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In this paper, secure wireless information and power transfer with intelligent reflecting surface (IRS) is proposed for a multiple-input single-output (MISO) system. Under the secrecy rate (SR) and the reflecting phase shifts of IRS constraints, the secure transmit beamforming at access point (AP) and phase shifts at IRS are jointly optimized to maximize the harvested power of energy harvesting receiver (EHR). Due to the non-convexity of optimization problem and coupled optimization variables, firstly, we convert the optimization problem into a semidefinite relaxation (SDR) problem and a sub-optimal solution is achieved. To reduce the high-complexity of the proposed SDR method, a low-complexity successive convex approximation (SCA) technique is proposed. Simulation results show the power harvested by the proposed SDR and SCA methods approximately double that of the existing method without IRS given the same SR. In particular, the proposed SCA achieves almost the same performance as the proposed SDR but with a much lower complexity.

Technically, the security performance of generalized spatial modulation (GenSM) networks can be enhanced by dynamically adjusting the precoder allocated to the legitimate signal as communication channel varies. For this purpose, our paper proposes a secure transmission strategy upon designing both digital and analog precoders for hybrid GenSM systems, where an eavesdropper is taken into account. The concept of the hybrid GenSM system has arose to improve the spatial multiplexing (SMX) gain for remedying the shortcoming of the limited number of radio frequency chains in traditional GenSM systems. However, this may lead to a great deal of security degradation since the SMX gain of the unintended receiver will be also improved. To this end, we develop a secrecy enhancement scheme by devising both analog and digital precoders for hybrid GenSM networks. Specifically, we derive an efficiently closed-form alternative to the original secrecy rate (SR) expression for reducing the excessive computational complexity of the joint optimization problem over the hybrid precoder. Then, by using this alternative as our cost function, an iterative algorithm is proposed. In particular, we elaborately conceive a pair of concave maximization problems in order to optimize the digital and analog precoders, respectively. Our proposed strategy not only utilizes semi-positive definite relaxing technique over the analog precoder but also invokes a lower bound of the alternative to further simplify the optimization over the vectored digital precoder. Subsequently, both the convergence and computational complexity of the proposed alternating iteration algorithm are analyzed. Compared to existing designs, our proposed strategy strikes a compelling role in balancing the SR performance and complexity. Finally, our simulation results confirm the efficiency of the proposed algorithm in terms of the SR performance achieved.

Network-assisted full-duplex (NAFD) distributed massive multiple-input multiple-output (MIMO) systems enable simultaneous uplink and downlink communications by dynamically allocating the numbers of uplink and downlink remote antenna units (RAUs), which potentially improve the spectral efficiency in wireless communications. In such systems, channel state information (CSI) plays a critical role in uplink reception and downlink transmission, as well as the cross link interference cancelation caused by downlink RAUs to uplink RAUs. Moreover, downlink terminals need to estimate CSI to reliably decode the received signals due to the reduced channel hardening effect. However, high training overhead makes it generally impossible to directly estimate CSI. This paper proposes to estimate effective CSI (inner products of beamforming and channel vectors) instead based on beamforming training scheme. Under this scheme, we derive closed-form expressions for uplink and downlink achievable rates with different receivers and beamforming. Given these expressions, we propose an efficient power allocation scheme which is only dependent on slowly varying large-scale fading from the perspective of multi-objective optimization. Numerical results verify the accuracy of the derived closed-form expressions and effectiveness of beamforming training based CSI estimation. Moreover, trade-off regions between the considered optimization objectives under various system parameters offer numerous flexibilities for system optimization.

Mobile edge computing is considered as a promising method to release the computation burden of mobile devices (MDs) by transferring the computation tasks to the nearby edge server. In this paper, we address the computation offloading problem by jointly optimizing offloading-decision making, multi-user multiple input and multiple output (MU-MIMO) precoding and computation resource allocation. The optimization problem is formulated as the minimization of the weighted sum of energy consumption and time delay of MDs, which is a mixed-integer non-linear programming problem. Due to the complexity of offloading time delay, we consider two special cases namely, the lower bound and upper bound of offloading time delay for the original problem, and exploit semidefinite relaxation and rounding methods to obtain the offloading decisions. Specially, we adopt the quadratic transform based fractional programming and the weighted minimum mean square error methods to solve the MU-MIMO precoding design problem for the two cases of offloading time delay, respectively. Simulation results confirm the effectiveness of the proposed method, and show that the application of multi-antenna MU-MIMO communication into MEC can sufficently reduce the energy consumption and time delay during computation offloading.

