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Heritage multimedia, which comprises images, audio, and videos, are precious artifacts of a region. In addition to enabling a better understanding of earlier generations, heritage media provides information about their creative approach, style of living, and diversity of historical and archaeological ideologies. Heritage is an important resource that boosts the local economy, generates sustainable communities, and improves tourism and business sectors. Once heritage images are dissipated to consumers, they may be transmitted through wire/wireless systems, where the data may be accessed by both authorized and unauthorized consumers. With the rapid advancement of technology and 5G networks, establishing an approach that protects cultural heritage media from unauthorized consumers is necessary. This study presents a robust and blind watermarking-framework for cultural images (RBWCI) utilizing the discrete cosine transform domain for ownership verification and copyright protection. The embedding scenario of our technique relies on the difference between coefficients obtained from the two channels of the YCbCr color model. The watermark bit is embedded by taking the difference between two preselected mid-frequency coefficients (Cb-Cr). Moreover, chaotic and deoxyribonucleic acid encryption are employed to ensure double-layer watermark security. The proposed algorithm demonstrates a peak signal-to-noise ratio between 42-43 dB and structural similarity index metric value of approximately 1 for different test images when not under attack. The robustness of the RBWCI was revealed by comparing it with various state-of-the-art schemes, making it suitable for different consumer applications.

An effective and practical joint analog and digital self-interference cancellation (SIC) scheme without additional signalling overhead for an I/Q imbalanced full duplex transceiver is proposed in this paper. This scheme combines an I/Q imbalanced analog least mean square (ALMS) loop at the transceiver radio frequency frontend and a two-stage digital signal processing (DSP) at the digital baseband to achieve excellent SIC performance with low complexity. The steady state weighting coefficients of the I/Q imbalanced ALMS loop with periodical transmitted signal and the loop’s convergence behaviour are firstly analysed. The residual SI is then modelled as the output of a time-varying widely linear system. With a track/hold control mechanism applied to the ALMS loop, the system model for digital SIC is further presented, followed by the DSP algorithms suitable for real-time implementation. The noise enhancement in each stage digital cancellation is also analysed and formulated. Finally, simulation results are provided to verify the theoretical analyses and demonstrate the overall SIC performance.

As the Wi-Fi technology transitions into its sixth generation (Wi-Fi 6), there is a growing consensus on the need to support security and coordination functions at the Physical (PHY) layer. In contrast to the costly approach of extending the PHY-layer header to support new functions (e.g., Spatial Reuse field in the 802.11ax-based Wi-Fi frame), we propose to turn specific parts of the frame preamble into a reliable data field while maintaining its primary functions. Specifically, in this paper, we develop a scheme called extensible preamble modulation (eP-Mod) for 802.11n/ac/ax protocols that are built on multiple-input-multiple-output (MIMO) and orthogonal frequency-division multiplexing (OFDM). For each frame, eP-Mod can embed up to 144 user bits into the 802.11ac preamble of an 8×8 MIMO 40MHz transmission. The proposed scheme is adaptive to channel conditions and enables several promising PHY-layer services, such as PHY-layer encryption and channel/device authentication, and PHY-layer signaling. At the same time, it allows legacy (eP-Mod-unaware) devices to continue to process the received preamble as normal by guaranteeing that the proposed preamble waveforms satisfy the structural properties of a standardized preamble. Through numerical analysis, extensive simulations, and hardware experiments, we validate the practicality and reliability ofeP-Mod.

This work generalizes the alternating projection approach (APA) to synthesize wideband frequency-invariant (FI) beam patterns for linear nonuniformly spaced arrays (NUSAs). Different from the conventional APA where excitation weights are synthesized for a desired pattern at single frequency, the generalized APA intends to find appropriate finite-impulse response (FIR) filter coefficients for each antenna element to provide the required wideband frequency-dependent excitation for a desired wideband FI pattern, and two modification techniques called mainlobe FI modification and wideband sidelobe control are introduced in each iteration to successively update the FI pattern performance. Several examples for synthesizing wideband FI beam patterns for NUSAs in different applications are conducted to verify the effectiveness and advantages of the proposed generalized APA.

In this paper, we utilize a concatenation scheme to construct new families of quantum error correction codes achieving the quantum Gilbert-Varshamov (GV) bound asymptotically. We concatenate alternant codes with any linear code achieving the classical GV bound to construct Calderbank-Shor-Steane (CSS) codes. We show that the concatenated code can achieve the quantum GV bound asymptotically and can approach the Hashing bound for asymmetric Pauli channels. By combing Steane's enlargement construction of CSS codes, we derive a family of enlarged stabilizer codes achieving the quantum GV bound for enlarged CSS codes asymptotically. As applications, we derive two families of fast encodable and decodable CSS codes with parameters $\mathscr{Q}_1=[[N,\Omega(\sqrt{N}),\Omega( \sqrt{N})]],$ and $\mathscr{Q}_2=[[N,\Omega(N/\log N),\Omega(N/\log N)/\Omega(\log N)]].$ We show that $\mathscr{Q}_1$ can be encoded very efficiently by circuits of size $O(N)$ and depth $O(\sqrt{N})$. For an input error syndrome, $\mathscr{Q}_1$ can correct any adversarial error of weight up to half the minimum distance bound in $O(N)$ time. $\mathscr{Q}_1$ can also be decoded in parallel in $O(\sqrt{N})$ time by using $O(\sqrt{N})$ classical processors. For an input error syndrome, we proved that $\mathscr{Q}_2$ can correct a linear number of ${X}$-errors with high probability and an almost linear number of ${Z}$-errors in $O(N )$ time. Moreover, $\mathscr{Q}_2$ can be decoded in parallel in $O(\log(N))$ time by using $O(N)$ classical processors., Comment: 11 pages,2 figures

