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Surface wave-assisted wireless communication systems have recently emerged as a promising complementary solution for creating a smart radio environment, particularly in the context of beyond-fifth generation (5G) and sixth generation (6G) networks. Unlike traditional approaches that rely solely on space waves or use passive elements on a large surface to reflect space waves, the incorporation of surface waves can effectively address challenges such as blockage and severe path loss, which are prevalent at extremely high frequencies, including millimeter wave (mmWave) and terahertz (THz) bands. Recent advances in metasurface properties have enabled the propagation of electromagnetic waves on a surface without re-radiation. Motivated by these developments, in this paper, we develop a path loss model and two surface impedance models for surface wave-assisted wireless communication systems. We demonstrate that the signal-to-noise ratio (SNR) of this system can be significantly higher than that of traditional wireless communication systems because the path loss for a surface wave is proportional to the propagation distance, whereas the path loss for a space wave is proportional to the square of the propagation distance. Furthermore, we explore how the metasurface should be designed in terms of its materials and structures, considering that the incident space wave must be aligned with Brewster’s angle to excite a surface wave.

Abstract This paper introduces a novel reconfigurable technique for partitioning the propagation of surface waves by utilizing a T-shaped structure and pathways established through the introduction of fluid metal or metal pins into evenly spaced cylindrical cavities within a porous surface wave platform. Notably, the co-printing of metal and dielectric materials via 3D printing is employed, resulting in an expedited fabrication process. Extensive 3D electromagnetic simulations and experimental investigations validate the proposed approach’s efficacy in achieving surface wave division while minimizing interference. The study encompasses an exploration of diverse power distribution ratios achievable within the distributed surface waves. Critical physical parameters of the T-junction are comprehensively examined, including partition depth, junction geometry, output port symmetry, and asymmetry. Additionally, the research delves into the frequency-dependent behaviours of asymmetric T-junctions and pathways. These findings establish the groundwork for adaptable architectures, facilitating concurrent communication among multiple devices within a unified surface wave communication network. This innovation holds potential to enhance various applications through improved communication capabilities.

Limited power and computational resources make the employment of complex classical encryption schemes unrealistic in resource-limited networks, e.g., the Internet of Things (IoT). To this end, physical layer security (PLS) has shown great potential in securing such resource-limited networks. To further combat the power scarcity in IoT nodes, radio frequency (RF) based energy harvesting (EH) is an attractive energy source while relaying can enhance the energy efficiency and extend the range of data transmission. Additionally, due to deploying low-cost hardware, imperfections in the RF chain of IoT transceivers are common. Against this background, in this paper, we investigate an untrusted EH relay-aided secure communication with RF impairments. Specifically, the relay simultaneously receives the desired signal from the source and the jamming from the destination in the first phase. Hence the relay is unable to extract the confidential desired signal. The resultant composite signal is then amplified by the relay in the second phase by using the energy harvested from the composite signal followed by its transmission to the destination. Since the destination is the original source of the jamming, its effect can be readily subtracted from the composite signal to recover the original desired signal of the source. Moreover, in the face of hardware impairments (HWIs) in all nodes, maintaining optimal power management both at the source and destination may impose excessive computations on an IoT node. We solve this problem by deep learning (DL) based optimal power management maximizing the secrecy rate based on the instantaneous channel coefficients. We show that our learning-based scheme can reach the accuracy of the exhaustive search method despite its considerably lower computational complexity. Moreover, we developed an optimization framework for judiciously sharing HWIs across the nodes, so that we attain the maximum secrecy rate. To derive an efficient solution, we utilize a majorization-minimization (MM)algorithm, which is a particular instance in the family of successive convex approximation (SCA) methods. The simulation results show that the proposed HWI aware design considerably improves the secrecy rate.

