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This paper introduces Waste Factor (W), also denoted as Waste Figure (WF) in dB, a promising new metric for quantifying energy efficiency in a wide range of circuits and systems applications, including data centers and Radio Access Networks (RANs). Creating and managing 5G and future 6G networks that are energy-efficient is of paramount importance as the wireless industry evolves to become a major consumer of energy. Also, the networks used to connect data centers and artificial intelligence (AI) computing engines with users for machine learning (ML) applications must become more power efficient. This paper illustrates the limitations of existing energy efficiency metrics that inadequately capture the intricate energy dynamics of RAN components. We show here that W provides a generalized analysis of power utilization and energy waste at both the component and system levels for any source-to-sink communication system. We delineate the methodology for applying W across various network configurations, including multiple-input single-output (MISO), single-input and multiple-output (SIMO), and multiple-input multiple-output (MIMO) systems, and demonstrate the effectiveness of W in identifying energy optimization opportunities. Our findings reveal that W not only offers nuanced insights into the energy consumption of RANs but also facilitates informed decision-making for network design and operational efficiency. Furthermore, we show how W can be integrated with other key performance indicators (KPIs) and key value indicators (KVIs) to guide the development of optimal strategies for enhancing network energy efficiency under different operational conditions. Additionally, we present simulation results for a distributed multi-user MIMO (MU-MIMO) system at 3.5, 17, and 28 GHz, demonstrating overall network power efficiency on a per square kilometer basis, and show how overall W decreases (e.g., energy efficiency increases over the entire network) with an increasing number of base stations and increasing carrier frequency. This paper shows that adopting W as a figure of merit (FoM) can enable the design of more sustainable next-generation wireless communication networks, paving the way for greener and more sustainable, energy-efficient 5G and 6G technologies.

The integration of intelligent reflecting surfaces (IRS) into cooperative non-orthogonal multiple access (NOMA) systems revolutionizes wireless networks by enhancing signal strength, mitigating interference, and optimizing spectral efficiency. The cooperative NOMA (CNOMA) framework, empowered by IRS technology, further promises enhanced performance, robustness, and scalability for next-generation wireless networks as compared to NOMA only systems. This paper explores the synergy between IRS and NOMA to leverage cooperative techniques for superior wireless system design. Fundamental principles, technological advancements, and potential applications of IRS-assisted CNOMA systems are discussed, highlighting existing works. Both underlay and overlay NOMA principles are examined in conjunction with IRS in the paper. Spatial modulation-aided CNOMA is explored for multiple-input multiple-output (MIMO) systems, along with its advantages and practical challenges. Additionally, the paper discusses fundamental principles and technological advancements of IRS-assisted CNOMA systems, emphasizing solutions to potential challenges and the role of machine learning (ML)/deep learning (DL) in resource optimization like transmit power and IRS phase settings. Simulation results are presented to highlight the benefits of IRS-aided CNOMA system design. Finally, the paper outlines future directions and potential research topics in IRS-aided CNOMA.

We investigated low Earth orbit satellite (LEO) communications applying Multiple-Input/Multiple-Output (MIMO) technology to the feeder link transmission between a satellite and gateway antennas to improve the channel capacity. This paper calls LEO satellites using MIMO technology “LEO-MIMO” and considers satellite and gateway antenna configurations to improve the feeder link capacity. LEO-MIMO operates with a single LEO satellite equipped with multiple antenna elements and a gateway with multiple remotely located antennas. The conditions of the antenna configuration, such as the distances of satellite/gateway antenna elements and the number of antenna elements in the satellite and gateway, affect the channel capacity of LEO-MIMO. This paper reveals antenna configurations that improve the channel capacity for LEO-MIMO systems. Computer simulations show that the channel capacity of LEO-MIMO can achieve 4 bps/Hz at maximum higher than that of the conventional multi-beam system.

This paper proposes a design principle for reactance compensation circuit for multiple-input multiple-output (MIMO) inductive power transfer (IPT) where the primary side employs a coil array to send power to a receiving coil array located at the secondary side. Delivering wireless power over multiple streams, MIMO topology is an effective approach to relax strict burdens on system components in high-power IPT applications. An important requirement in MIMO topology is a method to mitigate cross-coupling inside the coil arrays so that load-independent constant-current and constant-voltage can be realized in the same fashion as conventional single-input single-output IPT. Based on transfer matrix analysis, this paper proposes a design principle indicating that passive compensation circuit should implement the negative of the cross-coupling’s reactance matrix to cancel it in the reactance domain. The proposed design is independent of the transmission distance, applicable to MIMO IPT systems at arbitrary scale and does not require the coil arrays to employ any decoupling structure. Full-wave electromagnetic simulations with $3\times 2$ MIMO and experiments with $2\times 2$ MIMO are provided to verify effectiveness and feasibility of the proposed scheme.