For two unknown mixed quantum states $\rho$ and $\sigma$ in an $N$-dimensional Hilbert space, computing their fidelity $F(\rho,\sigma)$ is a basic problem with many important applications in quantum computing and quantum information, for example verification and characterization of the outputs of a quantum computer, and design and analysis of quantum algorithms. In this paper, we propose a quantum algorithm that solves this problem in $\operatorname{poly}(\log (N), r, 1/\varepsilon)$ time, where $r$ is the lower rank of $\rho$ and $\sigma$, and $\varepsilon$ is the desired precision, provided that the purifications of $\rho$ and $\sigma$ are prepared by quantum oracles. This algorithm exhibits an exponential speedup over the best known algorithm (based on quantum state tomography) which has time complexity polynomial in $N$., Comment: Final version with an improvement over the previous version. 19 pages, 2 tables, 1 algorithm

A highly multifunctional antenna with multilinear polarizations (MLPs) and multibands (MBs) is proposed, which can operate over four reconfigurable frequency bands, each band having 16 switchable linear polarizations (LPs) at an 11.25° interval. The larger number of MLPs is achieved by developing an odd-even switching strategy for the independently controlled p-i-n diodes placed between the inner patch and outer sector patches. The diode control is enabled by a field programmable gate array (FPGA) with an engineered biasing network. Moreover, the reported antenna can switch among four continuous bands for each LP, covering a combined range larger than 35%. It is an attractive feature, yet a challenging task for an antenna with such a large number of MLPs. This is realized by utilizing a double-layer feeding strategy with appropriate sector-patch activation, which is particularly appealed to such a highly multifunctional antenna. Furthermore, the antenna has a rotational symmetry along the azimuthal direction and, hence, all the LPs show almost rotationally invariant pattern shapes and gains. The measured results are consistent with the simulated ones, commonly validating the effectiveness of our design.

Reconfigurable surfaces (RS) have recently emerged as an enabler for smart radio environments where they are used to actively tailor/control the radio propagation (e.g., to support users under adverse channel conditions). If multiple RSs are deployed (e.g., coated on various buildings) to support different groups of users, it is critical to jointly optimize the phase-shifts of all the RSs to mitigate interference amongst them as well as to leverage the secondary reflections amongst them. Motivated by these considerations, this paper considers the uplink transmissions of multiple users that are grouped and supported by multiple RSs to communicate with a multi-antenna base station (BS). We first formulate two optimization problems: the weighted sum-rate maximization and the minimum achievable rate (from all users) maximization. Unlike existing works that considered single user or single RS or multiple RSs without inter-RS reflections, the considered problems require the joint optimization of the phase-shifts of all RS elements and all beamformers at the multi-antenna BS. The two problems turn out to be non-convex and thus are difficult to be solved in general. Moreover, the inter-RS reflections give rise to the coupling of the phase-shifts amongst the RSs, making the optimization problems even more challenging to solve. To tackle them, we design alternating optimization algorithms that provably converge to locally optimal solutions. Simulation results reveal that by effectively mitigating interference and leveraging the secondary reflections amongst the RSs, there is a great benefit of deploying more RSs to support different groups of users so as to achieve a higher rate per user. This gain is even more significant with a larger number of elements per RS. Without properly dealing with the secondary reflections, by contrast, increasing the number of RSs can adversely impact the network throughput, especially for high transmit power.

Currently, there is increasing interest in analog multibeam antennas whose beams can be flexibly steered to arbitrary directions. In a previous paper, we presented the theoretical framework for synthesizing individually steerable multiple beams using generalized joined coupler (GJC) matrices. The synthesis method was to optimize the array excitation vectors to approximate known distributions. In this article, we present a more robust optimization method to optimize the multibeams directly in order to control the half-power beamwidth, the sidelobe levels, and nulls for mitigating system interference. The effectiveness of the proposed method is demonstrated by numerical examples. We reveal how the quality of the multiple beams is inherently determined by the dimensions of the GJC matrix. Experimental results of a 3 10 Nolen-like GJC matrix are presented for the first time to validate the proposed method in realizing low sidelobe multibeams.

More and more 5G and AI applications demand flexible and low-cost processing of their traffic through diverse virtualized network functions (VNFs) to meet their security and privacy requirements. As such, the Network Function Virtualization (NFV) market has been emerged as a major service market that allows network service providers to trade their network services among customers. Since each service market usually involves complex interplays among players with different roles, efficient mechanisms that guarantee stable and efficient operations of the NFV market are urgently needed. One fundamental problem in the NFV market is how to maximize the social welfare of all players so that all players have incentives to participate in the activities of the market. In this paper, we first formulate a novel social welfare maximization problem in an NFV market of a multi-tier edge cloud network, with the aim to maximize the total revenue collected from all players, and we implement VNF services on Virtual Machines (VMs) leased by service providers to fulfill customers with service requests, where the edge cloud network consists of both cloudlets in edge networks and remote data centers in the core network. We then design an efficient incentive-compatible mechanism for the problem, and analyze the existence of a Nash equilibrium of the mechanism. Also, we consider an online social welfare maximization problem with uncertain values of customers and without the knowledge of future request arrivals, for which we devise an online learning algorithm by adopting the Multi-Armed Bandits (MAB) method with a bounded regret. We finally evaluate the performance of the proposed mechanisms through simulations and a testbed. Results show that the proposed mechanisms deliver up to 27% higher social welfare than those of existing studies

In this paper, we study a novel service reliability augmentation problem in an Edge Computing network, where mobile users request network services with a service function chain (SFC) and reliability expectation requirements. To enhance the service reliability of user requests, it is a common practice to make use of redundant network function (VNF) instance placement in case their primary VNF instances fail. To this end, we first formulate a novel service reliability augmentation problem for each admitted request and show the NP-hardness of the problem. We then provide an admission framework of user requests by placing primary VNF instances of network functions in the SFC to different cloudlets, and then develop a randomized algorithm with a good approximation ratio and high probability for the service reliability augmentation problem, at the expense of moderate resource constraint violations. We also devise a deterministic heuristic for the problem without any resource violation. We thirdly extend the proposed algorithm for a single request to the service reliability augmentation problem for a set of requests. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising and superior to their analytical counterparts.

