In this paper, we propose an integrated sensing and backscatter communication system utilizing high scanning-rate slot array antennas (SAAs) in a frequency-diverse configuration, leveraging an inverse scattering approach. This system employs a frequency diversity scheme to facilitate rapid spatial data acquisition, significantly outperforming traditional mechanical and electronic scanning methods in terms of speed and cost-efficiency. The proposed integrated system achieves simultaneous localization, identification, and backscatter communication of tags within cluttered environments. Our approach analyzes backscatter process based on the structural and antenna modes of tags, as well as the presence of clutter scatterers. By modulating the tags between open-circuit and short-circuit states, we effectively extract the structural and antenna mode components. The structural mode component allows us to sense both the tags and the surrounding clutter scatterers, while the antenna mode component is used for precise tag identification by incorporating both inverse scattering and compressive sensing (CS) algorithms. During these processes, channel state information (CSI) is gathered through the antenna mode, enhancing the backscatter communication capability of the system. Additionally, the efficacy of backscatter communication is assessed using the bit error rate (BER) with the maximum ratio combining (MRC) technique. Simulations and experimental results demonstrate that our proposed system can accurately sense and identify tags amidst clutter scatterers while maintaining robust backscatter communication over a 40 MHz bandwidth within the 3.98-4.02 GHz range. These results highlight the significant advantages of employing the proposed frequency-diverse antennas and the inverse scattering approach in integrated backscatter communication and sensing applications.

Pixel antennas, based on discretizing a continuous radiation surface into small elements called pixels, are a flexible reconfigurable antenna technology. By controlling the connections between pixels via switches, the characteristics of pixel antennas can be adjusted to enhance the wireless channel. Inspired by this, we propose a novel technique denoted antenna coding empowered by pixel antennas. We first derive a physical and electromagnetic based communication model for pixel antennas using microwave multiport network theory and beamspace channel representation. With the model, we optimize the antenna coding to maximize the channel gain in a single-input single-output (SISO) pixel antenna system and develop a codebook design for antenna coding to reduce the computational complexity. We analyze the average channel gain of SISO pixel antenna system and derive the corresponding upper bound. In addition, we jointly optimize the antenna coding and transmit signal covariance matrix to maximize the channel capacity in a multiple-input multiple-output (MIMO) pixel antenna system. Simulation results show that using pixel antennas can enhance the average channel gain by up to 5.4 times and channel capacity by up to 3.1 times, demonstrating the significant potential of pixel antennas as a new dimension to design and optimize wireless communication systems.

This paper proposes a cooperative cell-free integrated sensing and communication network (CoCF-ISACNet) adopting hybrid beamforming (HBF) architecture, which improves both radar sensing and communication performance. The main contributions of this work are three-fold. First, we introduce a CoCF-ISACNet with energy-efficient HBF architecture. To show the benefits of proposed CoCF-ISACNet, we propose to jointly design the HBF to maximize the network communication capacity while satisfying the constraint of beampattern similarity for radar sensing, which results in a highly dimensional and non-convex problem. Second, to facilitate the joint design, we propose a novel distributed optimization framework called Proximal grAdieNt Decentralized Alternating direction method of multipliers (PANDA). Third, we further adopt the proposed PANDA framework to solve the joint HBF design problem for the CoCF-ISACNet. By using the proposed PANDA framework, all access points (APs) optimize the HBF in parallel, where each AP only requires local channel state information and limited message exchange among the APs. Such framework reduces significantly the computational complexity and thus has pronounced benefits in practical scenarios. Simulation results verify the effectiveness of the proposed algorithm compared with the conventional centralized algorithm and show the remarkable performance improvement of radar sensing and communication by deploying CoCF-ISACNet.