In-band full-duplex (IBFD) is an attractive technology in broadband power line communication (BB-PLC) because it helps to improve spectral efficiency. However, IBFD is challenging since it requires additional hardware and advanced signal processing to mitigate self-interference (SI) signals. SI cancelation architectures and channel estimation techniques determine the overall IBFD performance. Accurate SI channel estimation is required since imperfect SI cancelation reduces signal-to-interference-plus-noise ratio (SINR), causing an increase in data errors and a decrease in data rates. Although channel estimation can be improved by sending additional training symbols, increasing the training duration will lower data throughput. Thus, the training symbol number is an essential trade-off for IBFD performance in BB-PLC. In this paper, we investigate IBFD performance in single-input single-output (SISO), single-input multiple-output (SIMO), and multiple-input multiple-output (MIMO) communication scenarios, including the influence of the training period. By analyzing error vectors on a constellation diagram, we obtain the closed-form expressions for the symbol error probability (SEP) affected by IBFD and the training duration. Based on the obtained expressions, we propose a bit allocation algorithm to determine bit loading to ensure reliable IBFD communication. Furthermore, we suggest a procedure to compute the optimal training symbol number that maximizes throughput in IBFD. Using the proposed bit allocation strategy and a database of measured channels, we estimated the achievable bidirectional throughput and the throughput gain in IBFD compared to time division duplexing (TDD).

The research community has been more interested in nature-inspired antenna solutions in recent years. Well-known organizations all around the world have set up labs to investigate the structures, operations, and traits of creatures that exist. One valuable method is investigating geometrical depictions of biological shapes, including fractal shapes and polarized geometry. This paper focuses on the design of a leaf-shaped antenna, its $4\times 1$ corporate-fed array, spatial diversity Multiple Input Multiple Output (MIMO) antenna, and pattern diversity MIMO antenna, which operate in the sub-6 GHz range and specifically cover the 5G bands: N77 (3.3–4.2 GHz), N78 (3.3–3.8 GHz), and N79 (4.7 GHz) (4.4–5 GHz). The radiating element of the array is modeled like a natural plant leaf, and a defective ground plane is used to reduce size and increase bandwidth. To obtain dual band characteristics and circular polarization, a diagonal slot in the parasitic metallic elliptical disc in the ground plane is used. A corporate feed network is used in a $4\times 1$ array for excitation to radiating elements and to generate high gain performance by ensuring little reflection loss and good impedance matching. An electromagnetically coupled hexagonal array is used in a T shape between the inter-elements to reduce mutual coupling and increase the gain of the $4\times 1$ array. The proposed multiband antenna array reflects less than 10% of the power between 3.4–4 GHz and 4.51–5.2 GHz and achieves a maximum gain of 6.55 dB with variations of less than 3 dB across the full operating band. MIMO configurations are required to increase the network capacity. Spatial diversity and pattern diversity MIMO antennas are designed to provide good diversity performance and sufficient isolation bandwidth. All the proposed antennas are fabricated on a commercially available FR4 substrate with a dielectric constant of 4.3 and a height of 1.6 mm, making them inexpensive for mass production. The performance of fabricated antennas is compared with that of simulated antennas. Compact size, planar design, broad bandwidth, and stable gain make the proposed antennas suitable for sub-6 GHz 5G applications.

This paper presents a novel MIMO antenna array configuration that incorporates metamaterial isolation surfaces to enhance overall performance. It was demonstrated that the directivity of this antenna array can be precisely electronically reconfigured using PIN diode switches. Additionally, the feasibility of integrating solar panels with the proposed MIMO antenna array is shown. Adopting solar panels in 5G base stations is expected to reduce dependency on traditional grid power sources, thereby decreasing energy usage and operational expenses, and supporting the goal of achieving netzero emissions in communication systems. Furthermore, the effectiveness of an artificial intelligence (AI)-enabled equalizer in mitigating degradation in channel capacity caused by signal power fluctuations was demonstrated, thereby enhancing the reliability and efficiency of wireless communication systems. The multifaceted benefits of combining these technologies were demonstrated.