Abstract Compact ultra massive array (CUMA) is a new form of the emerging fluid antenna system where a huge number of flexible‐position antennas are selected to produce the output signal. By making sure that the in‐phase channels (similarly for the quadrature channels) of the desired signal at the selected antenna ports align, it builds an advantage of the desired signal over the interference. It is known that CUMA as a multiple access scheme is able to deal with hundreds of users on the same channel use, in the case of rich scattering, if binary phase shift keying is considered. It is nevertheless unclear if higher‐level modulation can bring even greater network rate in this extreme massive connectivity scenario. This letter investigated this situation by presenting the average data rate expression of CUMA when M‐ary amplitude shift keying is used, assuming a binary symmetric channel. Numerical results reveal that M‐ary amplitude shift keying can indeed raise the rate performance considerably.

Future wireless communication is expected to be a paradigm shift from three basic service requirements of 5th Generation (5G) including enhanced Mobile Broadband (eMBB), Ultra Reliable and Low Latency communication (URLLC) and the massive Machine Type Communication (mMTC). Integration of the three heterogeneous services into a single system is a challenging task. The integration includes several design issues including scheduling network resources with various services. Specially, scheduling the URLLC packets with eMBB and mMTC packets need more attention as it is a promising service of 5G and beyond systems. It needs to meet stringent Quality of Service (QoS) requirements and is used in time-critical applications. Thus through understanding of packet scheduling issues in existing system and potential future challenges is necessary. This paper surveys the potential works that addresses the packet scheduling algorithms for 5G and beyond systems in recent years. It provides state of the art review covering three main perspectives such as decentralised, centralised and joint scheduling techniques. The conventional decentralised algorithms are discussed first followed by the centralised algorithms with specific focus on single and multi-connected network perspective. Joint scheduling algorithms are also discussed in details. In order to provide an in-depth understanding of the key scheduling approaches, the performances of some prominent scheduling algorithms are evaluated and analysed. This paper also provides an insight into the potential challenges and future research directions from the scheduling perspective.

Abstract Fluid antenna multiple access (FAMA) is a new way of accommodating a large number of users on a single channel for massive connectivity, with slow FAMA (s‐FAMA) being the practical version for achieving this. The impressive performance is understood to be achievable if the users have independent Rayleigh fading envelopes. With mobile networks vamping up the operating frequencies in 5G and beyond, nevertheless, the channel will have less multipath and become more directional. It is unclear if s‐FAMA still performs well and how its performance is affected by different channel parameters. To address this, this letter first develops a multipath fading channel model capable of modelling a mixture of directional line‐of‐sight (LoS) and non‐LoS paths over the ports of a fluid antenna system. The results indicate that a large number of paths in the channel is essential to the performance and a large Rice factor will degrade the performance rapidly. Also, contrast to the initial belief, the size of the fluid antenna plays a more important role than the number of ports or resolution of the fluid antenna. To restore the gain of s‐FAMA, it is proposed to employ extra‐large multiple‐input multiple‐output (XL‐MIMO) at the base station (BS) to scramble the channel and create artificial multipath so that the users' envelopes can become independent Rayleigh again. The results confirm that XL‐MIMO enabling s‐FAMA is an effective technique for massive connectivity in the directional millimeter‐wave (mmWave) bands.

An intelligent reflecting surface (IRS) is proposed to enhance the physical layer security in the Rician fading channel wherein the angular direction of the eavesdropper (ED) is aligned with a legitimate user. A two-phase communication system under active attacks and passive eavesdropping is considered in this scenario. The base station avoids direct transmission to the attacked user in the first phase, whereas other users cooperate in forwarding signals to the attacked user in the second phase, with the help of IRS and energy harvesting technology. Under the occurrence of active attacks, an outage-constrained beamforming design problem is investigated under the statistical cascaded channel error model, which is solved by using the Bernstein-type inequality. An average secrecy rate maximization problem for the passive eavesdropping is formulated, which is then addressed by a low-complexity algorithm. The numerical results of this study reveal that the negative effect of the ED’s channel error is larger than that of the legitimate user.

