Service diversity is expected in the upcoming fifth-generation (5G) cellular networks, which poses great challenges to the underlying waveforms to accommodate heterogeneous service requirements in a flexible way. By dividing the bandwidth into several subbands, each having a different numerology, this paper reports a field trial in time division duplex downlink conducted on a configurable test bed in a real-world environment for the performance evaluations of orthogonal frequency-division multiplexing (OFDM)-based 5G waveform candidates, i.e., cyclically prefixed OFDM (CP-OFDM), windowing OFDM (W-OFDM), and filtered OFDM (f-OFDM), in the presence of mixed numerologies. Field trial results confirm the feasibility of mixed numerologies and reveal the impact of several important system parameters, e.g., guard bandwidth, data bandwidth, signal-to-noise ratio (SNR), and transmit power. The results also suggest that f-OFDM outperforms CP-OFDM and W-OFDM in terms of both the spectrum efficiency and robustness in a high SNR regime, and the gain increases with a higher inter-numerology out-of-band interference. In some specific scenarios, ideal spectrum utilization can be realized by f-OFDM which completely removes the guard band.

A mathematical framework for system-level analysis and optimization of uplink cellular networks that use optimum combining at the Base Stations (BSs) is introduced. Fractional power control is explicitly taken into account. The locations of the BSs are modeled as points of a homogeneous Poisson point process. With the aid of stochastic geometry, coverage and rate are formulated in integral but mathematically tractable expressions. Based on them, performance trends for small-and large-scale multi-antenna BSs are discussed. Coverage and rate are shown to be highly dependent on several parameters, including the path-loss exponent, the fractional power control compensation factor and the maximum transmit power of the mobile terminals. Based on the proposed frameworks, a heuristic algorithm for system-level optimization is proposed and its effectiveness is demonstrated with the aid of Monte Carlo simulations.

In this paper, mathematical frameworks for system-level analysis and design of uplink heterogeneous cellular networks with multiple antennas at the base station (BS) are introduced. Maximum ratio combining (MRC) and optimum combining (OC) at the BSs are studied and compared. A generalized cell association criterion and fractional power control scheme are considered. The locations of all tiers of BSs are modeled as points of homogeneous and independent Poisson point processes. With the aid of stochastic geometry, coverage probability and average rate are formulated in integral but mathematically and computationally tractable expressions. Based on them, performance trends for small- and large-scale multiple-antenna BSs are discussed. Coverage and rate are shown to highly depend on several parameters, including the path-loss exponent, the fractional power control compensation factor, and the maximum transmit power of the mobile terminals. The gain of OC compared with MRC is proved to increase, if a more aggressive power control is used and if the number of BS antennas increases but is finite. For the same number of BS antennas, OC is shown to reach the noise-limited asymptote faster than MRC. All findings are validated via Monte Carlo simulations.

The intensity matching approach for tractable performance evaluation and optimization of cellular networks is introduced. It assumes that the base stations are modeled as the points of a Poisson point process (PPP) and leverages stochastic geometry for system-level analysis. Its rationale relies on observing that system-level performance is determined by the intensity measure of transformations of the underlaying spatial PPP. By approximating the original system model with a simplified one, whose performance is determined by a mathematically convenient intensity measure, tractable yet accurate integral expressions for computing area spectral efficiency and potential throughput are provided. The considered system model accounts for many practical aspects that, for tractability, are typically neglected, e.g., line-of-sight (LOS) and non-LOS propagation, antenna radiation patterns, traffic load, practical cell associations, and general fading channels. The proposed approach, more importantly, is conveniently formulated for unveiling the impact of several system parameters, e.g., the density of base stations and blockages. The effectiveness of this novel and general methodology is validated with the aid of empirical data for the locations of base stations and for the footprints of buildings in dense urban environments.

In this paper, an approximation to the mathematical evaluation of the spectral and energy efficiency of multi-antenna downlink cellular networks is introduced. The locations of the Base Stations (BSs) are modeled as points of a Poisson Point Process (PPP) and stochastic geometry is used for system-level performance assessment. The approximation is applicable to spatial multiplexing Multiple-Input-Multiple-Output (MIMO) systems having an arbitrary number of antennas at the transmitter (Nt) and at the receiver (Nr). A Rayleigh fading channel model is assumed. The accuracy of the proposed approximation is substantiated with the aid of Monte Carlo simulations.

In this paper, a mathematical framework for evaluating the error probability of downlink Multiple-Input-Multiple-Output (MIMO) cellular networks is introduced. It is based on the Poisson Point Process (PPP)-based abstraction for modeling the spatial locations of the Base Stations (BSs) and it exploits results from stochastic geometry to characterize the distribution of the other-cell interference. The framework is applicable to spatial multiplexing MIMO systems with an arbitrary number of antennas at the transmitter (Nt) and at the receiver (Nr). It is shown that the proposed framework leads to easy-to-compute integral expressions, which provide insights for network design and optimization. The accuracy of the mathematical analysis is substantiated through extensive Monte Carlo simulations for various MIMO cellular network setups.

In this paper, a mathematical framework to the computation of the error probability of downlink cellular networks is introduced. It is based on the Poisson point process (PPP)-based abstraction for modeling the spatial locations of the base stations (BSs), and it exploits results from stochastic geometry for characterizing the distribution of the other-cell interference. The framework is applicable to spatial multiplexing multiple-input-multiple-output (MIMO) systems with an arbitrary number of antennas at the transmitter (Nt) and at the receiver (Nr). If Nt= Nr= 1, the mathematical approach can be used for arbitrary fading distributions on both useful and interfering links. If either Nt> 1 or Nr> 1, it can be applied to arbitrary fading distributions on the useful link and to Rayleigh fading on the interfering links. It is shown that the proposed approach leads to easy-to-compute integral expressions, which reduce to closed-form formulas in some asymptotic regimes. Furthermore, the framework is shown to provide insights for system design and optimization. The accuracy of the mathematical analysis is substantiated through extensive Monte Carlo simulations for various cellular network setups.