The scope of the present thesis is twofold. Firstly it is meant to provide an overview of the application of multibeam joint processing techniques in the forward and return link of satellite networks. These techniques
1.6. Scope 37
have the potential of being incorporated in future satellite payloads with minor modifications on the state of the art equipment. To this end, the performance gains of multibeam joint processing techniques are derived under realistic assumptions.
Secondly, novel signal processing methods –inspired by the inherent attributes of SatComs– are developed, towards enabling the adoption of joint processing over satellite. Therefore, the main practical constraints in current systems that may inhibit the application of precoding are iden- tified and tackled. Thus, the current state of the art in signal processing techniques are extended to account for practical constraints that are of utmost importance for SatComs.
The present thesis is structured as follows. In Ch. 2 the problem under investigation is analytically formulated. A detailed description of the applicable system model is presented, the related state of the art is discussed and the contributions over it are explicitly given. Following this, the technical contributions are described in Ch. 3, 4, 5 and 6, while Ch. 7 concludes the findings of this thesis.
In more detail, Ch. 3 presents closed-form derivations that describe the capacity of the RL of multibeam joint decoding satellites, under real- istic channel assumptions. In Ch. 4, the extension of a fundamental signal processing problem, namely the multigroup multicasting, to account for individual constraints on the per-antenna transmit power is given. More- over, the application of precoding in the FL of aggressive frequency reuse multibeam satellites is investigated under realistic system level assump- tions in Ch.5. Therein, novel signal processing methods are developed for precoding over satellite. Following this, Ch. 6 proposes the exploitation of user scheduling towards further enhancing the performance of the multi- beam satellite transmitter. In this direction, novel scheduling algorithms are presented. Finally, the results of the thesis are summarized in Ch. 7, along with the way forward.
Chapter 2
Problem Formulation
Multibeam Joint Processing is tackled in the present thesis from a PHY perspective. In the present chapter, the problem of multibeam joint pro- cessing is formally defined. The problem formulation is based on the baseband MIMO signal model. In this context, the main point of refer- ence is the so-called channel matrix H. Towards accurately modeling the inherent attributes of the satellite channel, the present work proposes a generalized multibeam satellite channel model. Thus, the channel matrix H will include the inherent attributes of the baseband memoryless satel- lite channel. This model has the flexibility to include (a) the multibeam antenna characteristics, (b) the rank deficiencies introduced by the Line- of-Sight (LoS) signal components and the antenna correlation, (c) the shadowing due to user mobility and (d) the rain fading. In parallel with the problem formulation, previous related works that are based on similar models are also described in the present chapter. The contributions of this thesis over these SoA works, will conclude the chapter.
2.1
Multiuser MIMO communications
Starting from the early years of the past decade, MIMO communications are nowadays established as the most promising transmission method for the future broadband wireless communications. The concept of jointly processing the signals at the multi-antenna array of the receiver and/or the transmitter, is enabling the conversion of the interference RL and/or
FL channels of the MIMO system into a Broadcast Channel (BC) and/or Multiple Access Channel (MAC) respectively. Before proceeding to the transceiver architectures, the general vector channel model of MIMO com- munications will be introduced.
2.1.1 Signal Model
When multiple independent user terminals (herein refereed to as users) communicate with a single multi-antenna transceiver, then a MU MIMO system is realized. Let Ntdenote the number of transmit antennas and Nu
the total number of users simultaneously served. Under the assumption of multiple single antenna users up-linking to a multi-antenna receiver, the received signal at the at the j-th receive antenna is given as
yj= h†j,U Lx + nj, (2.1)
where hj,U Lis a Nu× 1 vector composed of the baseband channel coeffi-
cients (i.e. channel gains and phases) between the Nuusers and the j-th
antenna of the receiver, x is the Nu× 1 vector of the transmitted symbols
and njis the independent complex circular symmetric (c.c.s.) independent
identically distributed (i.i.d) zero mean Additive White Gaussian Noise (AWGN), measured at the j-th receive antenna. Equivalently, assuming a multiple antenna transmitter and multiple single antenna receivers, the received signal at the i-th user reads as
yi= h†i,DLx + ni, (2.2)
where hi,DL is a Nt× 1 vector composed of the baseband channel coeffi-
cients between the i-th user and the Ntantennas of the transmitter, while
x is the Nt× 1 vector of the transmitted symbols and niis the AWGN,
measured at the i-th user’s receive antenna.
Clearly, the general input output signal model of MIMO communica- tions can apply for the forward and the return links. By collecting all user channels in one channel matrix, the general linear signal model in vector form reads as
y = Hx + n, (2.3) where the vector dimensions depend on the link under study. Therefore, the channel matrix H constitutes the main point of references in the baseband modeling of the physical layer of MIMO communications.