We introduce a new robust, outage minimum, millimeter wave (mmWave) coordinated multipoint (CoMP) beamforming scheme to combat the random path blockages typical of mmWave systems. Unlike state-of-the-art methods, which are of limited applicability in practice due to their combinatorial nature which leads to prohibitive complexity, the proposed method is based on a stochastic-learning-approach, which learns crucial blockage patterns without resorting to the well-known worstcase optimization framework. Simulation results demonstrate the superior performance of the proposed method both in terms of outage probability and effective achievable rate.

We propose a new signal spreading-based non-orthogonal multiple access (NOMA) schemes that exploits the frame-theoretic design principles to enable the efficient concurrence of multiple users in both downlink (DL) and uplink (UL). In contrast to many other NOMA schemes, the proposed method does not require built-in sparsity on the usage of resources by any individual users, hence referred to as massively concurrent non-orthogonal multiple access (MC-NOMA). Instead, MC-NOMA leverages frames as vectorized ensembles of the individual complex-valued waveform signatures, optimized for low mutual coherence so as to minimize the multi-user interference. A theoretical analysis of the MC-NOMA is offered, which reveals that it can theoretically reach the capacity of the multi-user MIMO channel and outmatches the state of the art in terms of maximum achievable rate over discrete constellations. A detailed description of both the frame-based transmitter design and a newly developed low-complexity efficient decoder is given, which, via simulations, are used to demonstrate that MC-NOMA outperforms the well-established NOMA schemes, such as sparse-coded multiple access (SCMA) and pattern division multiple access (PDMA).INDEX TERMS Frame theory, massively-concurrent non-orthogonal multiple access, multiuser detection, non-orthogonal communications, tree search decoding.

The sixth generation (6G) of mobile network will be composed by different nodes, from macro-devices (satellite) to nano-devices (sensors inside the human body), providing a full connectivity fabric all around us. These heterogeneous nodes constitute an ultra dense network managing tons of information, often very sensitive. To trust the services provided by such network, security is a mandatory feature by design. In this scenario, physical-layer security (PLS) can act as a first line of defense, providing security even to low-resourced nodes in different environments. This paper discusses challenges, solutions and visions of PLS in beyond-5G networks.

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In this study, we consider the downlink beamforming problem in millimeter wave (mmWave) systems subjected to both path blockages and imperfect channel state information (CSI), and propose a new robust hybrid sum-outage minimizing design as a solution. We first formulate the problem as an empirical risk minimization (ERM) stochastic learning problem, whose solution can be obtained by the alternate iteration of a baseband digital and a radio frequency (RF) analog Riemann manifold-constrained beamforming updates through a mini-batch stochastic gradient descent (MSGD) approach, with gradient minimizing update rules given in closed-form, and learning rates optimized based on the lower-bounds of the corresponding Lipschitz constants. Unlike existing solutions to the path blockage-robust mmWave beamforming problem, wherein out-of-band side information is required or perfect CSI is assumed, our method relies only on the estimates and statistical knowledge of the channel's angles of departure (AoD) and complex gains, which are simultaneously captured in a Bernoulli-Gaussian model and used to generate the training data for the MSGD-based optimizer. Further, unlike preceding fully-digital or fullyconnected hybrid contributions, the proposed scheme assumes a virtually-configured partially-connected setup; therefore, it is compatible with coordinated multipoint (CoMP) architectures, which are known to be crucial in terms of exploiting the full potential of mmWave systems. Simulation results confirm the effectiveness of our MSGD-based robust hybrid CoMP mmWave beamformer in mitigating the effects of path blockage and CSI error, demonstrating its superiority to state-of-the-art (SotA) alternatives.INDEX TERMS Millimeter wave systems, distributed hybrid beamforming, stochastic gradient descent, cooperative multi-point downlink beamforming.

We consider the estimation of millimeter wave (mmWave) channel gains following the now well-established approach of sparse signal processing. Within that context, we offer two major contributions. The first is a complete frame-theoretical treatment of the sensing matrix design (beam management) problem, compactly described by a pair of Lemmas that together with efficient low-coherence frame construction algorithms offer a general solution for the optimal transmitter (Tx) and receiver (Rx) beamforming components of the sparse mmWave channel estimation problem. The second contribution is a pair of novel sparse recovery algorithms, which unlike the majority of sparse solvers found in the literature, is not based on the relaxation of 0-norm into an 1-norm, but rather on a smooth approximation of the 0-norm handled further via fractional programming (FPG). The two algorithms differ from each other in that in the first (slightly more accurate), the resulting convex problem is solved via interior point methods, while the second (stand-alone) makes use of the alternating direction method of multipliers (ADMM). As a bonus, an original ADMM variation of the well-known basis pursuit denoising (BPDN)-1-reweighted sparse solver is also given. Simulation results confirm the channel estimation accuracy improvements obtained by both contributions.

We propose a new scheme for the robust estimation of the millimeter wave (mmWave) channel. Our approach is based on a sparse formulation of the channel estimation problem coupled with a frame theoretic representation of the sensing dictionary. To clarify, under this approach, the combined effect of transmit precoders and receive beamformers is modeled by a single frame, whose design is optimized to improve the accuracy of the sparse reconstruction problem to which the channel estimation problem is ultimately reduced. The optimized sensing dictionary frame is then decomposed via a Kronecker decomposition back into the precoding and beamforming vectors used by the transmitter and receiver. Simulation results illustrate the significant gain in estimation accuracy obtained over state of the art alternatives. As a bonus, the work offers new insights onto the sparse mmWave-multiple-input multiple-output (MIMO) channel estimation problem by casting the trade-off between correlation and variation range in terms of frame coherence and tightness.Index Terms-mmWave channel estimation, Compressed Sensing, complex incoherent tight frames, basis pursuit denoising.

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