In this article, we review the key concepts and the progress in the design of physical-layer aspects of in-band full-duplex (IBFD) communications. One of the fundamental challenges in realizing IBFD is self-interference that can be up to 100 dB stronger than signals of interest. Thus, we start by reviewing state-of-the-art research in self-interference cancellation, addressing both model-based and emerging machine learning-based methods. Then, we turn our attention to new wireless systems with many degrees of freedom for which the traditional IBFD designs do not gracefully scale and, hence, require many innovations to enable IBFD. We provide an extensive review of basic concepts and state of the art in massive multiple-input–multiple-output IBFD. Then, we consider the mmWave band IBFD and review advanced physical-layer architectures. The above review provides the proper context to discuss IBFD innovations and new challenges for sixth-generation networks and beyond, where wireless networks are envisioned to be multifunctional, combining communications with functions such as sensing, cognitive radios, physical-layer security, and wireless power transfer. We conclude this article with a status update on the adoption of IBFD in communication standards.

Non-orthogonal multiple access (NOMA) aims to increase the spectral efficiency of fifth generation (5G) networks by relaxing the orthogonal use of radio-resources. In this work, a network with multiple half-duplex (HD) buffer-aided (BA) relays is considered, where the source transmits with fixed rate towards two users. The users might demand the same rate by the source (e.g., two cellular users requiring the same service), or they could have different rate requirements (e.g., a cellular user coexisting with an Internet of Things (IoT) device). By deploying multiple BA relays, increased reliability and additional degrees of freedom are provided. Leveraging the spectral efficiency of NOMA and the increased diversity gain of BA relaying, two relay selection algorithms with broadcasting are proposed for power-domain (PD) NOMA and hybrid NOMA/OMA, namely BA–NOMA and BA–NOMA/OMA, respectively. BA–NOMA can improve the performance in terms of outage probability when the power allocation factor $\alpha$ is selected such that robustness against channel uncertainties due to, e.g., outdated channel state information (CSI), is provided. Moreover, BA–NOMA/OMA further improves the sum-rate by switching to OMA when the relays cannot serve the users through NOMA. Moreover, both algorithms reduce the average delay by avoiding the delay of orthogonal scheduling and prioritizing the relay-destination transmission. For both cases, a theoretical analysis for the outage probability is conducted and the asymptotic performance is studied. Finally, numerical results and comparisons with other stateof-the-art algorithms are provided for the outage probability, average throughput and average delay.

Machine-to-machine (M2M) communication represents a new paradigm for mobile cellular networks, where a massive number of low-cost devices request the transfer of small amounts of data without human intervention. One option to tackle this problem is obtained by combining random beamforming (RBF) with opportunistic scheduling. RBF can be used to induce larger channel fluctuations, and opportunistic scheduling can be used to select M2M devices when their overall channel quality is good. Traditional RBF does not fulfill M2M requirements, because overall channel quality needs to be tracked continuously. In order to tackle this limitation, a novel codebook-based RBF architecture that identifies in advance the time instants in which overall channel quality should be reported, within a coherence time window, is proposed. This opportunistic feedback mechanism reduces signaling overhead and enables energy saving at M2M devices. A simplified methodology is presented to evaluate the system mean data rate, using for this purpose closed form formulas derived from SNR distribution approximations. Results reveal that the performance loss that is experienced for introducing the proposed modifications to traditional RBF scheme is negligible. The concepts analyzed in this paper provide useful insights, and show that codebook-based RBF with simplified opportunistic scheduling algorithms is an excellent combination to provide wide-area M2M services with low-cost devices and limited signaling overhead.

This paper proposes the integration of non-orthogonal multiple access (NOMA) and broadcasting with buffer-aided (BA) full-duplex (FD) relaying to improve the spectral efficiency of a single-source two-user network. Furthermore, opportunistic relay selection is employed, activating one or two FD relays to support the end-to-end communication. The network performance is improved through buffers, allowing to switch to half-duplex (HD) transmission/reception when FD operation is not feasible. A hybrid BA FD/HD NOMA relay selection algorithm is proposed, herein called BAHyNOMA, orchestrating the three aforementioned techniques, towards a simple yet efficient and robust relay selection. BAHyNOMA is evaluated in terms of outage probability, average sum-rate and average delay, and it is observed that the average sum-rate can be doubled, while maintaining low latency.

We propose novel direct and indirect learning predistorters (PDs) that employ a new baseband simplicial canonical piecewise linear (SCPWL) function. The performance of the proposed PDs is easily controlled by varying the number of segments of the SCPWL function. When comparing to polynomial-based PDs, our SCPWL-based PDs are more robust for modeling strong nonlinearities and are less sensitive to input noise. In particular, we show that noise appearing in the feedback path of an indirect learning SCPWL-PD has negligible effect on the performance while the polynomial counterpart suffers from a noise-induced coefficient bias. We consider adaptive implementations of both Hammerstein-based and memory-based SCPWL PDs; the former featuring less parameters to be identified while the latter renders more straightforward parameter identification. When deriving the PD algorithms, we avoid a separate PA identification step which allows for a true real-time, or sample-by-sample, implementation without an alternating PA and PD identification procedure. However, to arrive at efficient sample-by-sample algorithms for Hammerstein PDs we need to bypass the problem of the associated nonconvex cost function. This is done by employing a modified, linear-in-the-parameter, Wiener model whose parameters can be explicitly or implicitly used for both indirect and direct learning. Extensive simulations confirm that the proposed SCPWL PDs outperform their polynomial counterparts, especially when noise is present in the feedback path of the indirect learning structure. The same is also verified by circuit level simulations on the Freescale MRF6S23100H class-AB PA in an 802.16d WiMAX system.

In networks with multiple wireless nodes and especially in fast changing environments, the continuous acquisition of Channel State Information (CSI) may become infeasible. In this paper, we study the relay selection problem for a cooperative network, where Full-Duplex (FD) relays are equipped with buffers and only statistical CSI is available at the transmitters (CSIT). For this setting, a novel relay selection algorithm is proposed, named HyQoS, relying only on statistical CSIT and performing power adaptation. HyQoS solves an optimization problem for selecting the optimal set of relays. We consider two optimization approaches: 1) minimization of the total power consumed in the network, and 2) maximization of the throughput per energy unit, while guaranteeing a specific Quality of Service (QoS), in terms of Successful Transmission Probability (STP). The power allocation problem is formulated as a nonlinear optimization one, taking into account inter-relay interference and residual loop interference. Numerical results show that HyQoS can reduce the power expenditure and increase the throughput, by using only statistical CSIT.