Standard machine-learning approaches involve the centralization of training data in a data center, where centralized machine-learning algorithms can be applied for data analysis and inference. However, due to privacy restrictions and limited communication resources in wireless networks, it is often undesirable or impractical for the devices to transmit data to parameter sever. One approach to mitigate these problems is federated learning (FL), which enables the devices to train a common machine learning model without data sharing and transmission. This paper provides a comprehensive overview of FL applications for envisioned sixth generation (6G) wireless networks. In particular, the essential requirements for applying FL to wireless communications are first described. Then potential FL applications in wireless communications are detailed. The main problems and challenges associated with such applications are discussed. Finally, a comprehensive FL implementation for wireless communications is described.

The Internet-of-Things (IoT) is an emerging technology that connects and integrates a massive number of smart physical devices with virtual objects operating in diverse platforms through the internet. Due to massive size and physical spread of many applications such as smart healthcare, IoT is increasingly implemented in distributed setting. This distributed nature of implementation of the entities connected to the IoT networks are exposed to an unprecedented level of privacy and security threats. This is particularly severe for IoT healthcare system as it involves huge volume of sensitive and personal data. Although blockchain has posed to be the solution in this scenario thanks to its inherent distributed ledger technology (DLT), it suffers from a major drawback of rapidly increasing storage and computation requirements with the increase in network size which makes its implementation impractical. This paper proposes a holochain-based security and privacy-preserving framework for IoT healthcare systems that overcomes the scalability challenge and is particularly suited for resource constrained IoT scenarios. Through thorough analysis and performance results, we have demonstrated that the holochain based IoT healthcare solution outperforms blockchain based solution in terms of resource requirements while ensuring appropriate level of privacy and security.

Reliable wireless communication networks are a significant but challenging mission for post-disaster areas and hotspots in the era of information. However, with the maturity of unmanned aerial vehicle (UAV) technology, drone mobile networks have attracted considerable attention as a prominent solution for facilitating critical communications. This paper provides a system-level analysis for drone mobile networks on a finite three-dimensional (3D) space. Our aim is to explore the fundamental performance limits of drone mobile networks taking into account practical considerations. Most existing works on mobile drone networks use simplified mobility models (e.g., fixed height), but the movement of the drones in practice is significantly more complicated, which leads to difficulties in analyzing the performance of the drone mobile networks. Hence, to tackle this problem, we propose a stochastic geometry-based framework with a number of different mobility models including a random Brownian motion approach. The proposed framework allows to circumvent the extremely complex reality model and obtain upper and lower performance bounds for drone networks in practice. Also, we explicitly consider certain constraints, such as the small-scale fading characteristics relying on line-of-sight (LOS) and non line-of-sight (NLOS) propagation, and multi-antenna operations. The validity of the mathematical findings is verified via Monte-Carlo (MC) simulations for various network settings. In addition, the results reveal some design guidelines and important trends for the practical deployment of drone networks.

Dynamic frequency allocation (DFA) with massive multiple-input multiple-output (MIMO) is a promising candidate for multicell communications where massive MIMO is adopted to maximize the per-cell capacity whereas the inter-cell interference (ICI) is tackled by DFA. Realizing this approach in a distributed fashion is however very difficult due to the lack of global channel state available at the base stations (BSs) in the cell level. We utilize a forward-looking game to automate reconciliation for DFA in a distributed manner between cells while zero-forcing (ZF) is used at each cell to maximize the multiplexing gain. To maximize the network capacity, multi-agent deep reinforcement learning (DRL) using offline centralized training is leveraged to train the BSs to master their game-theoretic reconciliation strategies. The result is a trained neural network for each BS, empowering it with rich experience of reconciliation with other BSs for converging to a network-efficient equilibrium. The online algorithm is distributed with the BSs competing as expert players to start the negotiation process using their trained actions. Simulation results show that the proposed synergized deep-learning game-theoretic algorithm outperforms significantly the DRL-only and game-theoretic only methods, and other benchmarks for multicell MIMO.

