Downlink MIMO HetNets: Modeling, Ordering Results and Performance Analysis

Abstract

We develop a general downlink model for multiantenna heterogeneous cellular networks (HetNets), where base stations (BSs) across tiers may differ in terms of transmit power, target signal-to-interference-ratio (SIR), deployment density, number of transmit antennas and the type of multi-antenna transmission. In particular, we consider and compare space division multiple access (SDMA), single user beamforming (SU-BF), and baseline single-input single-output (SISO) transmission. For this general model, the main contributions are: (i) ordering results for both coverage probability and per user rate in closed form for any BS distribution for the three considered techniques, using novel tools from stochastic orders, (ii) upper bounds on the coverage probability assuming a Poisson BS distribution, and (iii) a comparison of the area spectral efficiency (ASE). The analysis concretely demonstrates, for example, that for a given total number of transmit antennas in the network, it is preferable to spread them across many single-antenna BSs vs. fewer multiantenna BSs. Another observation is that SU-BF provides higher coverage and per user data rate than SDMA, but SDMA is in some cases better in terms of ASE.

Joint Precoding and Load Balancing Optimization for Energy-Efficient Heterogeneous Networks

“…Moreover, load balancing was analyzed in [14]- [23] for systems where the BSs are distributed according to stochastic point processes. Using stochastic geometry tools, these works have compared how different BS association rules (e.g., the nearest-BS based, the highest-received-power based, the maximum signal-to-interference-and-noise ratio (SINR) based, and the biased-SINR based cell selection) affect the downlink SINR distribution [15]- [17] and the average achievable rate [17]- [21].…”

“…We note that the results in [14]- [19], [21], [22] are limited to BSs with single antennas, while contemporary and future networks use multiple antennas for downlink precoding. The papers [20] and [23] consider the practically important case of multi-antenna BSs, but these results are restricted to single-cell zero-forcing precoding with perfect CSI; in contrast, imperfect CSI and inter-cell interference coordination are essential properties of future heterogeneous networks. Moreover, shadowing has a great impact on the system performance of heterogeneous networks, but was not considered in [14], [15], [17], [18], [20]- [23], probably due to mathematical intractability.…”

“…Thus, we obtain the global optimal solution by an exhaustive search over all possible BS mode combinations. The obtained global optimal solution serves as an upper bound for any other suboptimal precoding and load balancing solutions, e.g., the strategies proposed in [6], [7], [14]- [23].…”

Resource Allocation for Energy Efficiency in 5G Wireless Networks

“…Normally, the MUEs present users with high mobility and SUEs assign users with static and low mobility. Similar to conventional HetNets, interferences is still a major problem in massive MIMO HetNets which leave many challenges for researchers [12,15]. However, besides the benefits of massive MIMO, the beamforming design, which involves a large-scale size of beamformer, leads to the dramatically high computational complexity.…”

Massive MIMO Wireless Networks: An Overview

“…In addition, the numerical integrals need to be computed. Authors of [167] studied multi antenna HetNets with zero-forcing precoding. They compared the coverage probability and rate per user for both open access (where users are allowed to access any BS in any tier) and closed access networks (where users are granted access to certain BSs in restricted tiers).…”