We investigate antenna coding utilizing pixel antennas as a new degree of freedom for enhancing multiple-input multiple-output (MIMO) wireless power transfer (WPT) systems. The objective is to enhance the output direct current (DC) power under RF combining and DC combining schemes by jointly exploiting gains from antenna coding, beamforming, and rectenna nonlinearity. We first propose the MIMO WPT system model with binary and continuous antenna coding using the beamspace channel model and formulate the joint antenna coding and beamforming optimization using a nonlinear rectenna model. We propose two efficient closed-form successive convex approximation algorithms to efficiently optimize the beamforming. To further reduce the computational complexity, we propose codebook-based antenna coding designs for output DC power maximization based on K-means clustering. Results show that the proposed pixel antenna empowered MIMO WPT system with binary antenna coding increases output DC power by more than 15 dB compared with conventional systems with fixed antenna configuration. With continuous antenna coding, the performance improves another 6 dB. Moreover, the proposed codebook design outperforms previous designs by up to 40% and shows good performance with reduced computational complexity. Overall, the significant improvement in output DC power verifies the potential of leveraging antenna coding utilizing pixel antennas to enhance WPT systems.

Rotatable antennas (RAs) expand the spatial degrees of freedom of multiple-input multiple-output (MIMO) systems beyond conventional fixed-panel arrays. However, existing evaluations often model rotation merely as a scalar gain adjustment and overlook three hardware-critical effects: 1) the phase carried by the antenna pattern, 2) rotation-induced changes in the polarization, and 3) orientation-dependent mutual coupling variations. In this context, we develop a unified modeling-evaluation-optimization framework that explicitly incorporates phase, polarization, and mutual coupling effects through a rotation-explicit complex vector element pattern (CVEP). Polarization coupling and mismatch are captured by embedding the rotated CVEPs into a$2\times 2$per-ray polarization structure, while mutual coupling variation is modeled via power-consistent interpolation of embedded radiation patterns under joint orientation states. Additionally, a compact RA base-station (RA-BS) element and a$6\times 3$RA-BS prototype supporting ±60° mechanical rotation with a high front-to-back ratio are designed, assembled, and measured. The simulation and measurement results validate that the proposed electromagnetic (EM)-consistent RA system model accurately characterizes rotation-induced variations in gain, polarization, and radiation patterns. Furthermore, an alternating feasible-direction optimization algorithm, tailored to the jointly programmable BS- and subarray-level rotations, is developed to maximize the downlink sum-rate in multi-user MIMO deployments. Our simulations demonstrate that the proposed rotation-aware RA-BS architecture significantly outperforms conventional gain-only and mutual-coupling-neglected baselines. These findings highlight the necessity of incorporating phase, polarization, and mutual coupling effects into RA modeling and optimization to ensure robust performance under uniform user distributions and maximize achievable throughput in hotspot deployments.

Non-orthogonal multiple access (NOMA) is a promising multiple access technique. Its performance depends strongly on the wireless channel property, which can be enhanced by reconfigurable intelligent surfaces (RISs). In this paper, we jointly optimize base station (BS) precoding and RIS configuration with unsupervised machine learning (ML), which looks for the optimal solution autonomously. In particular, we propose a dedicated neural network (NN) architecture RISnet inspired by domain knowledge in communication. Compared to state-of-the-art, the proposed approach combines analytical optimal BS precoding and ML-enabled RIS, has a high scalability to control more than 1000 RIS elements, has a low requirement for channel state information (CSI) in input, and addresses the mutual coupling between RIS elements. Beyond the considered problem, this work is an early contribution to domain knowledge enabled ML, which exploit the domain expertise of communication systems to design better approaches than general ML methods.

This work focuses on the use of reconfigurable intelligent surface (RIS) in dual-function radar-communication (DFRC) systems to improve communication capacity and sensing precision, and enhance coverage for both functions. In contrast to most of the existing RIS aided DFRC works where the RIS is modeled as a diagonal phase shift matrix and can only reflect signals to half space, we propose a novel beyond diagonal RIS (BD-RIS) aided DFRC system. Specifically, the proposed BD-RIS supports the hybrid reflecting and transmitting mode, and is compatible with flexible architectures, enabling the system to realize full-space coverage and to achieve enhanced performance. To achieve the expected benefits, we jointly optimize the transmit waveform, the BD-RIS matrices, and sensing receive filters, by maximizing the minimum signal-to-clutter-plus-noise ratio for fair target detection, subject to the constraints of the communication quality of service, different BD-RIS architectures and power budget. To solve the non-convex and non-smooth max-min problem, a general solution based on the alternating direction method of multipliers is provided. Numerical simulations validate the efficacy of the proposed algorithm and show the superiority of the BD-RIS aided DFRC system in terms of both communication and sensing compared to conventional RIS aided DFRC.