In this communication, two polarization selective partial reflective decoupling layers (PRDLs) are utilized to mitigate at least two kinds of mutual couplings between two closely spaced dual-polarized dipole antennas. The PRDL is a large-scale model of an actual dielectric board, obtained by arranging a large number of identical conducting obstacles in a regular two-dimensional pattern. The two PRDLs are designed at proper distances from the antenna aperture with well-engineered dielectric constants for two preset polarizations. Each layer is selective to only one polarization by appropriately designing the dimension of the unit cells. Consequently, the two dominant couplings between the two dual-polarized antennas are reduced significantly. The measured isolation between all ports of the two antennas are increased to more than 21 dB, while their reflection coefficients can still maintain in satisfactory status. The radiation patterns, total efficiencies, and envelop correlation coefficients of the proposed antenna, as well as its counterpart without any decoupling measures, are also obtained through measurement, showing significant and reasonable improvements, which further indicates that it has great potential to be extended and applied to 5G MIMO arrays in both base stations and wireless routers.

The fast proliferation of digital twin (DT) establishes a direct connection between the physical entity and its deployed digital representation. As markets shift toward mass customization and new service delivery models, the digital representation has become more adaptive and agile by forming digital twin networks (DTN). DTN institutes a real-time single source of truth everywhere. However, there are several issues preventing DTN from further application, which are centralized processing, data falsification, privacy leakage, lack of incentive mechanism, etc. To make DTN better meet the ever-changing demands, we propose a novel blockchain-enabled adaptive asynchronous federated learning (FedTwin) paradigm for privacy-preserving and decentralized DTN. We design Proof-of-Federalism (PoF), which is a tailor-made consensus algorithm for autonomous DTN. In each DT's local training phase, generative adversarial network enhanced differential privacy is used to protect the privacy of local model parameters while a modified Isolation Forest is deployed to filter out the falsified DTs. In the global aggregation phase, an improved Markov decision process is leveraged to select optimal DTs to achieve adaptive asynchronous aggregation while providing a roll-back mechanism to redact the falsified global models. With this paper, we aim to provide insights to the forthcoming researchers and readers in this under-explored domain.

A novel method of synthesizing shaped-beam cylindrical conformal array by rotating the antenna elements and optimizing their excitation phases is presented. Rotation of antennas on curved surface is mathematically described by rotating its vectorial active element pattern (VAEP) in its local coordinate system (LCS). Then the scalar patterns and polarization unit vectors of all the rotated VAEPs are properly transformed and then interpolated at a unified angle sampling grid in a common coordinate system, where they are summed to obtain an approximated array expression. With this expression, element rotations and phases can be optimized using the constriction factor particle swarm optimization (CF-PSO) to synthesize desired shaped-beam pattern with controlled sidelobe level (SLL) and cross-polarization level (XPL). However, since rotations on curved surface introduce considerable variations to the element curvature and mutual coupling (MC), the synthesized array pattern would have considerable errors. A refined strategy is adopted then to improve the synthesis accuracy. The proposed method uses uniform amplitude weighting, thus saving many unequal power dividers. Three typical shaped pattern synthesis examples are provided to show the effectiveness of the proposed method. A cylindrical array prototype with 24 rotated U-slot loaded patch antennas is fabricated and measured for practical validation.

In this work, a parametric up-converter amplifier is introduced as a wideband impedance-matching network. Chu’s limit restricts the minimum Q-factor of unloaded small antennas. However, the practical bandwidth (BW) of small antennas is defined by their loaded Q-factor. By connecting a small antenna to an amplifier with a real input impedance that is several times greater than the radiation resistance of the antenna, we propose increasing the return loss, which leads to a reduction in the loaded Q-factor and an increase in the BW. In addition, a parametric amplifier is used because, in comparison with transistor amplifiers, it offers low-noise characteristics. The gain of the low-noise parametric amplifier compensates for the loss due to the imposed mismatch. Our simulation result shows BW improvements up to 32 times can be accomplished by trading 2 dB of noise figure (NF), compared to the 15 dB suggested by Chu’s limit for a lossy antenna. Accepted version

One of the key antenna requirements in many modern wireless systems for communications and sensing is wide-angle beam-scanning [1]. The traditional method to achieve wide-angle beam-scanning is to employ mechanically controlled reflectors or lenses, which are bulky, heavy, and suffer from slow beam scanning speed. Other important limitations of mechanical scanning antennas include the lack of multi-beam scanning capability and the ability to conform with non-planar structures (conformal geometries), which are essential in a number of emerging systems requiring very low-profile antennas. An alternative technique is to employ electronically beam-scanning antennas using passive or active phased arrays [1]. Main disadvantages of phased arrays are high complexity, high power consumption, and high cost due to a large number of radio frequency (RF) or microwave phase shifters and T/R modules required. The problems worsen for phased arrays at millimeter-wave, sub-THz, and THz frequencies, due to significant losses in phase shifters and feed networks at higher frequencies combined with lower efficiency of power amplifiers [2]. The digital beamforming approach is even more costly and energy hungry due to the employment of large number of RF modules and digital devices [1], [3], [4].

