Intelligent reflecting surfaces (IRSs) are being widely investigated as a potential low-cost and energy-efficient alternative to active relays for improving coverage in next-generation cellular networks. However, technical constraints in the configuration of IRSs should be taken into account in the design of scheduling solutions and the assessment of their performance. To this end, we examine an IRS-assisted time division multiple access (TDMA) cellular network where the reconfiguration of the IRS incurs a communication cost; thus, we aim at limiting the number of reconfigurations over time. Along these lines, we propose a clustering-based heuristic scheduling scheme that maximizes the cell sum capacity, subject to a fixed number of reconfigurations within a TDMA frame. First, the best configuration of each user equipment (UE), in terms of joint beamforming and optimal IRS configuration, is determined using an iterative algorithm. Then, we propose different clustering techniques to divide the UEs into subsets sharing the same suboptimal IRS configuration, derived through distance- and capacity-based algorithms. Finally, UEs within the same cluster are scheduled accordingly. We provide extensive numerical results for different propagation scenarios, IRS sizes, and phase shifters quantization constraints, showing the effectiveness of our approach in supporting multi-user IRS systems with practical constraints.

Intelligent reflecting surfaces (IRSs) are being studied as possible low-cost energy-efficient alternatives to active relays, with the goal of improving coverage in millimeter wave (mmWave) and terahertz (THz) network deployments. In the literature, these surfaces are often studied by idealizing their characteristics: notably, it is often assumed that IRSs can tune with arbitrary frequency the phase-shifts induced by their elements, thanks to a wire-like control channel to the next generation node base (gNB). Instead, in this work we investigate an IRS-aided time division multiple access (TDMA) cellular network, where the reconfiguration of the IRS entails an energy or communication cost, and we aim at limiting the number of reconfigurations over time. We propose a clustering-based heuristic scheduling, which optimizes the cell sum-rate subject to a given number of reconfigurations within the TDMA frame. To this end, we first cluster user equipments (UEs) with a similar optimal IRS configuration, determined through a novel beamforming and IRS iterative optimization algorithm. Then, we obtain a single IRS configuration for each cluster of UEs. Numerical results show that our approach is effective in supporting IRSs-aided systems with practical constraints, achieving up to 85% of the sum-rate obtained by an ideal deployment, while reducing by 50% the number of IRS reconfigurations.

Fixed wireless access has been spreading recently as a complement to wired or fiber solutions, and its implementation on millimeter waves has attracted attention for its potentially high data rates, achievable also in densely populated areas. New hardware and software solutions are needed to overcome various technical issues at those frequencies. We propose a novel modular hybrid beamforming (MHB) architecture, whereby, in receive mode, each module comprises a set of antennas connected to fixed analog beamformers, in turn connected by configurable switches to a smaller set of radio frequency chains. The outputs of all modules are then jointly processed by a digital beamformer. In particular, an MHB performing discrete Fourier transform beamforming for uniform linear arrays is studied. We address the problems of a) switch configuration, b) power allocation and digital beamformer design, and c) channel estimation. For both a) and b) we target the maximization of the total weighted spectral efficiency (WSE) under power and per-user average spectral efficiency (SE) constraints, and we propose greedy efficient solutions. For c) we propose a multistage scheme. We assess the MHB performance in terms of WSE, estimation accuracy, estimation overhead, and computational complexity, and compare them with existing solutions in literature.

Complex phenomena are generally modeled with sophisticated simulators that, depending on their accuracy, can be very demanding in terms of computational resources and simulation time. Their time-consuming nature, together with a typically vast parameter space to be explored, make simulation- based optimization often infeasible. In this work, we present a method that enables the optimization of complex systems through Machine Learning (ML) techniques. We show how well-known learning algorithms are able to reliably emulate a complex simulator with a modest dataset obtained from it. The trained emulator is then able to yield values close to the simulated ones in virtually no time. Therefore, it is possible to perform a global numerical optimization over the vast multi-dimensional parameter space, in a fraction of the time that would be required by a simple brute-force search. As a testbed for the proposed methodology, we used a network simulator for next-generation mmWave cellular systems. After simulating several antenna configurations and collecting the resulting network-level statistics, we feed it into our framework. Results show that, even with few data points, extrapolating a continuous model makes it possible to estimate the global optimum configuration almost instantaneously. The very same tool can then be used to achieve any further optimization goal on the same input parameters in negligible time.

In this paper, we investigate a wireless communication scenario, where polarization multiplexing is exploited to increase the spectral efficiency. Independent modems over each polarization are considered, with communication links affected by phase noise and cross-polarization interference (XPI). We devise a novel per-polarization soft decision-directed iterative receiver with separatea posterioriprobability-based synchronization and decoding. The synchronization algorithm relies on a minimum mean square error-based master–slave phase estimation followed by the cancellation of the XPI on the polarization of interest and requires no statistical knowledge of the phase noise process. The performance of the proposed iterative receiver is investigated for a pilot symbol-assisted low-density parity-check-coded quadrature amplitude modulation scheme.