While 5G is tasked to transform our lives for the better over the next 10 years, next-generation mobile communications, a.k.a. 6G, will undoubtedly demand even higher energy and spectral efficiencies capable of providing myriads of new services and experience to users everywhere they go. Although our technologies do evolve from one generation to the next, the root of the ambition in mobile communications has always been to ensure reliable performance from an uncertain, fluctuating medium. The previous generations have already seen numerous technologies such as advanced coding and signal processing, resource allocation, and most famously, multiple-input multiple-output to redeem some stability from the wireless medium. Inevitably, 6G will be built upon further disruptive technologies that enable another cycle of revolution. In this article, we examine one emerging technology, referred to as fluid antenna system that represents any software-controllable fluidic, conductive, or dielectric structure that can alter its shape and position to reconfigure the gain, radiation pattern, operating frequency, and other characteristics. Fluid antenna takes inspiration from Bruce Lee's Jeet Kune Do to innovate mobile communication systems design. In Bruce Lee's philosophy, one can imitate water to adapt combat style, whereas fluid antenna exploits the dynamic nature of fluids or switchable pixels to achieve ultimate flexibility for diversity and multiplexing benefits that have been unseen before in mobile devices, and the implication can be transformative. This article discusses the potential of fluid antenna systems for 6G, and in particular, we introduce six research topics in fluid antenna systems that if solved successfully could revolutionize mobile communications network design and optimization. This article intends to stimulate discussion that will help shape the development of 6G technologies.

This paper investigates joint unmanned aerial vehicle (UAV) trajectory planning and time resource allocation for minimum throughput maximization in a multiple UAV-enabled wireless powered communication network (WPCN). In particular, the UAVs perform as base stations (BS) to broadcast energy signals in the downlink to charge IoT devices, while the IoT devices send their independent information in the uplink by utilizing the collected energy. The formulated throughput optimization problem which involves joint optimization of 3D path design and channel resource assignment with the constraint of flight speed of UAVs and uplink transmit power of IoT devices, is not convex and thus is extremely difficult to solve directly. We take advantage of the multi-agent deep Q learning (DQL) strategy and propose a novel algorithm to tackle this problem. Simulation results indicate that the proposed DQL-based algorithm significantly improve performance gain in terms of minimum throughput maximization compared with the conventional WPCN scheme.

Ultra-dense networks (UDNs) are widely considered as an effective solution to greatly improve coverage by shortening the communication distance between user equipments (UEs) and base stations (BSs). The reality of UDN is that line-of-sight (LOS) communication becomes more likely to occur but this desirable result also complicates the performance analysis of random UDNs and puts an obstacle on the design and optimization of UDNs. The aim of this paper is to derive analytical results that take into account the phenomenon of having mixed LOS and non line-of-sight (NLOS) links in UDNs. In particular, the use of an arbitrary shaped thinning process to model the LOS wireless links allowed us to investigate a wide set of scenarios for what concerns the desired and interfering power levels. Our contribution is an accurate approximation in closed form for the success content delivery probability (SCDP) that decouples the contribution from LOS and NLOS links. Simulation results corroborate the accuracy of the proposed approximation.

Cognitive radio is more than just radio environment awareness, and more importantly, has the ability to interact with the environment in the best way possible. Ideally, cognitive radios will form a self-regulating society of mobile radios achieving maximum spectrum utilization. However, challenges arise as mobile radios tend to compete with one another for spectrum, generating harmful interference, and damaging performance individually and for the network as a whole. In this paper, we present a framework that allows competing radios to teach and learn from each other’s action so that a desirable equilibrium can be reached. The heart of cognition to establish this is the forward-looking ability, which enables competing radios to see beyond the present time, negotiate and optimize their actions toward a more agreeable equilibrium. Technically speaking, we adopt a belief-directed game where each mobile radio, regarded as player, formulates a belief function to project how the radio environment as a whole would respond to any of its action. This model facilitates engineering of the equilibrium by different choices of the players’ belief functions. Under this model, players will negotiate naturally through a sequence of calculated competition (i.e., cycles of teaching and learning with each other). We apply this methodology to a cognitive orthogonal frequency-division multiple-access radio network where mobile users are free to access any of the subcarriers and thus compete for radio resources to maximize their rates. The results reveal that the proposed negotiation-by-forward-looking competition mechanism guides users to converge to an equilibrium that benefits not only the individual users but the entire network approaching the maximum achievable sum-rate.