A low-profile and shared aperture dual-polarized (DP) omnidirectional antenna by reusing the structure of an annular quasi-dipole array is presented. The proposed antenna consists of a center-fed circular patch antenna, an annular quasi-dipole array, a wideband one-to-four power divider integrated with the center-fed circular patch antenna, and two annular strips. The center-fed circular patch antenna is utilized as the driven element for the vertically polarized (VP) radiation. The annular quasi-dipole array fed by the one-to-four power divider produces horizontally polarized (HP) radiation. Simultaneously, it also acts as the VP director to improve VP gain and bandwidth. Besides, the two annular strips enhance the HP omni-directivity and gain. In this way, the VP and HP share the same aperture to improve the profile utilization rate effectively. Due to the symmetry of the basic antenna structure, stable DP donut patterns are obtained within the entire operating bandwidth. For demonstration, a 4.1-5.0 GHz prototype is designed, fabricated, and measured. Under a low profile of 0.13λ0 , the prototype realizes a-10 dB overlapped bandwidth of 20%, high port isolation of above 35 dB, and stable DP donut patterns with horizontal gain larger than 1.8 dBi.

In this article, a new approach to designing 6 × 6 beamforming networks (BFNs) for producing five or six beams is presented. The configuration of the 6 × 6 BFNs is designed utilizing planar wideband hybrid couplers and phase shifters. A new type of wideband hybrid coupler is built based on a slotline resonator and microstrip-to-slotline transitions. The proposed coupler design can achieve high level of isolation between the sum and difference ports. Two different formats of wideband 6 × 6 BFNs are then developed, to generate five and six beams, respectively. A prototype is fabricated and tested, and the experimental results agree with the simulation ones very well, achieving a wide operating bandwidth of more than 30%. Finally, the BFN is verified by feeding a six-element array, and the realized patterns match the predicted ones very well, demonstrating the function of the BFN. The topology of an extended 12 × 12 BFN is also given to demonstrate the possibility of creating higher-order BFNs using the presented approach.

Edge computing has received significant attention from academia and industries and has emerged as a promising solution for enhancing the information processing capability at the edge for next generation 6G networks. The technical design of 6G edge networks in terms of offloading the computationally extensive task is very critical because of the overgrowth in data volume primarily due to the explosion of smart IoT devices, and the ever-reducing size of these energy-constrained devices in IoT systems. Toward harnessing the benefits of deep recurrent neural network based on Long Short Term Memory (LSTM) in the design of next-generation edge networks, this paper presents a framework DECENT-Deep learning Enabled green Computation for Edge centric Next generation 6G neTworks. The data offloading problem is modeled as a Markov decision process considering joint optimization of energy consumption, computation latency, and offloading rate for network utility in 6G environment. The algorithm learns faster from previous long-term offloading experiences and solves the optimization problem with better convergence speed. Simulation results of the proposed framework DECENT shows that it maximizes the network utility by overcoming the challenges as compared to the state-of-the-art techniques.

Sensor communication in the smart home environment is still in its infancy as the information exchange between sensors is vulnerable to security threats. Many traditional solutions use single-layer or multi-layer (i.e., onion routing protocol) encryption/decryption algorithms. But, in the traditional onion routing protocol, if the directory server is compromised, it may not track the malicious onion nodes within the onion network. It questioned the path anonymity of the onion routing protocol. Motivated by this, we proposed a blockchain and onion routing (OR)-based secure and trusted framework in the paper. The anonymity of the proposed OR network is maintained by storing and tracking the onion nodes threshold values through the blockchain network. A long short-term memory (LSTM) model is also utilized to classify the sensors data requests as malicious and non-malicious. The performance of the proposed system is evaluated with different performance metrics such as F1 score and accuracy. The LSTM model significantly improves the initial detection rate of malicious data requests from smart home sensors. Over these benefits, we considered the entire communication via 6G channel, reducing the overall communication latency. Additionally, the OR network is simulated over the shadow simulator to analyze the OR network's performance considering parameters such as packet delivery ratio and malicious onion node detection rate.

Space-air-ocean integrated networks (SAOINs), composed of low earth orbit (LEO) satellites, unmanned aerial vehicles (UAVs), and unmanned surface vehicles (USVs), have been advocated to provide seamless, high-rate, and reliable wireless transmission services for USVs. However, due to the restrictions of limited resources (e.g., spectrum bandwidth, transmission power, etc.), diverse demands of USVs, and selfishness of both UAVs and LEOs, there comes a significant challenge to provision high-quality wireless data rate for USVs to achieve their satisfied quality of experience (QoE). To this end, in this paper, we propose a hierarchical on-demand wireless data rate provisioning scheme to provide ubiquitous transmission services for USVs. Specifically, we first devise a hierarchical wireless data rate provisioning framework. The LEO satellite with an extensive wireless coverage is utilized to provide LEO satellite-to-UAV (L2U) data rate for UAVs with a certain L2U data rate price. Each UAV is employed to provide UAV-to-USV (U2U) data rate for covered multiple USVs with a certain U2U data rate price. We then propose a modified three-stage Stackelberg game to model the wireless data rate assignments among LEO satellites, UAVs, and USVs, where the time-varying data rate demands of USVs are considered to formulate the utility maximization problem. Afterwards, the backward induction approach is leveraged to attain the Stackelberg equilibrium as the solution of the formulated problem, where the closed-form expressions on the optimal strategies of both USVs and UAVs under different data rate budgets are obtained by the nonlinear programming method. Besides, an accelerated conjugate gradient descent (ACGD) based iteration algorithm is also designed to obtain the optimal strategies of the LEO satellites on the L2U data rate prices. At last, extensive simulations are carried out to demonstrate that the proposed scheme can significantly increase the utilities of USVs, as compared to other benchmark schemes.