Pattern synthesis is widely used in many radar and communication systems and received great interest. So, this paper proposes a new beamforming strategy based on a hybrid combination between grey wolf optimizer (GWO) with ${\mathrm { L}}_{2}$ -norm called proposed GWO. This approach is applied to synthesized uniform linear arrays (ULA), Chebyshav arrays, and shaped pattern arrays. Moreover, it is utilized for side lobe level (SLL) and size reduction of antenna elements. In this strategy, the GWO is utilized to optimize the element spacing to adjust the half-power beamwidth (HPBW) to save it the same as desired pattern. Furthermore, the excitations of the antenna elements are optimized via the ${\mathrm { L}}_{2}$ -norm minimization problem. The proposed GWO has low complexity (fewer iterations and computing time) compared to other algorithms. In addition, it has a very accurate approximation of the original radiation pattern. As well, the computer simulation technology (CST) microwave package is utilized to achieve the practical validation of the proposed methodologies. As an application of the proposed GWO, it is employed to create a proposed hybrid beamforming (PHB) structure for Multi-input Multi-output (MIMO) systems. Consequently, the BS transmitting antennas are synthesized for gain maximization while utilizing the current amount of antenna elements. This results in considerable savings in antenna components and associated radio frequency (RF) chains which reduces system complexity. Furthermore, array gain maximization will increase the received signal-to-noise ratio (SNR). In addition, the SLL reduction scenario will decrease the interference from undesired users which in turn will also increase SNR. Hence, the performance of the system in terms of spectral efficiency (SE) and power utilization will be improved.

There is an increasing demand for data with the development of the world, and various fiber optic multiplexing techniques have become an important research direction to improve transmission capacity. However, the transmitted signals are subject to great interference due to mode coupling and mode dispersion, which require multiple-input multiple-output (MIMO) digital signal processing techniques to restore the quality of the transmitted signals. In this paper, a novel MIMO detector is designed using an adaptive learning recurrent neural network and successfully implemented in a mixed wavelength-division-mode-division-multiplexing (WDM-MDM) optical transmission system, and its performance is compared with that of the forced-zero detector and the minimum-mean-square-error detector. The results show that the introduction of an adaptive machine learning model in MIMO detection for WDM-MDM optical transmission systems can significantly improve the quality of the transmitted signals and achieve better performance than other MIMO detection algorithms while maintaining a faster computational speed and a lower number of parameters.

This paper proposes a multi-band coupled-fed antenna (MBCFA) design for 4G LTE, Sub-6G, and WLAN frequencies, suitable for electronic devices with different sizes and fully metallic backplates. The MBCFA structure can be replicated on the fully metallic backplates of laptops, tablets, or smartphones to form a multi-input multi-output (MIMO) antenna system. Each MBCFA at different positions on laptops and tablets exhibits similar resonance characteristics, highlighting its high robustness and stability. The proposed MBCFA is manufactured using low-cost FR4 material. The overall size of MBCFA is 70 mm $\times12.5$ mm and an FR4 thickness of 0.8 mm, the measured reflection losses within the -6dB range are 740–990 MHz (250 MHz bandwidth), 1380–2640 MHz (1260 MHz bandwidth), and 4250–7330 MHz (3080 MHz bandwidth). The MBCFA’s isolation levels in the 4G LTE frequency band are below -16 dB, while the remaining frequency bands have isolation levels below -20 dB. The maximum antenna gain is approximately 5 dBi, and the optimal antenna efficiency is 60%. The Equivalent Coupling Coefficient (ECC) for all frequency bands is below 0.5. Given the excellent antenna characteristics described above, the proposed multi-band coupled-fed antenna is an outstanding candidate for laptop, tablet, and smartphone antennas.