In this paper, we establish a cryptographic primitive for wireless communications. An asymmetric physical layer encryption (PLE) scheme based on elliptic curve cryptography is proposed. Compared with the conventional symmetric PLE, asymmetric PLE avoids the need of key distribution on a private channel, and it has more tools available for processing complex-domain signals to confuse possible eavesdroppers when compared with upper-layer public key encryption. We use quantized information entropy to measure the constellation confusion degree. The numerical results show that the proposed scheme provides greater confusion to eavesdroppers and yet does not affect the bit error rate (BER) of the intended receiver (the information entropy of the constellation increases to 17.5 for 9-bit quantization length). The scheme also has low latency and complexity [O(N2.37), where N is a fixed block size], which is particularly attractive for implementation.

A low-cost 3D-printed frequency agile fluidic monopole antenna system is demonstrated to respond to the increasing demand for reconfigurable antennas, which can operate in a dynamic environment, in this paper. Antennas that can be reconfigured for different operating frequencies, polarizations, or radiation patterns are attracting attention. Traditional reconfigurable antennas using a metallic radiating element with electronic switches are limited by their pre-defined physical geometries. As conductive fluid, either liquid metal or ionized fluid has no defined shape, so it is possible to create the desired shape of a fluidic antenna to support different wireless environments. The fabrication of the leakage-free containers for fluidic antennas needs special consideration, and stereo-lithography-based 3D-printing technology is a possible option to support the fabrication. Moreover, researchers will have higher design freedom and accuracy to create new container shapes for fluidic antennas. The fluidic monopole antenna proposed is coupling-fed by a ring geometry for separating the electrical and mechanical structures; such an approach enables individual optimization and minimizes mutual disturbances in the system. A parametric study of the proposed coupling-feed geometry and the experimental verification of the antenna prototypes have been performed. Reasonable frequency agility from 3.2 to 5 GHz has been demonstrated, and the peak efficiency is about 80%. A maximum gain of 3.8 dBi is obtained. The radiation patterns of the antenna are stable across the operating bandwidth. The proposed antenna could be useful for the applications in the recent 5G mid-bands operations.

In this paper, we investigate the physical layer security in downlink of Power Domain Non-Orthogonal Multiple Access (PD-NOMA)-based heterogeneous cellular network (HetNet). In this paper, we assume two categories of users are available: 1) Trusted users and 2) untrusted users (eavesdroppers) at which transparency of users is not clear for the BSs, i.e., they are potential eavesdroppers. Our aim is to maximize the sum secrecy rate of the network. To this end, we formulate joint subcarrier and power allocation optimization problems to increase sum secrecy rate. Moreover, we propose a novel scheme at which the eavesdroppers are prevented from doing Successive Interference Cancellation (SIC), while legitimate users are able to do it. In practical systems, perfectly availability of all eavesdroppers' Channel State Information (CSI) at legitimate transmitters are impractical. Also CSIs of legitimate users may be also imperfect due to the error of channel estimation. Hence, we study two cases of CSI availability: 1) Perfect CSI of nodes (legitimate users and eavesdroppers) are available at the BSs and 2) imperfect CSI of nodes are available at the BSs. Since the proposed optimization problems are non-convex, we adopt the well-known iterative algorithm called Alternative Search Method (ASM). In this algorithm, the optimization problems are converted to two subproblems, power allocation and subcarrier allocation. We solve the power allocation problem by the Successive Convex Approximation approach and solve the subcarrier allocation subproblem, by exploiting the Mesh Adaptive Direct Search algorithm (MADS). Moreover, in order to study the optimality gap of the proposed solution method, we apply the monotonic optimization method. Moreover, we evaluate the proposed scheme for secure massive connectivity in Heterogeneous Ultra Dense Networks (HUDNs). Furthermore, we investigate multiple antennas base stations scenario in this literature. Finally, we numerically compare the proposed scheme with the conventional case at which the eavesdroppers are able to apply SIC. Numerical results highlight that the proposed scheme significantly improves the sum secrecy rate compared with the conventional case.