A low-profile, electrically small, ultrawideband (UWB) antenna enabled with a grid array metasurface is presented. The design incorporates a driven dipole, an electric near-field resonant parasitic (NFRP) element, and a grid array structure which operates as an inductive metasurface. The NFRP element is an Egyptian axe dipole (EAD) that is excited by the driven dipole. This combination itself is an electrically small NFRP dipole antenna. In contrast to previous versions, its operation in both its first and third resonant modes is established. The presence of the grid, which consists of identical interconnected rectangular loops, then provides a new dipole mode and facilitates the reduction of the frequency ratio of the NFRP dipole's modes. The resulting overlap of these three resonant modes yields the UWB operation. The electrically small (ka = 0.92), low-profile ( 0.003λ0 ) antenna exhibits uniform radiation patterns and stable and high radiation efficiency (RE) performance characteristics. The antenna was optimized, fabricated, and tested. The measured results, in good agreement with their simulated values, demonstrate that the antenna yields an ultrawide, 67.7%, -10 dB impedance bandwidth with stable realized gain (RG) values, ~1.6 dBi, and high RE values, RE >77%, over its entire operational bandwidth.

Controlling water salinity plays a key role in farming efficiency. Current sensors are mostly expensive and need regular maintenance. In addition, they require electrical connections or extra power supply that leads to difficult and costly implementation in remote-sensing scenarios. In this article, an accurate and low-profile sensor is developed using a metamaterial perfect absorber (MPA) structure. The proposed sensor works based on the level and frequency of the absorbed signals. Hence, there is no need for electrical connections, which enables remote-sensing applications. Square-shaped channels have been created in a regular FR-4 substrate to facilitate sensing of water salinity levels. A 7 × 7 array with a total size of 140 mm × 160 mm has been fabricated that shows a resolution of 10 MHz per percentage of water salinity. The absorption frequency shifts from f=3.12 to 3.59 GHz for salinity level from 0% to 50%. A strong correlation between measurement and simulation results validates the design procedure.

Future mobile networks are envisioned to become multi-service systems, enabling the dynamic deployment of services with vastly different performance requirements, accommodating the needs of diverse service providers. Virtualizing the mobile network infrastructure is of fundamental importance for realizing this vision in a cost-effective manner. While there have been extensive research efforts in virtualization for the mobile core network, virtualization in the radio access network (RAN) is still at an early stage. In this paper, we present AIRTIME, a new RAN slicing system that enables the dynamic on-the-fly virtualization of RANs, with the programmability required by service providers to customize any aspect of their virtual RAN to meet their service needs. We present a prototype implementation of AIRTIME and evaluate the: (i) capacity to create virtual RANs on-the-fly, (ii) performance experienced by slice owners, (iii) isolation among multiple virtual RANs sharing the same physical infrastructure, and (iv) scalability to accommodate a large number of virtual RANs. Accepted version

Electrically small, high-directivity antennas are in demand for a variety of current and future wireless applications. An electrically small directive antenna (ESDA) that requires only one specially engineered port to excite a set of multipoles is demonstrated in this article. Four 90° copper sectors are combined with additional structures and fed with a coaxial cable. Two resonant quadrupoles (equivalent to two pairs of resonant electric dipoles) and one magnetic dipole are excited. Both high radiation efficiency and good impedance matching are achieved. Theoretical calculations, numerical simulations, and experimental measurements are shown to be in good agreement. An optimized prototype is designed, fabricated, and tested. The measured results confirm that it is a supergain system. The unidirectional ESDA has a peak directivity of 6.71 dBi, a peak realized gain of 6.31 dBi, radiation efficiency of 94.5%, and a front-to-back ratio of 14.89 dB at its resonance frequency, 814 MHz. Its height is 0.06 lambda -{mathrm{ res}} , and ka = 0.98. These measured realized gain and directivity values exceed both the Harrington and Kildal-Best ka -based upper limits.

By virtue of the prediction abilities of machine learning (ML) methods, the ML-assisted evolutionary algorithm has been treated as an efficient solution for antenna design automation. This article presents an efficient ML-based surrogate-assisted particle swarm optimization (SAPSO). The proposed algorithm closely combines the particle swarm optimization (PSO) with two ML-based approximation models. Then, a novel mixed prescreening (mixP) strategy is proposed to pick out promising individuals for full-wave electromagnetic (EM) simulations. As the optimization procedure progresses, the ML models are dynamically updated once new training data are obtained. Finally, the proposed algorithm is verified by three real-world antenna examples. The results show that the proposed SAPSO-mixP can find favorable results with a much smaller number of EM simulations than other methods.

Power-handling capability of bulk CMOS-based single-pole double-throw (SPDT) switch operating in millimeter-wave (mm-wave) and subterahertz region is significantly limited by the reduced threshold voltage of deeply scaled transistors. A unique design technique based on impedance transformation network (ITN) is presented in this work, which improves 1-dB compression point, namely P1dB, without deteriorating other performance. To prove the presented solution is valid, a 70-100-GHz switch is designed and implemented in a 55-nm bulk CMOS technology. At 90 GHz, it achieves a measured P1dB of 15 dBm, an insertion loss (IL) of 3.5 dB, and an isolation (ISO) of 18 dB. The total area of the chip is only 0.14 mm2.