In this work, we propose a compact millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) system utilizing electronically steerable parasitic array radiator (ESPAR). We analyze the system and channel models for the compact mmWave massive MIMO system using a beamspace formulation and demonstrate that we can optimize the spectral efficiency of the compact mmWave massive MIMO system by jointly adjusting the variable reactive loads in the ESPAR with a digital beamformer. We formulate the compact mmWave massive MIMO system optimization problem to maximize spectral efficiency and propose an unconstrained optimization based algorithm with an initialization method. The spectral and energy efficiencies of the compact mmWave massive MIMO system are evaluated in comparison to conventional mmWave massive MIMO systems of the same antenna size using full-digital, fully-connected hybrid, and partially-connected hybrid beamforming. The results show that the compact mmWave massive MIMO system provides higher spectral efficiency than partially-connected hybrid beamforming. On top of this, it can provide higher energy efficiencies of around 3.21, 2.88, and 1.22 times compared to full-digital, fully-connected hybrid, and partially-connected hybrid beamforming but with lower hardware complexity and lower cost. Therefore, compact mmWave massive MIMO systems are a promising and effective alternative to conventional massive MIMO systems in millimeter wave (mmWave) communications.

This work investigates the modeling and optimization of beyond diagonal reconfigurable intelligent surface (BD-RIS), which generalizes conventional RIS with diagonal phase shift matrices and provides additional flexibility for manipulating wireless channels, in wideband communication systems. Specifically, we start from the signal modeling of the BD-RIS-aided orthogonal frequency division multiplexing (OFDM) system, which bridges the time-domain and frequency-domain channels, and explicitly shows the frequency dependence of the BD-RIS response. We next characterize the frequency dependence of the BD-RIS response based on circuit models. Benefiting from the admittance parameter analysis, we model individually each tunable admittance component of BD-RIS and derive an approximated linear expression with respect to the frequency of the transmit signals. With the proposed signal model for the BD-RIS-aided OFDM system and the frequency-dependent BD-RIS model, we propose algorithms to optimize the BD-RIS and the power allocation at the transmitter to maximize the average rate for a BD-RIS-aided OFDM system. Finally, simulation results show that BD-RIS outperforms conventional RIS in the OFDM system. More importantly, the impact of wideband modeling of BD-RIS on the system performance becomes more significant as the circuit complexity of BD-RIS architectures increases.

space-division multiple access (SDMA) plays an important role in modern wireless communications. Its performance depends on the channel properties, which can be improved by reconfigurable intelligent surfaces (RISs). In this work, we jointly optimize SDMA precoding at the base station (BS) and RIS configuration. We tackle difficulties of mutual coupling between RIS elements, scalability to more than 1000 RIS elements, and high requirement for channel estimation. We first derive an RIS-assisted channel model considering mutual coupling, then propose an unsupervised machine learning (ML) approach to optimize the RIS with a dedicated neural network (NN) architectureRISnet, which has good scalability, desired permutation-invariance, and a low requirement for channel estimation. Moreover, we leverage existing high-performance analytical precoding scheme to propose a hybrid solution of ML-enabled RIS configuration and analytical precoding at BS. More generally, this work is an early contribution to combine ML technique and domain knowledge in communication for NN architecture design. Compared to generic ML, the problem-specific ML can achieve higher performance, lower complexity and permutation-invariance.