Fifth generation (5G) mobile systems are expected to be integrated with different radio access methods such as satellite components to provide seamless connectivity and ubiquitous coverage for users worldwide. In this article, we first provide a brief review of current initiatives and system architecture for the convergence of satellite and 5G networks. Then, we investigate various approaches to improve reliability or security at the physical layer for the integrated 5G-satellite networks. An effective achievable rate is presented as the key performance indicator to measure the security and reliability trade-off, followed by the implementation of a proposed beamforming scheme. Finally, we present future trends and challenges in integrated 5G-satellite networks. This work was supported in part by the Key International Cooperation Research Project under Grant 61720106003, and in part by the Shanghai Aerospace Science and Technology Innovation Foundation under Grant SAST2019-095.

Covert communications can hide the very existence of wireless transmissions and thus are able to address privacy issues in numerous applications of the emerging Internet of Things (IoT). In this article, we adopt channel inversion power control (CIPC) to achieve covert communications in Rayleigh fading wireless networks, where a transmitter can possibly hide itself from a warden while transmitting information to a receiver. The CIPC can guarantee a constant signal power at the receiver, which removes the requirement that the receiver has to know the channel state information in order to coherently decode the transmitter’s signal. Specifically, we examine the performance of the achieved covert communications in terms of the effective covert rate (ECR), which quantifies the amount of information that the transmitter can reliably convey to the receiver subject to the warden’s total error probability being no less than some specific value. The noise uncertainty at the warden serves as the enabler of covert communications. For fairness, we also consider noise uncertainty at the legitimate receiver. Our examination shows that increasing the noise uncertainty at the warden and the receiver simultaneously may not continuously improve the ECR achieved in the considered system model.

Since the analysis of cell coverage faces complex environments in unmanned aerial vehicle base station (UAV-BS) systems, general coverage probability in a typical cell is derived to analyse a UAV-BS multi-tier network, and this coverage probability is closely related with power difference among UAV-BSs, two-dimension (2D) and three-dimension (3D) UAV-BS deployment, and general interference fading models. Especially, closed-form coverage probability expressions are also derived in the form of 2D scenario and 3D scenario, when the interference fading coefficient is assumed to follow a Gamma distribution and an exponential distribution, respectively. It shows that there exists maximum spectral efficiency density (SED) as the BS density increases, and such a BS density achieving the max-SED is independent of noise power, but is affected by various propagation parameters.

This letter investigates a multiuser mobile-edge computing (MEC) system with data compression technique to reduce the redundancy of sensed data, save the energy consumption and reduce the latency for wireless transmission. In this regard, the problem of minimizing the total energy consumption by jointly optimizing the transmission bandwidth allocation and data compression ratio is firstly considered under the constraints of latency and limited computation resource. Moreover, Lagragian and iterative-based algorithms are proposed to solve the non-convex optimization problem. Finally, simulation results are shown to verify the efficiency of the proposed algorithms and demonstrate that the application of data compression technique into MEC system can effectively reduce the energy consumption and latency.

This letter investigates streaming coding that places multiple repair packets between source packets to achieve in-order delivery over highly lossy links. While directly analyzing the deterministic placement of repair packets is difficult, a tractable random model is proposed and analyzed to show that a finite in-order delay is achieved. Using it as a guide of the insertion percentage, the delay is then shown to be further reducible by uniformly placing the repair packets, which suppresses the random variation.

