| dc.description.abstract | In this thesis, we address the problem of large training/feedback overhead and the requirement
of computationally intensive algorithms to determine the phase angles of the analog
precoder for downlink data transmission in hybrid analog-digital (A-D) multi-user (MU)
millimeter wave (mmWave) systems. We investigate the use of opportunistic beamforming
(OBF) using a dumb analog precoder as a solution to these issues. The OBF based
schemes work as follows. The BS transmits a known pilot symbol over a random analog
precoding vector. Using this, all the users in the system estimate their effective channels,
and the best user efficiently feeds back its SNR to the BS, e.g., using a timer-based scheme.
Then, the BS schedules the best user using the chosen analog precoding vector. Since the
randomly chosen precoding vector is likely to be optimal to a subset of the users, and since
the best user is selected in each time slot, deep fades at any given user are avoided, and
the overall system throughput improves. Note, also, that this scheme requires very little
feedback and no optimization of the analog precoding vector is necessary.
This thesis has two parts. In the first part, we consider a single radio frequency (RF)
chain at the base station (BS). We analyze two schemes of OBF, called fully random
precoder (full RP) and channel structure-aware random precoder (CSA-RP). In the full
RP scheme, we use random phase angles across the antenna array. In the CSA-RP scheme,
we consider the use of structured random beams, where the phase angles of the analog precoder are chosen so as to have the same structure as the array response of the geometric
channel model of mmWave systems. For the CSA-RP scheme, we derive the asymptotic
scaling laws of the average throughput as a function of the number of users, number of
antennas, and the SNR, using extreme value theory, as the number of users gets large.
Our simulation results show that the second scheme achieves near-optimal throughput,
i.e., close to that achieved using coherent beamforming with best user selection (called the
maximum rate baseline (max-rate BL) scheme, since the user obtaining the maximum rate
is scheduled to be served), via opportunistic selection among fewer users in the system
compared to the first scheme. Next, we use M pilot symbols with M different random
orthonormal analog precoding vectors instead of a single pilot symbol, to further reduce
the number of users required to obtain near-optimal throughput. We derive the scaling
law of the average throughput for OBF using M random orthonormal precoding vectors
also.
In the second part, we consider the case where the BS is equipped with multiple RF
chains. We propose two schemes (called greedy high and greedy low) with OBF to simultaneously
serve multiple users, with very low feedback overhead. The BS randomly
generates an analog precoding matrix whose columns form a set of random orthonormal
precoding vectors, and transmits pilot symbols using the precoding matrix. Each user measures
the SINR for each of the random precoding vectors. The two schemes we consider
differ in terms of how many beams are assigned to the users in each round of feedback: the
greedy high scheme entails a larger number of rounds of feedback compared to the greedy
low scheme, but is purportedly better in its performance. Through simulations, we find
that the two schemes offer nearly identical throughputs, and thus conclude that the greedy low scheme with lower feedback overhead is more attractive for practical implementation.
Furthermore, we derive the average throughput scaling laws using extreme value theory
when many users exist in the system for both schemes.
The results in this thesis show that as long as there are a reasonable number of users in
the system, OBF is an attractive, low-complexity, and low-feedback approach for practical
implementation of MU mmWave MIMO communications | en_US |