Radio tomographic imaging (RTI) is a device-free sensing technology that can image the radio frequency (RF) attenuation of physical objects in the environment. RTI uses received signal strength (RSS) information from a wireless communication network (WCN) to perform image reconstruction. However it requires a dense WCN consisting of a large number of nodes making it difficult to apply in realistic WCN. In this paper we investigate the performance of RTI when reconfigurable intelligent surfaces (RISs) are integrated into the WCN for integrated sensing and communication (ISAC). This approach can potentially enable the use of RTI in realistic WCN without a large number of nodes. Theoretical and numerical comparisons based on the Cramér-Rao Lower Bound (CRLB) are provided to verify the improvement on sensing performance brought by RIS. The scaling behavior of the reconstruction error as a function of the number of RIS element groups is also derived. Simulations are conducted to validate the proposed RIS-RTI system with a limited number of nodes. The results indicate that incorporating RIS can reduce the necessary number of nodes by more than a half while maintaining high-quality reconstruction compared to a network without RIS.

This work focuses on the synergy of rate-splitting multiple access (RSMA) and beyond diagonal reconfigurable intelligent surface (BD-RIS) to enlarge the coverage, improve the performance, and save on antennas. Specifically, we employ a multi-sector BD-RIS modeled as a prism, which can achieve highly directional full-space coverage, in a multiuser multiple input single output communication system. With the multi-sector BD-RIS aided RSMA model, we jointly design the transmit precoder and BD-RIS matrix under the imperfect channel state information (CSI) conditions. The robust design is performed by solving a stochastic average sum-rate maximization problem. With sample average approximation and weighted minimum mean square error-rate relationship, the stochastic problem is transformed into a deterministic one with multiple blocks, each of which is iteratively designed. Simulation results show that multi-sector BD-RIS aided RSMA outperforms space division multiple access schemes. More importantly, synergizing multi-sector BD-RIS with RSMA is an efficient strategy to reduce the number of active antennas at the transmitter and the number of passive antennas in BD-RIS.

A challenge of ambient backscatter communication (AmBC) systems is signal recovery because the transmitted information bits are embedded in the ambient RF signals and these are unknown and uncontrollable. To meet this challenge, averaging-based energy detectors are typically used but consequently the data rate is low and there is an error floor. Here we propose a new detection strategy based on the ratio between signals received from a multiple-antenna Reader. The advantage of using the ratio is that ambient RF signals are removed directly from the embedded signals without averaging and hence it can increase data rates and avoid the error floor. Different from the original ratio detector that uses the magnitude ratio of the signals between two Reader antennas, in our proposed approach, we utilize the complex ratio so that phase information is preserved and propose an accurate linear channel model approximation. This allows the application of existing linear detection techniques from which we can obtain a minimum distance detector and closed-form expressions for bit error rate (BER). Methods for the estimation of channel state information (CSI) are also provided. In addition, coding and interleaving are also included to further enhance the BER. The results are also general, allowing any number of Reader antennas to be utilized in the approach. Numerical results demonstrate the proposed approach performs better than approaches based on energy detection and the original ratio detectors.

Reconfigurable intelligent surfaces (RISs) allow controlling the propagation environment in wireless networks by tuning multiple reflecting elements. RISs have been traditionally realized through single connected architectures, mathematically characterized by a diagonal scattering matrix. Recently, beyond diagonal RISs (BD-RISs) have been proposed as a novel branch of RISs whose scattering matrix is not limited to be diagonal, which creates new benefits and opportunities for RISs. Efficient BD-RIS architectures have been realized based on group and fully connected reconfigurable impedance networks. However, a closed-form solution for the global optimal scattering matrix of these architectures is not yet available. In this paper, we provide such a closed-form solution proving that the theoretical performance upper bounds can be exactly achieved for any channel realization. We first consider the received signal power maximization in single-user single-input single-output (SISO) systems aided by a BD-RIS working in reflective or transmissive mode. Then, we extend our solution to single-user multiple-input multiple-output (MIMO) and multi-user multiple-input single-output (MISO) systems. We show that our algorithm is less complex than the iterative optimization algorithms employed in the previous literature. The complexity of our algorithm grows linearly (resp. cubically) with the number of RIS elements in the case of group (resp. fully) connected architectures.

