Miltiades C. Filippou

Decontaminating pilots in massive MIMO systems

Pilot contamination is known to severely limit the performance of large-scale antenna (“massive MIMO”) systems due to degraded channel estimation. This paper proposes a twofold approach to this problem. First we show analytically that pilot contamination can be made to vanish asymptotically in the number of antennas for a certain class of channel fading statistics. The key lies in setting a suitable condition on the second order statistics for desired and interference signals. Second we show how a coordinated user-to-pilot assignment method can be devised to help fulfill this condition in practical networks. Large gains are illustrated in our simulations for even small antenna array sizes.

Decontaminating pilots in cognitive massive MIMO networks

Cognitive radio has been lately suggested as a promising technology in order to improve spectrum utilization. This paper addresses the problem of channel estimation in an underlay interference-prone cognitive radio setup. We consider a primary and a secondary base station, both with multiple antenna capability and serving multiple users. Although previous studies propose the use of beamforming to handle secondary-caused interference, this cannot be done in practice unless channels are correctly estimated in the first place. However channel estimation itself is plagued by interference (pilot contamination effects). Therefore we propose a method to address channel estimation at the primary system while removing contamination caused by the secondary transmitter. The approach is twofold: (i) We develop a robust channel estimator which makes use of covariance information. We show analytically that the performance of this estimator is identical to the interference free scenario under certain when the number of antennas becomes large under a condition on the distribution of the multipath model. (ii) We build a pilot assignment algorithm which seeks to fulfill this condition. Significant gains are reported.

A Comparative Performance Analysis of Interweave and Underlay Multi-Antenna Cognitive Radio Networks

In this paper, an analytical performance study for multi-antenna Cognitive Radio (CR) systems is presented. The two most popular CR approaches, namely, the interweave and underlay system designs, are considered and based on the derived analytical framework, a throughput-based comparison of these two system designs is presented. The system parameters are selected such that a quality of service (QoS) constraint on primary communication, is satisfied. Closed form expressions for the outage probability at the Primary User (PU), as well as expressions for the ergodic rate of the Secondary User (SU) are derived, for both system designs. The derived expressions are functions of key design parameters, such as the sensing time and the detection threshold in the case of interweave CR, and the maximum allowable interference power received by the PU, in the case of underlay CR. Based on the derived expressions, for interweave CR, the sensing parameters, i.e., sensing time and energy detection threshold, are optimized such as to maximize the secondary system rate. By comparing the throughput performance for both system designs, the existence of specific regimes (in terms of primary activity, number of transmit and receive antennas as well as the outage probability of the PU), where one CR approach outperforms the other, is highlighted.

Optimal Combining of Instantaneous and Statistical CSI in the SIMO Interference Channel

The uplink of the two-user multiple-antenna interference channel is considered and the optimal (in the ergodic rate sense) beamforming (BF) problem is posed and solved. Specifically, a feedback scenario is studied whereby a base station (BS) is allowed to estimate the instantaneous channel vector information from the users it serves, but not from out-of-cell interference users. That is to say, only statistical information can be obtained regarding the interference. In contrast with most previous works, the motivation behind the presumed feedback scenario is the compliance with current cellular network standards. For this scenario, we derive new expressions for the ergodic user rates. Exploiting the derived expressions, the optimal, with respect to ergodic rate maximization, receive BF vectors are found in closed-form. Finally, new user scheduling schemes are proposed, which exploit the derived BF solution and allow an efficient use of combined instantaneous and statistical information.

New Radio 5G User Plane Design Alternatives: One 5G Air Interface Framework Supporting Multiple Services and Bands

The standardization of 5G, referred to as new radio (NR), has started. The 5G air interface (AI) is envisioned to comprise a number of AIs including the evolution of Long Term Evolution (LTE) and new NR AIs operating at frequencies from below 1GHz and up to 100GHz. One of the key challenges is the design of an overall 5G AI that efficiently combines these AIs into one 5G AI framework. This paper examines how the AIs can be harmonized into a single 5G AI framework, while minimizing the complexity of standardization and implementation. The harmonization among the 5G AIs could take place at any layer: at the physical layer (PHY), at the Medium Access Control layer (MAC) or at the Packet Data Convergence Protocol (PDCP) layer. Among various detailed harmonization options that are being investigated in this context, for the integration of the new AIs at the physical layer, cyclic prefix-orthogonal frequency division multiplexing (CP-OFDM) waveform with various numerologies is considered as a basis and the MAC layer harmonization can be built on top of the PHY layer, where the CP-OFDM-related AIs of different numerologies are catered for different future services, bands, and cell types. Also, harmonization at the PDCP layer appears to be the most viable harmonization option between the evolved LTE and the new AIs. The results of the first conducted 5G AI analysis will constitute discussion topics for other 5G-PPP projects as well as 3GPP.