In this paper, we investigate energy efficient transmission for a satellite-aerial-terrestrial network (SATN), where a multi-antenna unmanned aerial vehicle (UAV) is employed as a relay to assist the satellite signal delivery. By considering total power constraint (TPC) or per-antenna power constraint (PPC) at the UAV, we first formulate an optimization problem to maximize the energy efficiency of the SATN, which is defined as ratio of the ergodic capacity to the total power consumption for communication at UAV. Then, by jointly exploiting array signal processing with the Dinkelbach’s method, two new beamforming (BF) schemes, namely, TPC-BF and PPC-BF are proposed to solve the non-convex energy efficiency maximization problem. The main advantage of our method is that only angular information-based channel state information is used to obtain BF weight vectors so that a low implementation complexity is achieved. Furthermore, by assuming that the satellite-UAV link undergoes correlated Shadowed-Rician fading while the UAV-terminal link experiences correlated Rician fading, closed-form expressions for the statistics of the equivalent output signal-to-noise ratio are derived and, thus energy efficiency for the considered SATN with BF schemes is analytically presented. Finally, simulation results corroborate the derived expressions and confirm the effectiveness of the proposed BF schemes.

Data dissemination to multiple users within a predetermined deadline is a commonly required function in many emerging wireless scenarios. In this paper, we propose a data dissemination scheme enhanced by device-to-device (D2D) communications and random linear network coding (RLNC), where the broadcast and the D2D links are used simultaneously with on-the-fly RLNC to accelerate the dissemination. The probability distribution of the completion time of using either pure or systematic RLNC is analyzed assuming a finite field size, under a general model where the cooperation may occur probabilistically and multiple packets may be exchanged between the D2D users in each time slot. An optimization problem is formulated to design the cooperation parameters to minimize the expected energy consumption of the system. Numerical and simulation results show that the analysis is accurate, and that to minimize the energy consumption a tradeoff exists between allowing for longer broadcast and for more intensive cooperation. The cooperation parameters designed for multiple D2D pairs are shown to be effective for meeting the reliability requirements.

Power line communication (PLC) technology has created a niche use in the Internet of Things (IoT) by offering flexible and reliable connection among power-driven IoT devices/sensors over existing wired networks. In IoT over PLC networks, massive real-time data generated by the ever-growing connected devices will eventually pose an overwhelming burden on the IoT cloud, which in turn severely degrades the network performance. To cope with these issues, edge computing (EC) has emerged as a complement to cloud computing, aiming at offloading a portion of computing in the cloud to the network edges closer to the IoT devices. However, confronting a practical scenario that some electrical devices cannot communicate with wireless and mobile networks directly, existing EC paradigms may not be directly applied to IoT over PLC networks. In this paper, we propose a novel EC-IoT over PLC paradigm to reduce the transmission latency while migrating a portion of computing from the cloud to the edges. First, we develop a distributed EC platform to serve terminal users (TUs) in different IoT systems with various IoT services. Second, we put forth a cache-enabled scheme to store the popular contents from the cloud and edge sensors to reduce redundant data transmissions between TUs and the cloud. Finally, our experimental results demonstrate that the proposed EC-IoT over PLC network can significantly reduce energy consumption and transmission latency.

This work examines the possibility, performance limits, and associated costs for a self-sustained relay to transmit its own covert information to a destination on top of forwarding the source's information. Since the source provides energy to the relay for forwarding its information, the source does not allow the relay's covert transmission and is to detect it. Considering the time switching (TS) and power splitting (PS) schemes for energy harvesting, where all the harvested energy is used for transmission at the self-sustained relay, we derive the minimum detection error probability $\xi^{\ast}$ at the source, based on which we determine the maximum effective covert rate $\Psi^{\ast}$ subject to a given covertness constraint on $\xi^{\ast}$. Our analysis shows that $\xi^{\ast}$ is the same for the TS and PS schemes, which leads to the fact that the cost of achieving $\Psi^{\ast}$ in both the two schemes in terms of the required increase in the energy conversion efficiency at the relay is the same, although the values of $\Psi^{\ast}$ in these two schemes can be different in specific scenarios. For example, the TS scheme outperforms the PS scheme in terms of achieving a higher $\Psi^{\ast}$ when the transmit power at the source is relatively low. If the covertness constraint is tighter than a specific value, it is the covertness constraint that limits $\Psi^{\ast}$, and otherwise it is upper bound on the energy conversion efficiency that limits $\Psi^{\ast}$., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