In this paper, we study secrecy simultaneous wireless information and power transfer (SWIPT) in downlink non-orthogonal multiple access (NOMA) systems comprising a base station (BS), multiple information receivers (IRs), and multiple energy receivers (ERs) that have potential to wiretap the IRs. The goal of this paper is to maximize the sum secrecy rate (SSR) of the system subject to the individual IR's minimum data rate requirement and the individual ER's minimum harvested energy requirement. The corresponding problem involves resource allocation via max-min function, which is non-convex and difficult to solve directly. In order to tackle this, we first transform the original non-convex problem to a sequence of convex sub-problems which can be solved simultaneously. Hence, a closed-form solution of the optimal power allocation policy is derived based on the Karush-Kuhn-Tucker conditions. Numerical results validate the theoretical findings and demonstrate that the significant performance gain over the orthogonal multiple access scheme in terms of SSR can be achieved by the proposed algorithm in a SWIPT-enabled NOMA system.

This paper investigates the physical layer security in heterogeneous networks (HetNets) supported by simultaneous wireless information and power transfer (SWIPT). We first consider a two-tier HetNet composed of a macrocell and several femtocells, where the macrocell base station (BS) serves multiple users in the presence of a malicious eavesdropper, while each femtocell BS serves a couple of Internet-of-Things (IoT) users. With regard to the energy constraint of IoT users, SWIPT is performed at the femtocell BSs, and IoT users accomplish the reception of information and energy in a time-switching manner, where information secrecy is to be protected. To enhance the secrecy performance, we inject artificial noise (AN) into the transmit beam at both macrocell and femtocell BSs, and for the sake of achieving green communications, we formulate the problem of maximizing secrecy energy efficiency while considering the fairness in a cross-tier multi-cell coordinated beamforming (MCBF) design. To handle this resulting nonconvex max-min fractional program problem, we propose an iterative algorithm by applying successive convex approximation method. Then, we further develop a decentralized solution based on alternative direction multiplier method (ADMM), which reduces the overhead of information exchange among coordinated BSs and achieves good approximation performance. Finally, simulation results demonstrate the performance of the proposed AN-aided cross-tier MCBF design and verify the validity of distributed ADMM-based approach.

Abstract In this paper, we investigate the ergodic rate for a device-to-device (D2D) communication system aided by a two-way decode-and-forward (DF) relay node. We first derive closed-form expressions for the ergodic rate of the D2D link under asymmetric and symmetric cases, respectively. We subsequently discuss two special scenarios including weak interference case and high signal-to-noise ratio case. Then we derive the tight approximations for each of the considered scenarios. Assuming that each transmitter only has access to its own statistical channel state information (CSI), we further derive closed-form power allocation strategy to improve the system performance according to the analytical results of the ergodic rate. Furthermore, some insights are provided for the power allocation strategy based on the analytical results. The strategies are easy to compute and require to know only the channel statistics. Numerical results show the accuracy of the analysis results under various conditions and test the availability of the power allocation strategy.

Dense cellular networks (DenseNets) are fast becoming a reality with the large scale deployment of base stations aimed at meeting the explosive data traffic demand. In legacy systems, however, this comes at the cost of higher network interference and energy consumption. In order to support network densification in a sustainable manner, the system behavior should be made “load-proportional” thus allowing certain portions of the network to activate on-demand. In this paper, we develop an analytical framework using tools from stochastic geometry theory for the performance analysis of DenseNets where load-awareness is explicitly embedded in the design. The proposed model leverages on a flexible cellular network architecture where there is a complete separation of the data and signaling communications functionalities. Using this stochastic geometric framework, we identify the most energy-efficient deployment solution for meeting certain minimum service criteria and analyze the corresponding power savings through dynamic sleep modes. According to state-of-the-art system parameters, a homogeneous pico deployment for the data plane with a separate layer of signaling macro-cells is revealed to be the most energy-efficient solution in future dense urban environments.

