Abstract
We study support for unmanned aerial vehicle (UAV) communications through a cell-free massive MIMO architecture, wherein a large number of access points (APs) is deployed in place of large co-located massive MIMO arrays. We consider also a variation of the pure cell-free architecture by applying a user-centric association approach, where each user is served only from a subset of APs in the network. Under the general assumption that the propagation channel between the mobile stations, either UAVs or ground users (GUEs), and the APs follows a Ricean distribution, we derive closed form spectral efficiency lower bounds for uplink and downlink with linear minimum mean square error channel estimation. We consider several power allocation and user scheduling strategies for such a system, and, among these, also minimum-rate maximizing power allocation strategies to improve the system fairness. Our numerical results reveal that cell-free massive MIMO architecture and its low-complexity user-centric alternative may provide better performance than a traditional multi-cell massive MIMO network deployment.
Highlights
U NMANNED aerial vehicles (UAVs)— referred to as drones—have attracted a great deal of attention in the last few years, both in industry and accademia, due to their ability of performing a wide variety of critical tasks efficiently and in an automated manner
- For the downlink, i) we consider a proportional power allocation strategy, ii) we introduce a waterfilling-based power allocation for the CF approach only, and iii) we derive a power control rule aimed at the maximization of the minimum of the spectral efficiencies across the users, using the successive lower-bound maximization technique [32]–[34]
W, of two different network topologies according to the number and characteristics of the access points (APs) deployed: 1) CF and UC architectures with NA = 100 APs comprised of NAP = 4 antennas each
Summary
U NMANNED aerial vehicles (UAVs)— referred to as drones—have attracted a great deal of attention in the last few years, both in industry and accademia, due to their ability of performing a wide variety of critical tasks efficiently and in an automated manner. The second research approach focuses on the communications services that wireless networks can provide to UAVs [12]–[17]. CELL-FREE NETWORK TOPOLOGY we consider a network that consists of outdoor APs, GUEs, and UAVs, whose sets are denoted by A, G, and U , and have cardinalities NA, NG, and NU, respectively. We let the term users denote both GUEs and UAVs. The NA APs are connected by means of a backhaul network to a CPU wherein data-decoding is performed.. Where Kk,a is the Ricean K-factor, hk,a ∈ CNAP contains the small-scale fading i.i.d. CN (0, 1) coefficients between the a-th AP and the k-th user, and θk,a follows a uniform distribution in [0, 2π ], denoting the random phase offset for the direct path. AP pLOS(dk,a) is LOS, which, in turn, depends on the link length dk,a following [38]
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