Resource allocation of cellular-assisted device-to-device (D2D) communication is very challenging when frequency reuse is considered among multiple D2D pairs within a cell, as intense inter D2D interference is difficult to tackle and generally causes extremely large signaling overhead for channel state information (CSI) acquisition. In this paper, a novel resource allocation framework for cellular-assisted D2D communication is developed with low signaling overhead while maintaining high system capacity. By utilizing the spatial dispersion property of the D2D pairs, a geography-based sub-cell division strategy is proposed to divide the cell into multiple sub-cells and the D2D pairs within one sub-cell are formed into one group. Then, sub-cell resource allocation is performed independently among the sub-cells without the need of any prior knowledge of inter D2D interference. Under the proposed resource allocation framework, a tractable approximation for the inter D2D interference modeling is obtained and a computationally efficient expression for the average ergodic sum capacity of the cell is derived. The expression further allows us to obtain the optimal number of sub-cells, which is an important parameter for maximizing the average ergodic sum capacity of the cell. It is shown that with small CSI feedback, the system capacity can be improved significantly by adopting the proposed resource allocation framework, especially in dense D2D deployed systems.

This paper investigates the uplink and downlink achievable rates of full-duplex (FD) massive multi-input-multi-output (MIMO) systems in which low-resolution analog-to-digital converters/digital-to-analog converters (ADCs/DACs) are employed and maximum ratio combining/maximum ratio transmission processing are adopted. Then, employing an additive quantization noise model, we derive approximate expressions of the uplink and downlink achievable rates, in which the effect of the quantization error, the loop interference, and the inter-user interference is considered. The theoretical results show that using proper power scaling law and more antennas can eliminate the interference and the noise. Furthermore, under the fixed number of antennas, the uplink and downlink approximate achievable rates will become a constant, as the number of quantization bits tends to infinity. Increasing the resolution of ADCs/DACs will limitedly improve the system performance but cause excessive overhead and power consumption, so adopting low-resolution ADCs/DACs in FD massive MIMO systems is sensible.

Medium-scale or large-scale receive antenna array with digital beamforming can be employed at receiver to make a significant interference reduction but leads to expensive cost and high complexity of the RF-chain circuit. To deal with this issue, classic analog-and-digital beamforming (ADB) structure was proposed in the literature for greatly reducing the number of RF-chains. Based on the ADB structure, in this paper, we propose a robust hybrid ADB scheme to resist directions of arrival (DOAs) estimation errors. The key idea of our scheme is to employ null space projection (NSP) in the analog beamforming domain and diagonal loading (DL) method in digital beamforming domain. The simulation results show that the proposed scheme performs more robustly, and moreover, it has a significant improvement on the receive signal-to-interference-plus-noise ratio compared to NSP ADB scheme and DL method.

The radio frequency (RF) phase shifter with finite quantization bits in analog beamforming (AB) structure forms quantization error (QE) and causes a performance loss of received signal to interference plus noise ratio (SINR) at the receiver (called Bob). By using the law of large numbers in probability theory, the closed-form expression of the SINR performance loss is derived to be inversely proportional to the square of Sinc (or $\sin (x)/x$ ) function. Here, a phase alignment method is applied in the directional modulation transmitter with the AB structure. Also, the secrecy rate (SR) expression is derived with the QE. From the numerical simulation results, we find that the SINR performance loss gradually decreases as the number $L$ of quantization bits increases. This loss is less than 0.3 dB when $L$ is larger than or equal to three. As $L$ exceeds five, the SINR performance loss at Bob can be approximately trivial. Similarly, the SR performance loss gradually reduces as $L$ increases. In particular, the SR performance loss is about 0.1 bits/s/Hz for $L$ =3 at signal-to-noise ratio of 15 dB.