We introduce and analyse the problem of encoding classical information into different resources of a quantum state. More precisely, we consider a general class of communication scenarios characterised by encoding operations that commute with a unique resource destroying map and leave free states invariant. Our motivating example is given by encoding information into coherences of a quantum system with respect to a fixed basis (with unitaries diagonal in that basis as encodings and the decoherence channel as a resource destroying map), but the generality of the framework allows us to explore applications ranging from super-dense coding to thermodynamics. For any state, we find that the number of messages that can be encoded into it using such operations in a one-shot scenario is upper-bounded in terms of the information spectrum relative entropy between the given state and its version with erased resources. Furthermore, if the resource destroying map is the twirling channel over some unitary group, we find matching one-shot lower-bounds as well. In the asymptotic setting where we encode into many copies of the resource state, our bounds yield an operational interpretation of resource monotones such as the relative entropy of coherence and its corresponding relative entropy variance., 14 pages. Published version

A spherical Luneburg lens made of parallel planar layers of lightweight foam with embedded conducting cylindrical inserts on a uniform hexagonal grid centered in each layer is presented. This work draws on the authors' previous paper (Ansari et al., 2020) describing a Luneburg lens that uses cubic conducting inserts on a uniform cubic grid. This previous lens, while being of lightweight and economical construction, suffered from anisotropy resulting in a focal length that varied with the inclination of the beam relative to the orientation of the cubic grid. The lens described here largely overcomes this problem and allows for simpler and more economical construction. A prototype lens designed for the 3.3-3.8 GHz band with a diameter of 400 mm and a beamwidth of 14° was tested. Radiation patterns at wide scanning angles were nearly identical, and cross-polarization for slant incident polarization was below -25 dB on boresight and below -18 dB for all angles. A characteristic of this lens construction is its extremely high efficiency. The measured gain at the mid-band was 21.6 dBi, agreeing with simulated gain based on lossless materials to within measurement error. It is shown that wider bandwidths are obtainable if the thickness of the layers is reduced.

This communication presents a dual-band aperture-shared high gain antenna for mm-wave multi-beam and Sub-6 GHz applications by integrating transmitarray into Fresnel zone plate (FZP) lens. The transmistarray consists of two sets of unit cells (UCs), which can provide high transmission magnitude and [-π, π] phase tuning at the mm-wave frequency band. Meanwhile, they show distinct transmission features at the Sub-6 GHz band (one set of UC allows the electromagnetic (EM) wave to pass while the other prohibits the EM wave from passing). Then, by properly arranging the two kinds of UCs into the transparent and opaque region of the FZP lens, respectively, the Sub-6 GHz FZP lens and the mm-wave band transmitarray are integrated into the same aperture seamlessly. A waveguide-integrated patch antenna is adopted as the feed. The patch antenna operates at the Sub-6 GHz band and three open waveguide antennas are used for the mm-wave multi-beam radiation. The proposed antenna achieves high gain at both bands with a reused radiating aperture. Mm-wave multi-beam radiation is obtained without an extra feeding network. For demonstration purposes, the central frequencies of the Sub-6 GHz and the mm-wave bands are selected at 6-GHz and 27-GHz, respectively. An antenna prototype is fabricated and experimentally verified.

Although the vision of cognitive and intelligent Internet of Things is fascinating, it is still challenging to maximize its advantages in high-speed mobile networks. In this article, critical obstacles, including inflexible interactions, unreliable connections, and inefficient computations, are focused to establish a better communication solution. First, a concept named mobile-aware resource sharing (MARS) is proposed with straightforward motivation based on smart collaborative theory. Second, inspired by moving velocity and processing capacity, the design details and corresponding optimizations are analyzed from multiple per-spectives. Third, the implementation procedures are introduced to establish slight cooperations, stable associations, and strong virtualizations by considering specific features of fog, edge, and cloud zones. Fourth, both the functionality and performance are validated and discussed in a complex environment. The comparison results illustrate desirable improvements in critical latency and available bandwidth aspects. We expect MARS will be beneficial for both information and transportation communities within the smart city.

Recently, cell-free (CF) architectures, in which every user can potentially communicate with every base station, have received a lot of attention. This paper considers the uplink of fully and partially centralized CF networks where unmanned aerial vehicles serve as flying base stations (FBSs). A subset of FBSs participates in the reception of each user and a subset of users is received by each FBS. Deterministic equivalent expressions, exact asymptotically in the subset sizes and approximate for finite dimensions thereof, are derived for the spectral efficiency under Rician fading. Capitalizing on these expressions, the FBS deployment problem is investigated for different receiver architectures. The nonconvex deployment problem, tackled through a combination of gradient-based and Gibbs sampling algorithms, results in a superior performance with respect to a square grid deployment; this superiority extends to the minimum and aggregate spectral efficiency for both fully and partially centralized cell-free networks. This work was supported in part by NSF Award CCF-1815339, in part by European Research Council (ERC) under Grant 694975, in part by the ICREA Academia Program, and in part by the Fractus Chair on Tech Transfer and 6G.

The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources.

The malicious attacks in the scale-free Internet of Things (IoT) networks create a serious threat for the functionality of nodes. During the malicious attacks, the removal of high degree nodes greatly affects the connectivity of the remaining nodes in the networks. Therefore, ensuring the maximum connectivity among the nodes is an important part of the topology optimization. A good scale-free network has the ability to maintain the functionality of the nodes even if some of them are removed from the network. Thus, designing a robust network to support the nodes’ functionality is the aim of topology optimization in the scale-free networks. Moreover, the computational complexity of an optimization process increases the cost of the network. Therefore, in this paper, the main objective is to reduce the computational cost of the network with the aim of constructing a robust network topology. Thus, four solutions are presented to reduce the computational cost of the network. First, a Smart Edge Swap Mechanism (SESM) is proposed to overcome the excessive randomness of the standard Random Edge Swap Mechanism (RESM). Second, a threshold based node removal method is introduced to reduce the operation of the edge swap mechanism when an objective function converges at a point. Third, multiple attacks are performed in the network to find the correlation between the measures, which are degree, betweenness and closeness centralities. Fourth, based on the third solution, a Heat Map Centrality (HMC) is used that finds the set of most important nodes from the network. The HMC damages the network by utilizing the information of two positively correlated measures. It helps to provide a good attack strategy for robust optimization. The simulation results demonstrate the efficacy of the proposed SESM mechanism. It outperforms the existing RESM mechanism by almost 4% better network robustness and 10% less number of swaps. Moreover, 64% removal of nodes helps to reduce the computational cost of the network.