Unprecedented growth in wireless data traffic, and ever-increasing demand for highly secured, and low-latency wireless communication has motivated the research community to move towards sixth-generation (6G) technology, where networks can cater to the rising need for ubiquitous secure wireless connectivity. One of the promising technologies for 6G wireless communication is the reconfigurable intelligent surface (RIS) concept that is proposed to successfully deal with increasing security threats by smartly controlling the wireless channel conditions. This survey paper presents a detailed literature review on RIS-assisted physical layer security (PLS) for next-generation wireless communications. Firstly, we briefly discuss the PLS concept, its importance, the PLS performance metrics, and its applicability in different wireless networks. Next, we discuss the applications of RIS in the 6G scenario. Then, a detailed and systematic classification of the various RIS-assisted wireless system topologies exhibiting multiple scenarios, system models, channel fading models, performance metrics and objectives is done. The existing state-of-art approaches for PLS such as secret key generation (SKG), optimization algorithms, namely semidefinite relaxation-successive convex approximation (SDR-SCA) to optimize RIS coefficients, and optimal placement of RIS units are discussed for single-input single-output (SISO) case. For multiple-input single-output (MISO) case, PLS strategies such as inducing artificial noise (AN), optimization algorithms, alternating optimization (AO), machine learning (ML) and deep learning (DL), and reflect matrices are discussed. Similarly, for multiple-input multiple-output (MIMO) setup, block coordinate descent (BCD) and AN induction are some of the PLS methods used to analyze the secrecy. Lastly, we present some of the technical challenges and future directions based on the survey.

This paper introduces a novel spatial domain-based self-interference cancellation (SIC) method named constrained minimum mean square error (C-MMSE) for massive multiple-input multiple-output (mMIMO) full-duplex (FD) communication systems. The main idea involves treating the self-interference (SI) signal emitted from an FD node as a distinct spatial stream arriving at the receiver part of that same FD node. This SI signal needs to be spatially postcoded alongside other relevant signals coming from different transmitters, ensuring it falls into the null space of the MIMO channel, which includes the FD node transmitter part as an input. To establish this approach, we first adapt the expressions of spatial combiners for conventional zero forcing (ZF) and minimum mean square error combining (MMSE) criteria. We demonstrate that MMSE alone is insufficient for efficient SI signal cancellation unless an additional constraint is added to enable proper SIC. Consequently, we design the innovative C-MMSE combiner and derive its expression. In addition to our proposal, the originality of our work stems from employing a spherical wave model (SWM) to model the SI channel. The choice is justified by the proximity of the transmit and receive antenna panels in the FD node. We examine and compare the SIC performance of the modified ZF combiner, the modified MMSE combiner, and the newly introduced C-MMSE combiner by evaluating the spectral efficiency (SE). Additionally, we highlight the robustness of the SWM-based SI channel modeling in comparison to conventional planar wave modeling (PWM), emphasizing the relevance of its usage.

Integrated access and backhaul (IAB) networks operating in full-duplex (FD) mode at millimeter wave frequencies have been actively investigated in the context of future-generation communications networks. However, conventional analog cancellation techniques cannot adequately mitigate the self-interference resulting from the FD operation and the multi-user interference. Hence, in this paper, we consider a multi-cell, multi-user IAB network and jointly design the beamforming and combining matrices to maximize the network’s weighted sum rate. Given the non-convex nature of the problem, we reformulate it using weighted minimum-mean-square-error (WMMSE) and extended fractional programming (FP) techniques followed by a block coordinate descent (BCD) approach. Extensive simulation results validate the superior performance of our proposed algorithms. Specifically, the WMMSE and FP methods can achieve 50 bits/sec/Hz higher than the benchmark scheme for a network employing three cells with two uplink and two downlink users per cell.

In this paper, we investigate the performance of multiple-input and multiple-output (MIMO) unmanned air vehicle (UAV) based multi-user communication systems over generalized Nakagami- $m$ fading channels subject to channel feedback delays. The impact of decode-and-forward (DF) based relaying and outdated channel state information is assessed at the receiver nodes. To reduce the complexity and retain the MIMO gains, the transmit antenna selection (TAS) strategy is used to select the best antenna at the source and UAV nodes. A generic framework in the form of closed-form analytical expressions for outage probability (OP), asymptotic OP, and the average symbol error rate of higher-order quadrature amplitude modulation (QAM) schemes such as hexagonal QAM, rectangular QAM, and cross QAM are derived. In addition, energy efficiency analysis is also performed for the considered system model. In this framework, altitude and location-dependent path loss modeling are considered for the air-to-ground links. Optimization of UAV location and altitude is performed through a limited-memory Broyden-Fletcher-Goldfarb-Shanno (LBFGS) algorithm to attain minimum OP (MOP). Results illustrate optimal performance dependent on the channel correlation parameters, antenna elements, and fading conditions. Monte-Carlo simulations are performed to validate the derived analytical results and compared with the existing works.