Statistically coordinated precoding for the MISO cognitive radio channel

In this work1, we study a cognitive radio setting consisting of a primary multiple-antenna transmitter (TX) serving a single-antenna primary user (PU) and a secondary multiple-antenna TX serving a secondary user (SU). The main specificity of this work is that we let the primary TX coordinate its transmit strategy with the secondary TX, while considering a realistic channel state information (CSI) scenario where each TX has solely access to the instantaneous knowledge of its direct channel and the statistics of the multi-user channel. This setting gives rise to a Team Decision problem where the TXs aim at cooperating on the basis of individual information. We develop a novel coordination scheme where the TXs coordinate without any exchange of information or any iteration to guarantee the fulfillment of the primary constraint while maximizing the rate of the SU. The coordination is done on the basis of statistical information such that the coordination can be optimized offline. The proposed scheme outperforms conventional schemes from the literature and has low complexity. It can thus be used in settings with low signal processing capabilities and a weak backhaul infrastructure.

A team decisional beamforming approach for underlay cognitive radio networks

In this paper, the problem of the coexistence of two multiple-antenna wireless links is addressed in a cognitive radio scenario. The novelty brought by our setup is three-fold: First we consider a more realistic rate target constraint at the primary receiver instead of the less meaningful maximum interference temperature, second we propose a limited channel state information (CSI) structure whereby transmitters only have access to partly instantaneous feedback (i.e., about the direct channels) and partly statistical feedback (i.e., about the interference channels). Third, we formulate a distributed decision making scenario, by which channel information is not shared among primary and secondary transmitters. Instead, a transmitter must make a precoding decision based on local CSI only. The problem is recast as a team decision theoretic problem and the optimal precoders are obtained by solving semidefinite programs (SDPs). A distributed algorithm is derived and compared with classical precoding solutions and gains are illustrated over a range of scenarios.

5G Multi-RAT Integration Evaluations Using a Common PDCP Layer

5G is expected to operate in a wide frequency range to support new challenging use-cases. Multi- RATs (Radio Access Technologies): NR (New Radio) and evolved LTE (Long Term Evolution) will together constitute 5G. Utilizing NR at high frequencies will have a significant impact on radio propagation conditions with e.g. unfavorable higher path loss and increased outdoor-to-indoor penetration losses. In order to provide a reliable communication from the outset of 5G deployment and to minimize the standardization and implementation complexity, 5G UP (User Plane) instances of 5G AIs (Air Interface) related to evolved LTE and NR need to be aggregated on a certain layer of the protocol stack. This paper sheds light on how to integrate 5G AIs into a single 5G AI framework and explores which protocol stack layer could be used as aggregation layer. Inter-RAT hard handover is the state of the art technique to integrate multiple RATs in order to support mobility and reliability across different RATs. However, the hard handover incurs a transmission interruption which stands as an obstacle along the way of accomplishing 5G design. According to simulation results, a common PDCP (Packet Data Convergence Protocol) layer improves the hard handover functionality and stands out as a basis for tight interworking between evolved LTE and NR. By means of simulation, it is shown that the multi-RAT UP aggregation can achieve three times higher user throughput, when NR is using 28 GHz and LTE 2 GHz, compared to stand-alone NR.

Geometry Investigation of L.R.S. Synchronous Machines Using FEM

In this paper a geometry investigation of low rotational speed synchronous machines using finite element method is presented. The abovementioned investigation is accomplished using a two step design procedure for salient poles synchronous machines, while a respective example which will be shown synoptically is an under scale design of a low rotational salient pole synchronous motor connected to a ships propeller axis directly.Keywords: Geometry investigation, ships electric propulsion, finite element method, synchronous motor.