Distributed antenna systems (DASs) have been widely implemented in the state-of-the-art cellular communication systems to cover dead spots. Recent studies have also indicated that DAS has advantages in wireless energy transfer (WET). In this paper, we study simultaneous wireless information and power transfer for a multiple-input single-output DAS in the downlink, which consists of arbitrarily distributed remote antenna units (RAUs). In order to save the energy cost, we adopt the energy cooperation of energy harvesting (EH) and two-way energy flows to let the RAUs trade their harvested energy through the smart grid network. Under individual EH constraints, per-RAU power constraints, and various smart grid considerations, we investigate a power management strategy that determines how to utilize the stochastically spatially distributed harvested energy at the RAUs and how to trade the energy with the smart grid simultaneously to supply maximum wireless information transfer (WIT) with a minimum WET constraint for a receiver adopting power splitting. Our analysis shows that the optimal design can be achieved in two steps. The first step is to maximize a new objective that can simultaneously maximize both WET and WIT, considering both the smart grid profitable and smart grid neutral cases. For the grid-profitable case, we derive the optimal full power strategy and provide a closed-form result to see under what condition this strategy is used. On the other hand, for the grid-neutral case, we illustrate that the optimal power policy has a double-threshold structure and present an optimal allocation strategy. The second step is then to solve the whole problem by obtaining the splitting power ratio based on the minimum WET constraint. Simulation results are provided to evaluate the performance under various settings and characterize the double-threshold structure.

Interference alignment (IA) is well understood to approach the capacity of interference channels, and believed to be crucial in cellular networks in which the ability to control and exploit interference is key. However, the achievable performance of IA in cellular networks depends on the quality of channel state information (CSI) and how effective IA is in practical settings is not known. This paper studies the use of IA to mitigate inter-cell interference of cellular networks under imperfect CSI conditions. Our analysis is based on stochastic geometry where the structure of the base station (BS) locations is considered by a Poisson point process (PPP). Our main contribution is the coverage probability of the network and simulation results confirm the accuracy.

Use of clinical pathways in the rehabilitation of total knee and total hip replacement patients is on an increasing trend. However, in the literatures there was no universally agreed assessment and auditing method on the outcome of these clinical pathways. We created a new multi-disciplinary rehabilitation outcome checklist (MROC) to evaluate and audit the outcome of our patients after total joint replacement. MROC was applied in 96 total knee and 18 total hip replacement patients. The compliance rate of MROC was 100%. Most of the treatment goals of the clinical pathways were found to be fulfilled. The MROC provides an easy-to-use, free-of-charge, and tailor-made checklist for the evaluation and auditing purpose.

Correction Unfortunately, the original version of this article [1] contained an error. The affiliation of Yiyang Ni and Yuxi Wang was incorrect. The correct affiliation is Jiangsu Second Normal University and is presented in this correction.

The problems of multiple-input multiple-output (MIMO) radar adaptive waveform design in additive white Gaussian noise channels and multitarget recognition based on sequential likelihood ratio test are jointly addressed in this paper. Two information-theoretic waveform design strategies, namely, the optimal waveform for maximizing the mutual information (MI) between the extended target impulse response and the target echoes and the optimal waveform for maximizing the Kullback-Leibler (KL) divergence (or relative entropy), are applied in the multitarget recognition application. For multitarget case, two adaptive waveform design methods for all possible targets based on the current knowledge of each hypothesis are proposed. Method 1 is the probability weighted waveform method. Method 2 is the probability weighted target signature method. The optimal waveform is transmitted and adaptively changed such that a decision is made based on the likelihood ratio after several illuminations. Numerical results demonstrate that the best waveform is the KL divergence-based optimal waveform using Method 1 as it has the lowest average illumination number and the highest correct decision rate for target recognition. By optimally designing and adaptively changing the transmitted waveform, the average number of illuminations required for multitarget recognition can be much reduced.