MIMO processing plays a central part towards the recent increase in spectral and energy efficiencies of wireless networks. MIMO has grown beyond the original point-to-point channel and nowadays refers to a diverse range of centralized and distributed deployments. The fundamental bottleneck towards enormous spectral and energy efficiency benefits in multiuser MIMO networks lies in a huge demand for accurate channel state information at the transmitter (CSIT). This has become increasingly difficult to satisfy due to the increasing number of antennas and access points in next generation wireless networks relying on dense heterogeneous networks and transmitters equipped with a large number of antennas. CSIT inaccuracy results in a multi-user interference problem that is the primary bottleneck of MIMO wireless networks. Looking backward, the problem has been to strive to apply techniques designed for perfect CSIT to scenarios with imperfect CSIT. In this paper, we depart from this conventional approach and introduce the readers to a promising strategy based on rate-splitting. Rate-splitting relies on the transmission of common and private messages and is shown to provide significant benefits in terms of spectral and energy efficiencies, reliability and CSI feedback overhead reduction over conventional strategies used in LTE-A and exclusively relying on private message transmissions. Open problems, impact on standard specifications and operational challenges are also discussed. IntroductionPromising approaches for 5G consist in densifying the network by adding more antennas in a distributed or co-localized manner. A distributed deployment leads to dense homogeneous/heterogeneous networks where the widely recognized bottleneck is interference. Interference management relying on multi-point cooperation have drawn a lot of attention in industry (i.e. CoMP in LTE-A [1]) and academia. Co-localized deployment leads to massive MIMO (i.e. FD-MIMO in LTE-A).Although appealing in their concept, those aforementioned MIMO techniques are hampered by several practical factors. Among these, the acquisition of accurate CSI knowledge at the transmitter (CSIT) is the major challenge. The availability of accurate CSIT is crucial for Downlink (DL) multi-user MIMO wireless networks. The beamforming and interference nulling performance heavily depends on the channel estimation accuracy. Unfortunately, pilot reuse tends to impair channel estimation in TDD and a significant feedback overhead is required to guarantee sufficient feedback accuracy in FDD due to the large number of antennas. Delay and inaccurate calibrations of the RF chains also contribute to making the CSIT inaccurate. CSIT inaccuracy results in a multi-user interference and link adaptation problem that is the primary bottleneck of MIMO wireless networks, as highlighted e.g. in [2] for for CoMP.Looking backward, the problem has been to strive to apply techniques designed for perfect CSIT to scenarios with imperfect CSIT. Following the same path will only increase the ...

Simultaneous transmission of information and power over a point-to-point flat-fading complex Additive White Gaussian Noise (AWGN) channel is studied. In contrast with the literature that relies on an inaccurate linear model of the energy harvester, an experimentally-validated nonlinear model is considered. A general form of the delivered Direct Current (DC) power in terms of system baseband parameters is derived, which demonstrates the dependency of the delivered DC power on higher order statistics of the channel input distribution. The optimization problem of maximizing Rate-Power (R-P) region is studied. Assuming that the Channel gain is available at both the receiver and the transmitter, and constraining to independent and identically distributed (i.i.d.) channel inputs determined only by their first and second moment statistics, an inner bound for the general problem is obtained. Notably, as a consequence of the harvester nonlinearity, the studied inner bound exhibits a tradeoff between the delivered power and the rate of received information. It is shown that the tradeoff-characterizing input distribution is with mean zero and with asymmetric power allocations to the real and imaginary dimensions. DRAFT rate and delivered power. Just to name a few, frequency-selective channel [2], MIMO broadcasting [3], interference channel [4], [5], relaying [6], [7].One of the major efforts in a Simultaneous Wireless Information and Power Transfer (SWIPT) architecture is to increase the Direct-Current (DC) power at the output of the harvester without increasing transmit power. The harvester, known as rectenna, is composed of a rectifier 1 followed by a low-pass filter.In [8], [9], it is shown that the RF-to-DC conversion efficiency is a function of rectenna's structure, as well as its input waveform. Accordingly, in order to maximize rectenna's DC power output, a systematic waveform design is crucial to make the best use of an available RF spectrum. In [9], an analytical model for rectenna's output is introduced via the Taylor expansion of the diode characteristic function. As one of the main conclusions, it is shown that the rectifier's nonlinearity is key to design efficient wireless powered systems. The design of an efficient SWIPT architecture fundamentally relies on designing an efficient Wireless Power Transfer (WPT) structure as an important building block of SWIPT. The SWIPT literature has so far focused on the linear model of the rectifier, e.g., [2]-[7], whereas, it is expected that considering nonlinearity effect changes the SWIPT design, signalling and architecture significantly. Indeed, in [10],[11], the design of SWIPT waveforms is studied accounting for rectenna's nonlinearity with a power splitter at the receiver. It is shown that superposing deterministic multisines (for power transfer purposes) with Orthogonal Frequency Division Multiplexing (OFDM) symbols modulated with Circularly Symmetric Complex Gaussian (CSCG) zero-mean inputs (for information purposes) enlarges the Rate-Power (R-P) region, compared ...

We focus on a two-receiver Multiple-Input-Multiple-Output (MIMO) Broadcast Channel (BC) and Interference Channel (IC) with an arbitrary number of antennas at each node. We assume an imperfect knowledge of local Channel State Information at the Transmitters, whose error decays with the Signal-to-Noise-Ratio. With such configuration, we characterize the achievable Degrees-of-Freedom (DoF) regions in both BC and IC, by proposing a Rate-Splitting (RS) approach, which divides each receiver's message into a common part and a private part. Compared to the RS scheme designed for the symmetric MIMO case, the novelties of the proposed block lie in 1) delivering additional non-ZF-precoded private symbols to the receiver with the greater number of antennas, and 2) a Space-Time implementation. These features provide more flexibilities in balancing the common-message-decodabilities at the two receivers, and fully exploit asymmetric antenna arrays. Besides, in IC, we modify the power allocation designed for the asymmetric BC based on the signal space where the two transmitted signals interfere with each other. We also derive an outer-bound for the DoF regions and show that the proposed achievable DoF regions are optimal under some antenna configurations and CSIT qualities.

For a pair of (dependent) random variables (X, Y), the following problem is addressed: What is the maximum information that can be revealed about Y , while disclosing no information about X? Assuming that a Markov kernel maps Y to the revealed information U , it is shown that the maximum mutual information between Y and U , i.e., I(Y ; U), can be obtained as the solution of a standard linear program, when X and U are required to be independent, called perfect privacy. The resulting quantity is shown to be greater than or equal to the non-private information about X carried by Y. For jointly Gaussian (X, Y), it is shown that perfect privacy is not possible if the kernel is applied to only Y ; whereas perfect privacy can be achieved if the mapping is from both X and Y ; that is, if the private variables can also be observed at the encoder. Finally, it is shown that when Y is not a deterministic function of X, perfect privacy is always feasible when the mapping has access to both X and Y. 1

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