Recently, beyond diagonal reconfigurable intelligent surface (BD-RIS) has been proposed to generalize conventional RIS. BD-RIS has a scattering matrix that is not restricted to being diagonal and thus brings a performance improvement over conventional RIS. While different BD-RIS architectures have been proposed, it still remains an open problem to develop a systematic approach to design BD-RIS architectures achieving the optimal trade-off between performance and circuit complexity. In this work, we propose novel modeling, architecture design, and optimization for BD-RIS based on graph theory. This graph theoretical modeling allows us to develop two new efficient BD-RIS architectures, denoted as tree-connected and forest-connected RIS. Tree-connected RIS, whose corresponding graph is a tree, is proven to be the least complex BD-RIS architecture able to achieve the performance upper bound in multiple-input single-output (MISO) systems. Besides, forest-connected RIS allows us to strike a balance between performance and complexity, further decreasing the complexity over tree-connected RIS. To optimize tree-connected RIS, we derive a closed-form global optimal solution, while forest-connected RIS is optimized through a low-complexity iterative algorithm. Numerical results confirm that tree-connected (resp. forest-connected) RIS achieves the same performance as fully-connected (resp. group-connected) RIS, while reducing the complexity by up to 16.4 times.

Reconfigurable intelligent surface (RIS) is an emerging paradigm able to control the propagation environment in wireless systems. Most of the research on RIS has been dedicated to system optimization and, with the advent of beyond diagonal RIS (BD-RIS), to RIS architecture design. However, developing general and unified electromagnetic (EM)-consistent models for RIS-aided systems remains an open problem. In this study, we propose a universal framework for the multiport network analysis of RIS-aided systems. With our framework, we model RIS-aided systems and RIS architectures through impedance, admittance, and scattering parameter analysis. Based on these analyses, three equivalent models are derived accounting for the effects of impedance mismatching and mutual coupling. The three models are then simplified by assuming large transmission distances, perfect matching, and no mutual coupling to understand the role of the RIS in the communication model. The derived simplified models are consistent with the model used in related literature, although we show that an additional approximation is commonly considered in the literature. We discuss the benefits of each analysis in characterizing and optimizing the RIS and how to select the most suitable parameters according to the needs. Numerical results provide additional evidence of the equivalence of the three analyses.

Reconfigurable intelligent surface (RIS) has been envisioned as a highly promising technology for future wireless communication networks. Very recently, a novel beyond diagonal (BD)-RIS architecture has been proposed. This new architecture remarkably extends the traditional diagonal RIS model and yields much more powerful beamforming capability. Meanwhile, however, the emerging symmetry and orthogonality conditions imposed onto BD-RIS’ reflection matrix make its optimization highly difficult, especially when BD-RIS must satisfy numerous additional constraints. This difficulty arises in many BD-RIS applications and has remained unsolved so far. To resolve the above challenge, leveraging the penalty dual decomposition methodology, this paper proposes a novel unified approach that can optimize BD-RIS configuration when it is involved in any number of nonconvex constraints. Especially, we utilize our new approach to solve the power minimization and energy efficiency maximization problems when BD-RIS involves multiple quality-of-service constraints, which have not yet been solved in the literature. Besides, our new approach can also efficiently solve the sum-rate maximization in the BD-RIS assisted system by providing a new analytic-update-based solution, which is more efficient than existing methods. Extensive numerical results demonstrate the effectiveness of our new approach and the significant benefit of BD-RIS over the conventional diagonal RIS.

In this paper, we propose a new solution to improve the achievable rate of radio frequency (RF) powered cognitive radio networks (CRNs) with ambient backscatter communication (AmBC). Assisted with AmBC, the secondary transmitter (ST) can harvest energy and backscatter ambient signals when the primary channel is busy, which enhances the achievable rate compared with conventional RF-powered CRNs adopting the harvest-then-transmit (HTT) protocol. Our work proposes an RF-powered CRN that uses a multi-antenna ST since implementing multiple antennas on ST can enhance energy harvesting and increase the data rate. We discuss the corresponding time sharing and antenna selection tradeoffs and propose a low-complexity and time-efficient block coordinate descent (BCD)-assisted exhaustive search algorithm to find the optimal tradeoff that maximizes the data rate of the system. Simulation results show that our proposed scheme outperforms both the HTT mode and the ambient backscatter technique, leading to improved overall system performance.