This letter proposes a sensing-assisted uplink communications framework between a single-antenna user and a full-duplex (FD) base station (BS) against an aerial eavesdropper (AE). To protect the information from being overheard, the BS transmits radar signals to localize and jam AE while receiving uplink signals. The radar signal transmission is divided into detection phase and tracking phase. In detection phase, the BS synthesizes a wide beam to localize the AE under the secrecy rate constraint; while in tracking phase, the BS maximizes the signal-to-interference-plus-noise ratio (SINR) of its received signals under the AE’s SINR constraint while guaranteeing a predefined radar echo signal signal-to-noise ratio (SNR) level. To deal with the self interference, we jointly optimize the radar waveform and receive beamforming vector. An alternating optimization algorithm and a successive convex approximation (SCA) based algorithm are proposed to solve the two formulated problems, respectively. Simulation results verify the effectiveness of the proposed algorithms. They also show that the secrecy rate can be significantly improved with the assistance of BS sensing.

Vertically polarized, omnidirectional-and endfire-radiating, bandwidth-enhanced, high efficiency, electrically small monopole filtennas are developed. Both designs are attained by organically combining a coax-fed top-hat loaded monopole with two hybrid quarter-wavelength open-end stubs and a shorting post. The hybrid stubs serve two functions. They produce two radiation nulls at both the lower and upper band edges to sharpen the roll-off rate. Moreover, they facilitate the appearance of an additional resonance without affecting the original resonant mode. The shorting post is introduced to engineer the overlap of two resonant modes to yield an enhanced bandwidth. Prototypes of both filtennas were fabricated, assembled, and tested. The measured results are in good agreement with their simulated values, validating their efficacy. The omnidirectional filtenna has an electrically small size (ka = 0.73), a fractional bandwidth (FBW) of 11.3%, and a maximum realized gain of 3.01 dBi. The endfire-radiating filtenna has an electrically small size (ka = 0.93), an operating FBW of 12.8%, and a peak realized gain of 6.09 dBi. Both filtennas exhibit a flat realized gain response and high radiation efficiencies over their entire passbands, as well as very good selectivity with high out-of-band suppression levels.

Unlike theoretical distributed learning (DL), DL over wireless edge networks faces the inherent dynamics/uncertainty of wireless connections and edge nodes, making DL less efficient or even inapplicable under the highly dynamic wireless edge networks (e.g., using mmW interfaces). This article addresses these problems by leveraging recent advances in coded computing and the deep dueling neural network architecture. By introducing coded structures/redundancy, a distributed learning task can be completed without waiting for straggling nodes. Unlike conventional coded computing that only optimizes the code structure, coded distributed learning over the wireless edge also requires to optimize the selection/scheduling of wireless edge nodes with heterogeneous connections, computing capability, and straggling effects. However, even neglecting the aforementioned dynamics/uncertainty, the resulting joint optimization of coding and scheduling to minimize the distributed learning time turns out to be NP-hard. To tackle this and to account for the dynamics and uncertainty of wireless connections and edge nodes, we reformulate the problem as a Markov Decision Process and then design a novel deep reinforcement learning algorithm that employs the deep dueling neural network architecture to find the jointly optimal coding scheme and the best set of edge nodes for different learning tasks without explicit information about the wireless environment and edge nodes' straggling parameters. Simulations show that the proposed framework reduces the average learning delay in wireless edge computing up to 66% compared with other DL approaches. The jointly optimal framework in this article is also applicable to any distributed learning scheme with heterogeneous and uncertain computing nodes.

A planar single-layer parasitic swastika-shaped structure, which serves as an effective isolator, is developed for mutual coupling reduction in wideband, high-density circularly polarized (CP) arrays. A closely spaced two-element CP antenna array demonstrates the efficacy of the approach. The currents induced by the CP coupling are effectively canceled out when this isolator is integrated into it. The antenna elements are co-planar. They are arranged with a center-to-center distance of 0.4λ L (λL) being the wavelength of the lower bound of the array's operational bandwidth) and have an extremely small edge-to-edge distance of ∼ 0.024λ L. The parasitic element yields an increase of the isolation level between the antenna elements of more than 9.4 dB over the entire operational band, with a maximum improvement of 20.4 dB. Moreover, the antenna elements realize a modest maximum gain improvement of ∼ 1.2 dB in the boresight direction over the same band. The overlapped impedance matching and 3 dB axial ratio (AR) bandwidths cover the range of 1.7-2.7 GHz, a ∼ 45% fractional bandwidth. A prototype was fabricated and measured; the measured results are in good agreement with their simulated values.

Ubiquitous sensor-rich smartphones have promoted MCS, an emerging people-centric sensing paradigm for urban IoT. However, using cellular networks to transmit the big data collected by MCS would incur expensive financial costs for both participating phone users and the MCS organizer. A promising solution to this is to integrate the low-cost D2D communication into the MCS design. However, using D2D in MCS will require cooperative interactions among self-interested and strategic participating phone users, which significantly complicates the "human-in-the-loop" MCS design in both the digital and human dimensions, and brings a branch of new challenges: data communication and networking become more complex; D2D connections among opportunistically encountered phone users must be secure, fast, and user-transparent; and participating phone users need to be properly incentivized. In dealing with these challenges, this article covers recent developments of D2D-enabled MCS from both the theoretical and practical perspectives. Future research questions in this rapidly growing field are also discussed.

