More than 10 billions connected devices are predicted for 2020. While the mobile data
continues to grow, future mobile networks are expected to deliver on improved spectral
efficiencies, reduced latencies, better and more consistent throughput experience
in the radio cell. The conventional and still prevailing approach to optimize the radio
resources places the radio devices under the tight control of a central coordinator.
Nevertheless, such approach makes the mobile network dependent on a considerable
amount of measurement data that must be communicated in real-time to the centralized
processor, which is impossible or undesirable in some practical cases. Moreover,
the centralized approach ignores the computational and decision making capabilities
of the modern radio devices such as smartphones, drones and connected cars, with
great chance for direct device-to-device communication. Decentralized optimization
methods are thus viewed with increased interest for future mobile networks.
In the context of 5G and 5G+ mobile networks, massive multi-antenna transmission
is an established technique to manage multi-user interference and improve the
network performance through beamforming and multiplexing gain. In the massive
antenna regime, the leading forms of distributed cooperation that can be envisioned
are i) the beam selection and alignment across multiple mobile users – in particular, at
mmWave frequencies – and ii) the cooperation among base stations for user scheduling,
whose centralized solution requires significant coordination and resource overhead.
In this thesis, we focus on decentralized cooperative methods for massive multiantenna
transmission optimization that are implemented at the cooperating devices
themselves. We first tackle the beam alignment and selection problem from both singleuser and multi-user perspectives, where the radio devices coordinate their beam strategies using long-term spatial side-information such as location information, to reduce the coordination overhead. In particular, we consider the important limitation factors which hinder perfect coordination such as the measurement noise and the limited information exchange capabilities between the cooperating nodes, so as to introduce robust approaches to side-information-aided beam selection and overcome conventional schemes unsuitable to the distributed information configuration. In parallel, we show that multi-user beam selection in the massive antenna regime must deal with an interesting trade-off between i) harvesting large channel gain, ii) avoiding catastrophic multiuser interference, and iii) minimizing the channel acquisition overhead. To explore such trade-off, we propose a novel beam-domain coordination framework exploiting lowrate direct device-to-device side-links. Our results demonstrate the effectiveness of the proposed beam selection algorithms.
Since coordination entails some information flowing from one node to the others,
we then expose the existence of an additional, but different trade-off between coordination and user privacy, of high practical relevance. In particular, we consider beamdomain coordination among competing mobile operators for user scheduling in mm-Wave spectrum sharing, where a clear correlation is found between the channel data and the users’ locations. Our proposed privacy-preserving scheduling algorithm exploiting obfuscated beam-related information outperforms the uncoordinated benchmark.
© EURECOM. Personal use of this material is permitted. The definitive version of this paper was published in Thesis and is available at :