Reconfigurable intelligent surface (RIS) has gained much traction due to its potential to manipulate the propagation environment via nearly-passive reconfigurable elements. In our previous work, we have analyzed and proposed a beyond diagonal RIS (BD-RIS) model, which is not limited to traditional diagonal phase shift matrices, to unify different RIS modes/architectures. In this paper, we create a new branch of BD-RIS supporting a multi-sector mode. A multi-sector BD-RIS is modeled as multiple antennas connected to a multi-port group-connected reconfigurable impedance network. More specifically, antennas are divided into$L$($L \ge 2$) sectors and arranged as a polygon prism with each sector covering$1/L$space. Different from the recently introduced concept of intelligent omni-surface (or simultaneously transmitting and reflecting RIS), the multi-sector BD-RIS not only achieves a full-space coverage, but also has significant performance gains thanks to the highly directional beam of each sector. We derive the constraint of the multi-sector BD-RIS and the corresponding channel model taking into account the relationship between antenna beamwidth and gain. With the proposed model, we first derive the scaling law of the received signal power for a multi-sector BD-RIS -assisted single-user system. We then propose efficient beamforming design algorithms to maximize the sum-rate of the multi-sector BD-RIS -assisted multiuser system. Simulation results verify the effectiveness of the proposed design and demonstrate the performance enhancement of the proposed multi-sector BD-RIS.

A method for achieving the continuous-space electromagnetic channel capacity bound using loaded$N$-port structures is described. It is relevant for the design of compact multiple-input multiple-output (MIMO) antennas that can achieve channel capacity bounds when constrained by size. The method is not restricted to a specific antenna configuration and a closed-form expression for the channel capacity limits are provided with various constraints. Furthermore, using loaded$N$-port structures to represent arbitrary antenna geometries, an efficient optimization approach is proposed for finding the optimal MIMO antenna design that achieves the channel capacity bounds. Simulation results of the channel capacity bounds achieved using our MIMO antenna design with one square wavelength size are provided. These show that at least 18 ports can be supported in one square wavelength and achieve the continuous-space electromagnetic channel capacity bound. The results demonstrate that our method can link continuous-space electromagnetic channel capacity bounds to MIMO antenna design.

Reconfigurable intelligent surfaces (RISs) are envisioned as a promising technology for future wireless communications. With various hardware realizations, RISs can work under different modes (reflective/transmissive/hybrid) or have different architectures (single/group/fully-connected). However, most existing research focused on single-connected reflective RISs, mathematically characterized by diagonal phase shift matrices, while there is a lack of a comprehensive study for RISs unifying different modes/architectures. In this paper, we solve this issue by analyzing and proposing a general RIS-aided communication model. Specifically, we establish an RIS model not limited to diagonal phase shift matrices, a novel branch referred to as beyond diagonal RIS (BD-RIS), unifying modes and architectures. With the proposed model, we develop efficient algorithms to jointly design transmit precoder and BD-RIS matrix to maximize the sum-rate for RIS-aided systems. We also provide simulation results to compare the performance of BD-RISs with different modes/architectures. Simulation results show that under the same mode, fully- and group-connected RIS can effectively increase the sum-rate performance compared with single-connected RIS, and that hybrid RIS outperforms reflective/transmissive RIS with the same architecture.

A novel analog beamforming technique that utilizes single radio frequency (single-RF) electronically steerable parasitic array radiator (ESPAR) antennas is proposed. The ESPAR beamforming techniques are aimed at precoding in multiple antenna wireless communication systems using the single or hybrid RF approach. One advantage of ESPAR analog beamforming compared to conventional analog beamforming using phase shifters is that arbitrary patterns can be straightforwardly formed. Therefore, ESPAR analog beamforming can provide performance almost identical to digital beamforming. More importantly, ESPAR analog beamforming does not require phase shifters and power splitters, overcoming implementation challenges of conventional analog beamforming. It also has a more compact size compared to conventional beamforming using uniform antenna arrays. We analyze and optimize ESPAR analog beamforming and propose a channel approach based on beamspace pilot transmission to acquire channel state information (CSI). In addition, we propose limited feedback ESPAR analog beamforming based on a beamforming selection strategy. Simulation results for average gain show that ESPAR analog beamforming has almost identical performance to digital beamforming and better performance than analog beamforming using phase shifters. Energy efficiency simulations show that the technique has the highest efficiency among all the beamforming techniques. Therefore, ESPAR analog beamforming has near optimal performance, high energy efficiency, low circuit complexity, low cost, compact size and can be an effective alternative to conventional analog beamforming.

A systematic and general method for obtaining orthogonal far-field radiation patterns using any electronically steerable parasitic array radiator (ESPAR) embedded in a propagation environment with an arbitrary power angular spectrum (PAS) is described. The method is useful in designing single-RF multiple-input multiple-output (MIMO) systems where the orthogonal patterns can be utilized as a basis set for beamspace MIMO. The method utilizes the correlation matrix of the open-circuit radiation patterns and is termed the pattern correlation decomposition method (PCDM). PCDM is applicable to any antenna type (including realistic antennas with losses), array configuration, PAS and overcomes the limitations of previously proposed techniques. PCDM also provides closed-form solutions for the antenna currents to generate the orthogonal patterns. It also provides an estimate of the effective aerial degrees-of-freedom of the ESPAR. In addition, efficient optimization approaches to finding the optimal reactive loads that excite the required orthogonal radiation patterns are provided. Simulation examples using a 5-element and a 8-element planar inverted-F antenna array including material losses in various PAS are provided. These show that the proposed approach increases system capacity by up to 7 bits/s/Hz or equivalently reduces the required transmit power by up to 73% compared with previous approaches.

Reconfigurable intelligent surfaces (RISs) are an emerging technology for future wireless communication. The vast majority of recent research on RIS has focused on system level optimizations. However, developing straightforward and tractable electromagnetic models that are suitable for RIS aided communication modeling remains an open issue. In this paper, we address this issue and derive communication models by using rigorous scattering parameter network analysis. We also propose new RIS architectures based on group and fully connected reconfigurable impedance networks that can adjust not only the phases but also the magnitudes of the impinging waves, which are more general and more efficient than conventional single connected reconfigurable impedance network that only adjusts the phases of the impinging waves. In addition, the scaling law of the received signal power of an RIS aided system with reconfigurable impedance networks is also derived. Compared with the single connected reconfigurable impedance network, our group and fully connected reconfigurable impedance network can increase the received signal power by up to 62%, or maintain the same received signal power with a number of RIS elements reduced by up to 21%. We also investigate the proposed architecture in deployments with distance-dependent pathloss and Rician fading channel, and show that the proposed group and fully connected reconfigurable impedance networks outperform the single connected case by up to 34% and 48%, respectively.

Ambient radio-frequency (RF) energy harvesting can extract energy from RF signals in the ambient environment and has potential for applications in the Internet of Things. However, the power density of the ambient RF energy is low and therefore methods to maximize the average output direct current (dc) power are required. In this work we show that average output dc power in ambient RF energy harvesting is nonlinearly dependent on antenna directivity and linearly dependent on antenna port number. To maximize average output dc power it is therefore necessary to utilize directional multiport rectennas. To demonstrate the enhancements possible, the design for a directional 4-port pixel patch rectenna system to harvest ambient RF energy from the GSM-1800 frequency band is provided. The design has an average antenna size for each port of 0.3λ×0.3λ, and realized gains of 5.5 dBi. Measurement results show that the proposed rectenna can increase average output dc power by up to 6.2 and 4.5 dB compared to omnidirectional and directional single-port rectenna designs of similar size, respectively. Measurement in a real ambient environment is also conducted, showing that the proposed rectenna can achieve an output dc power of 11.2 μW which is five times higher than the two reference rectennas.

