Millimeter wave (mm-wave) communication is a topic of intensive recent study, as it allows to significantly boost data rates of future 5G networks. In this paper, we focus on a mm-wave system consisting of a single Access Point (AP) and two User Equipments (UEs), where one UE requires high throughput, while the other is characterized by a low latency demand. Given that setup, we aim at optimally allocating the available AP hardware resources for the beam training phase and data communication, in order to efficiently serve both UEs via hybrid analog-digital beamforming. We evaluate an optimization framework with the objective to maximize the expected rate of one UE, for a given latency constraint set by the other UE. The optimal data rates are illustrated for different latency constraints and for different strategies of exploiting the full RF chain set at the AP side. We observe that our proposed access schemes outperform the basic TDMA approach by up to 22 %.

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.

Recently, the new concept of Licensed Shared Access/Authorized Shared Access (LSA/ASA) has emerged as a feasible commercial version of dynamic spectrum reuse based on Cognitive Radio (CR) technologies, e.g., via spectrum sensing or by exploiting geo-location information. This paper considers the problem of effective spectrum sharing between a colocated Multiple-Input-Multiple-Output (MIMO) radar that monitors the existence of a target and a wireless communications system. More specifically, the investigated scenario considers the downlink of a communications system represented by a Base Station (BS) trying to reuse the spectrum allocated for a colocated MIMO radar in order to communicate with an assigned terminal, in the vicinity of the radar system. We present an accurate model for the operation of the wireless system in the downlink, while the MIMO radar tries to maintain an acceptable detectability level of a target in the far field. The target detection problem is reformulated using a sensing approach based on energy detection, while the BS applies beamforming to null the interference created at the radar receiver. Based on the theory of Hermitian quadratic forms and with the aid of the Linearly Constrained Minimum Variance (LCMV) beamforming solution, the performance of target detection, when the MIMO radar coexists with the data transmission is quantified and numerical results show that spectral coexistence is feasible.

In this paper, the operation of a licensed shared access system is investigated, considering downlink communication. The system comprises a multiple-input-single-output (MISO) incumbent transmitter (TX)-receiver (RX) pair, which offers a spectrum sharing opportunity to a MISO licensee TX-RX pair. Our main contribution is the design of a coordinated transmission scheme, inspired by the underlay cognitive radio (CR) approach, with the aim of maximizing the average rate of the licensee, subject to an average rate constraint for the incumbent. In contrast to most prior works on the underlay CR, the coordination of the two TXs takes place under a realistic channel state information (CSI) scenario, where each TX has solely access to the instantaneous direct channel of its served terminal. Such a CSI knowledge setting brings about a formulation based on the theory of Team Decisions, whereby the TXs aim at optimizing a common objective given the same constraint set, on the basis of individual channel information. Consequently, a novel set of applicable precoding schemes consisting in letting the two TXs cooperate on the basis of the statistical information is proposed. We verify by simulations that this novel, practically relevant, coordinated precoding scheme outperforms the standard underlay CR approach.

In this paper, the problem of joint design of spectrum sensing (SS) and receive beamforming, with reference to a cognitive radio (CR) system, is considered. The aim of the proposed design is the maximization of the achievable average uplink rate of a secondary user, subject to an outage-based quality-of-service constraint for primary communication. A hybrid CR system approach is studied, according to which the system either operates as an interweave (i.e., opportunistic) or as an underlay (i.e., spectrum sharing) CR system, based on SS results. A realistic channel state information framework is assumed, according to which the direct channel links are known by the multiple antenna receivers, while merely statistical (covariance) information is available for the interference links. A new closed-form approximation is derived for the outage probability of primary communication, and the problem of rate-optimal selection of SS parameters and receive beamformers is addressed for hybrid, interweave, and underlay CR systems. It is proved that our proposed system design outperforms both underlay and interweave CR systems for a range of system scenarios.

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.

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.

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.

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.

This paper addresses the problem of channel estimation in multi-cell interference-limited cellular networks. We consider systems employing multiple antennas and are interested in both the finite and large-scale antenna number regimes (so-called "massive MIMO"). Such systems deal with the multi-cell interference by way of per-cell beamforming applied at each base station. Channel estimation in such networks, which is known to be hampered by the pilot contamination effect, constitutes a major bottleneck for overall performance. We present a novel approach which tackles this problem by enabling a low-rate coordination between cells during the channel estimation phase itself. The coordination makes use of the additional second-order statistical information about the user channels, which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences. Importantly, we demonstrate analytically that in the large-number-of-antennas regime, the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance. Gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes.