canyon and urban macro-cells (UMa) scenarios. For BS antenna height of 10 m and 25 m for UMi and Uma, respectively, the distance is 10 m and 35 m for UMi and Uma, respectively [4]. Thus, these very short distances allow better frequency reuse within a dense network coverage area. The usage of high frequencies is expected to be one of the key 5G technology enabling very high data rates and significant increases in capacity. The spectrum at 28 GHz and 38 GHz are still unexploited and have negligible atmospheric gases attenuation as compared to others high-frequency ranges according to International Telecommunication Union (ITU) L-series recommendations. 5G new radio deployments will require ultra-dense network topologies with the usage of high frequencies, which required many new cells, resulting in additional potential deployment challenges. Consequently, where and when to deploy cells while satisfying user data rates requirement will be challenging. To help assess this deployment challenge, the new approach of network planning is needed to meet the demand of 5G networks and beyond. Network planning is vital in order to deploy 5G networks efficiently, it is considered to be a promising solution to satisfy the user data rates requirement in 5G network [5], it depends on various parameters such as geographical area, cells configuration parameters, estimated number of users, estimated number of cells, path loss and propagation models, and frequency reuse patterns [6]. In the proposed model, UMa a...

A subspace method for channel estimation has been recently proposed [1] for tackling the pilot contamination effect, which is regarded by some researchers as a bottleneck in massive MIMO systems. It was shown in [1] that if the power ratio between the desired signal and interference is kept above a certain value, the received signal spectrum splits into signal and interference eigenvalues, namely, the "pilot contamination" effect can be completely eliminated. However, [1] assumes an independently distributed (i.d.) channel, which is actually not much the case in practice. Considering this, a more sensible finite-dimensional physical channel model (i.e., a finite scattering environment, where signals impinge on the base station (BS) from a finite number of angles of arrival (AoA)) is employed in this paper. Via asymptotic spectral analysis, it is demonstrated that, compared with the i.d. channel, the physical channel imposes a penalty in the form of an increased power ratio between the useful signal and the interference. Furthermore, we demonstrate an interesting "antenna saturation" effect, i.e., when the number of the BS antennas approaches infinity, the performance under the physical channel with P AoAs is limited by and nearly the same as the performance under the i.d. channel with P receive antennas.Index Terms-massive MIMO, physical channel, subspace method, random matrix theory, asymptotic eigenvalue distribution 1536-1276 (c)