In this paper, we study a multi-sine multiple-input multiple-output (MIMO) wireless power transfer (WPT) system with the objective to increase the output DC power. We jointly optimize the multi-sine waveform and beamforming accounting for the rectenna nonlinearity, and consider two combining schemes for the rectennas at the receiver, namely DC and RF combinings. For DC combining, the waveform and transmit beamforming are optimized, as a function of the channel state information (CSI). For RF combining, the optimal transmit and receive beamformings are provided in closed form and the waveform is optimized. We also consider a practical RF combining circuit using phase shifter and RF power combiner and optimize the waveform, transmit beamforming, and analog receive beamforming adaptive to the CSI. Two types of performance evaluations, based on the nonlinear rectenna model and accurate and realistic circuit simulations, are provided. The evaluations demonstrate that the joint waveform and beamforming design can increase the output DC power by leveraging the beamforming gain, the frequency diversity gain, and the rectenna nonlinearity. It also shows that the joint waveform and beamforming design provides a higher output DC power than the beamforming-only design with a relative gain of 180% in a two-transmit antenna sixteen-sinewave two-receive antenna setup.

A systematic method based on the Theory of Characteristic Modes (TCM) for finding orthogonal radiation patterns of any electronically steerable parasitic array radiator (ESPAR) is described. This method can be useful for designing single-RF front-end multiple-input multiple-output (MIMO) systems in which orthogonal patterns can be utilized as a basis set for space modulation (SM) or full multiplexed MIMO systems. The method is based on the$N$-port formulation of TCM and is not restricted to antenna type or array configuration. It can also provide closed formed solutions for the antenna element currents required to generate the orthogonal patterns. In addition an approach to finding the required load reactances in ESPAR for generating the orthogonal patterns is provided and is based on the quasi-Newton method utilizing a closed form expression for the initial approximate solution. Approximate estimation of the effective aerial degrees of freedom of the ESPARs is also discussed. Two simulation examples of ESPARs, a 4-element linear dipole array and an 8-element rectangular planar inverted-F antenna array, using SM as well as full multiplexed MIMO are provided, demonstrating the effectiveness of the proposed method.

Ambient backscatter communication (AmBC) leverages the existing ambient radio frequency (RF) environment to implement communication with battery-free devices. The key challenge in the development of AmBC is the very weak RF signals backscattered by the AmBC Tag. To overcome this challenge, we propose the use of orthogonal space-time block codes (OSTBC) by incorporating multiple antennas at the Tag as well as at the Reader. Our approach considers both coherent and non-coherent OSTBC so that systems with and without channel state information can be considered. To allow the application of OSTBC, we develop an approximate linearized and normalized multiple-input multiple-output (MIMO) channel model for the AmBC system. This MIMO channel model is shown to be accurate for a wide range of useful operating conditions. Two coherent detectors and a non-coherent detector are also provided based on the proposed AmBC channel model. Simulation results show that enhanced bit error rate performance can be achieved, demonstrating the benefit of using multiple antennas at the Tag as well as the Reader.

In this article, we study the multiple-input and multiple-output (MIMO) wireless power transfer (WPT) system so as to enhance the output DC power of the rectennas. To that end, we revisit the rectenna nonlinearity considering multiple receive antennas. Two combining schemes for multiple rectennas at the receiver, DC and RF combinings, are modeled and analyzed. For DC combining, we optimize the transmit beamforming, adaptive to the channel state information (CSI), so as to maximize the total output DC power. For RF combining, we compute a closed-form solution of the optimal transmit and receive beamforming. In addition, we propose a practical RF combining circuit using RF phase shifter and RF power combiner and also optimize the analog receive beamforming adaptive to CSI. We also analytically derive the scaling laws of the output DC power as a function of the number of transmit and receive antennas. Those scaling laws confirm the benefits of using multiple antennas at the transmitter or receiver. They also highlight that RF combining significantly outperforms DC combining since it leverages the rectenna nonlinearity more efficiently. Two types of performance evaluations, based on the nonlinear rectenna model and based on realistic and accurate rectenna circuit simulations, are provided. The evaluations demonstrate that the output DC power can be linearly increased by using multiple rectennas at the receiver and that the relative gain of RF combining versus DC combining in terms of the output DC power level is very significant, of the order of 240% in a one-transmit antenna ten-receive antenna setup.