This study discusses synchronisation in phase noise‐impaired spectrally efficient communication systems employing high‐order modulations. In particular, an iterative receiver, where demodulation and decoding are separate from maximum a posteriori probability (MAP) synchronisation, is presented. The authors’ separate approach is tailored to the design of pragmatic iterative receiver schemes employing ‘off‐the‐shelf’ demodulation and decoding blocks. This allows full compatibility with already existing systems, which is attractive from the implementation viewpoint. The proposed MAP synchronisation algorithm also requires very limited knowledge of the phase noise process and achieves near coherent performance with moderate computational complexity. Although the approach is very general, the authors discuss its performance for low‐density parity‐check‐coded pilot symbol‐aided quadrature amplitude modulation schemes, demonstrating that a significantly lower computational complexity can be achieved with respect to benchmark joint receivers.

This paper focuses on phase noise-impaired communications. An efficient Maximum A-posteriori Probability (MAP) iterative synchronization algorithm, where detection and decoding are performed separately from phase estimation, is proposed. This approach has the following key advantages: (i) its computational complexity is relatively low and its performance is near optimal; (ii) it requires very limited statistical knowledge of the phase noise process; and (iii) it enables the direct use of “off-the-shelf” demodulation and decoding blocks. These features are particularly attractive from the implementation viewpoint, as they lead to the design of effective pragmatic high-order coded modulated schemes. The proposed iterative synchronization and decoding algorithm, evaluated for Low-Density Parity-Check (LDPC)-coded pilot symbol-assisted Quadrature Amplitude Modulation (QAM) schemes, entails a negligible energy efficiency loss with respect to optimized joint decoding and phase estimation approaches, with significantly lower computational complexity.

The Wireless over Cable (WoC) paradigm is a new technique to enable distributed antennas systems. WoC is based on the relay of radio frequency wireless signals over a cable infrastructure through a bidirectional (analog) amplify and forward device that translates the bandwidth of wireless signals to comply with the specifications of the wired links. Following the WoC concept, here the xDSL connection in femtocell architecture is replaced with a plain connection of the antenna with telephone lines after up/down-frequency conversion to enable Radio over Telephone Lines. Baseband processing and radio resource management is carried out simultaneously for multiple femtocells thus centralizing scheduling and mitigation of the radio frequency interference among neighboring femtocells. We propose the envisioned Radio over Telephone Lines architecture for femtocells and get insights into the hindrances of propagation over twisted pair copper lines via numerical analysis by employing OFDMA signals over the air interface (WiMAX/LTE) that coexist (and interfere) with xDSL services.

The wireless network scenario here is based on N users that communicate one with the others through a central relay node by adopting the Analog Network Coding (ANC) paradigm. In this paper, we propose a communication scheme for ANC able to guaratee the simultaneous transmissions of N ¿ 2 interfering terminals using the concept of multiuser processing (i.e., channel estimation and detection). Under the assumption of slow-varying channels, multi-link channel estimation is achieved through the coherent summation at the physical layer of the invariant training sequences over the different frames, thus enabling multiuser detection. Benefits of the proposed ANC protocol are stressed through comparisons with the traditional TDMA-based routing scheme where the relay is time-shared among the different users. Impacts of the hindrances to our scheme (namely, hardware impairments at the nodes and finite coherence-time of the channels) are also investigated by numerical analysis. The proposed algorithm enables the employment of simple sense-before-talk policies at each user interested in accessing the network, thus avoiding complex forms of centralized coordination by the relay.

According to the commons model of cognitive radio, the activity of secondary (unlicensed) nodes is required to guarantee quality-of-service (QoS) constraints on the transmission of primary (licensed) terminals. Towards this goal, vertical handover between different radio interfaces is currently being investigated as a promising solution to enhance flexibility in unlicensed channel access. In this paper, we propose an analysis of cognitive radio with vertical handover capability in a simple scenario with one secondary node and two primary nodes that employ different radio interfaces with packet-based transmission. The maximum stable throughput of the secondary node is evaluated under maximum-delay QoS constraints on the primary activity as a function of system geometry, QoS constraints and sensing errors. Numerical results show the relevant advantages of optimal vertical handover in terms of the throughput of secondary nodes.

In this letter, packet-by-packet vertical handover is investigated as a means to improve the performance of unlicensed spectrum access. The analysis focuses on the optimization of a channel access strategy for a cognitive multi-standard radio node that has the capability to switch between two orthogonal uplink radio interfaces, based on long-term channel state information. The radio interfaces differ for coverage and quality of service (QoS) requirements of primary users. The proposed strategy prescribes a cross-layer selection of physical-layer (transmitting powers) and medium access control layer (handover probability) parameters and is designed to maximize secondary throughput while guaranteeing the primary QoS constraints. Analysis and numerical results bring insight into the impact of network topology, measurement errors and primary QoS constraints on the optimal system design and corresponding performance.

Cooperation is increasingly regarded as a key technology for tackling the challenges of a practical implementation of cognitive radio. In this paper, we first give a brief overview of the envisioned applications of cooperative technology to cognitive radio, distinguishing among cooperative sensing for detection of the primary activity, cooperative transmission between secondary nodes and cooperative transmission of primary traffic by secondary users (cognitive relaying). Then, we focus on the latter scenario and investigate a simple wireless network, where one secondary transmitter has the option to relay traffic of the primary. Assuming that the primary is oblivious to the presence of the secondary (thus excluding the possibility of spectrum leasing), the secondary transmitter optimizes transmission/ relaying parameters towards the goal of maximizing the rate towards the secondary receiver. Numerical results are provided in order to discuss the advantage and limits of cognitive relaying.