In this paper, we employ multiple wireless-powered relays to assist information transmission from a multi-antenna access point to a single-antenna receiver. The wireless relays can operate in either the passive mode via backscatter communications or the active mode via RF communications, depending on their channel conditions and energy states. We aim to maximize the overall throughput by jointly optimizing the access point's beamforming and the relays' radio modes and operating parameters. Due to the non-convex and combinatorial structure, we develop a novel optimization-driven hierarchical deep deterministic policy gradient (H-DDPG) approach to adapt the beamforming and relay strategies dynamically. The optimization-driven H-DDPG algorithm firstly decomposes the binary relay mode selection into the outer-loop deep Q-network (DQN) algorithm and then optimizes the continuous beamforming and relaying parameters by using the inner-loop DDPG algorithm. Secondly, to improve the learning efficiency, we integrate the model-based optimization into the DDPG framework by providing a better-informed target estimation for DNN training. Simulation results reveal that these two special designs ensure a more stable learning and achieve a higher reward performance, up to nearly 20%, compared to the conventional DDPG approach., Comment: 29 pages, 7 figures, 2 tables, submitted to IEEE Transactions on Wireless Communications

Efficient techniques are developed for completing unbalanced and sparse low-order tensors, which cannot be effectively completed by popular matrix-rank optimization basedtechniques such as compressed sensing and/or the l_q-matrixmetric. We use our previously developed 2D-index encoding technique for tensor augmentation in order to represent these incomplete low-order tensors by high-order but low-dimensional tensors with their modes building up a coarse-grained hierachy of correlations among the incomplete tensor entries. The concept of tensor-trains is then exploited for decomposing these augmented tensors into trains of balanced and sparse matrices for efficient completion. More explicitly, we develop powerful algorithms exhibiting an excellent performance vs. complexity trade-off, which are supported by numerical examples by relying on matrix data and third-order tensor data constituted by color images.

A continuous beam-steering solution for circularly polarized (CP) radial-line slot array (RLSA) antennas is presented and demonstrated with a fabricated prototype. The solution is based on the Near-Field Meta-Steering (NFMS) method, which is implemented through a pair of highly transmitting hybrid metasurfaces (HMs) that are placed above a high-gain CP RLSA antenna in the near-field region. Cells in HMs, unlike conventional metasurfaces (CMs), can provide exact or close to exact phase shift with transmission magnitude larger than -1 dB. This is achieved by combining the non-overlapping phase ranges of two dramatically different phase-shifting cells. The RLSA is stationary while the two HMs are rotated to steer the beam in two-dimensional (azimuth and elevation) space. The measured results of the prototype demonstrate that the system can steer its beam to a maximum elevation angle of 40.6o with a maximum gain of 30.9 dBic. The axial ratio remained less than 3 dB in the 3dB beamwidth even when the beam is steered. The height of the system is only 4.5 cm, which is much less than mechanically steered reflector antennas. Unlike electronically steered high-gain antenna arrays, this system requires only fewWatts of power only when rotating HMs and has zero power consumption when the beam is stationary.

Clothing changes and lack of data labels are both crucial challenges in person ReID. For the former challenge, people may occur multiple times at different locations wearing different clothing. However, most of the current person ReID research works focus on the benchmarks in which a person's clothing is kept the same all the time. For the last challenge, some researchers try to make model learn information from a labeled dataset as a source to an unlabeled dataset. Whereas purely unsupervised training is less used. In this paper, we aim to solve both problems at the same time. We design a novel unsupervised model, Sync-Person-Cloud ReID, to solve the unsupervised clothing change person ReID problem. We developer a purely unsupervised clothing change person ReID pipeline with person sync augmentation operation and same person feature restriction. The person sync augmentation is to supply additional same person resources. These same person's resources can be used as part supervised input by same person feature restriction. The extensive experiments on clothing change ReID datasets show the out-performance of our methods., Comment: 9 pages

High-gain single-feed antennas are desired for current and future wireless systems. Slot-based highly overmoded cavity antennas are one solution, but the fact that they can only radiate broadside beams limits their applications. An innovative phase-control technique is developed for slot-based resonant cavity antennas that utilizes a planar array of closely-spaced, appropriately-sized metallic patches integrated into the very near field of their slot arrays. It facilitates the design and realization of the phase distribution over their radiating aperture that is required for them to generate single and/or multiple tilted beams pointed at specified directions. A TE(10)(11)(0)-mode slot-based cavity antenna is selected as the pathfinder system. A TE110-mode single-slot cavity antenna that is equivalent to the half-wavelength subsection of that cavity is introduced initially to monitor the phase radiated from each slot. The phase control surface is then carefully engineered to facilitate tilted output beams from the base design. The approach is validated with two subsequent tilted-beam systems. The first system achieves a single beam tilted to 30° with respect to the broadside direction. The second system is extended to produce two independently-directed titled beams pointing at –10° and 20°, respectively. All of the measured results agree well with their simulated values.

In this paper, we propose a general framework to provide a desirable trade-off between inference accuracy and privacy protection in the inference as service scenario (IAS). Instead of sending data directly to the server, the user will preprocess the data through a privacy-preserving mapping, which will increase privacy protection but reduce inference accuracy. To properly address the trade-off between privacy protection and inference accuracy, we formulate an optimization problem to find the privacy-preserving mapping. Even though the problem is non-convex in general, we characterize nice structures of the problem and develop an iterative algorithm to find the desired privacy-preserving mapping, with convergence analysis provided under certain assumptions. From numerical examples, we observe that the proposed method has better performance than gradient ascent method in the convergence speed, solution quality and algorithm stability.