In this paper, we propose the robust transmission design for an intelligent reflecting surface (IRS) aided distributed multi-antenna system under imperfect cascaded channel state information at transmitter (CSIT). The active transmit beamforming at access points and reflection coefficients at IRS are jointly optimized to maximize the worst-case sum rate subject to the transmit power constraint and unit-modulus reflection coefficients for all possible channel realizations in the uncertainty region. We propose the conservative approximation (CA) based robust optimization. Under the framework of alternative optimization algorithm, the transmit beamforming is first solved via weighted minimum mean square error method with the fixed reflection coefficients, and then the reflection coefficients are optimized via nearest point projection method. Since the CA method can introduce the rate saturation for non-scaling CSIT as signal-to-noise ratio scales, we investigate another robust optimization algorithm, cutting-set (CS) method, which solves the problem by alternating between an optimization step for a finite subset of the uncertainty region, and a pessimization step for updating the subset. The numerical simulation results illustrate that the CS method can reap better sum-rate performance than CA, and the deleterious effect of imperfect cascaded CSIT on the performance becomes more significant with the increase of the reflecting elements.

Reconfigurable intelligent surfaces (RISs) or intelligent reflecting surfaces (IRSs), are regarded as one of the most promising and revolutionizing techniques for enhancing the spectrum and/or energy efficiency of wireless systems. These devices are capable of reconfiguring the wireless propagation environment by carefully tuning the phase shifts of a large number of low-cost passive reflecting elements. In this article, we aim for answering four fundmental questions: 1) Why do we need RISs? 2) What is an RIS? 3) What are RIS's applications? 4) What are the relevant challenges and future research directions? In response, eight promising research directions are pointed out., Accepted by IEEE Communications Magazine. Keywords: Reconfigurable intelligent surface (RIS), intelligent reflecting surface (IRS). In this paper, we have answered four critical questions in RIS/IRS. More importantly, we have pointed out several promising research directions

To improve the localization precision of unmanned aerial vehicle (UAV), a novel framework is established by jointly utilizing multiple measurements of received signal strength (RSS) from multiple base stations (BSs) and multiple points on trajectory. First, a joint maximum likelihood (ML) of exploiting both trajectory information and multi-BSs is proposed. To reduce its high complexity, two low-complexity localization methods are designed. The first method is from BS to trajectory (BST), called LCSL-BST. First, fixing the $n$ th BS, by exploiting multiple measurements along trajectory, the position of UAV is computed by ML rule. Finally, all computed positions of UAV for different BSs are combined to form the resulting position. The second method reverses the order, called LCSL-TBS. We also derive the Cramer-Rao lower boundary (CRLB) of the joint ML method. From simulation results, we can see that the proposed joint ML and separate LCSL-BST methods have made a significant improvement over conventional ML method without use of trajectory knowledge in terms of location performance. The former achieves the joint CRLB and the latter is of low-complexity.

The problem of resource allocation in multicarrier non-orthogonal multiple access (NOMA) systems is non-deterministic polynomial time (NP)-hard and requires exhaustive search, which has prohibitive computational complexity. Computationally-efficient algorithms that provide a performance close to optimal are needed. In this paper, the problem of resource allocation is divided into two sub-problems, namely subcarrier assignment and power allocation. The optimal users’ channel condition relationship is derived in terms of effective system sum-rate, and a computationally-efficient ideal partner search algorithm (IPSA) is proposed. Further, optimal power allocation (OPA) is studied in terms of fairness, and an OPA coefficient is theoretically derived based on the channel condition ratio of two NOMA users. Numerical results show that IPSA with OPA outperforms IPSA with fractional transmit power allocation (FTPA), the strongest user-weakest user (SUWU) scheme and user grouping (UG) in terms of achievable system effective sum-rate, average user bit error rate (BER) and number of users with a poor BER. Further, the computational complexity of IPSA with OPA is much lower than that of exhaustive search.

In this paper, unmanned aerial vehicles (UAVs) and intelligent reflective surface (IRS) are utilized to support terahertz (THz) communications. To this end, the joint optimization of UAV's trajectory, the phase shift of IRS, the allocation of THz sub-bands, and the power control is investigated to maximize the minimum average achievable rate of all the users. An iteration algorithm based on successive Convex Approximation with the Rate constraint penalty (CAR) is developed to obtain UAV's trajectory, and the IRS phase shift is formulated as a closed-form expression with introduced pricing factors. Simulation results show that the proposed scheme significantly enhances the rate performance of the whole system., Accepted by IEEE Wireless Communications Letters. Keywords: Intelligent reflective surface (IRS), reconfigurable intelligent surface (RIS), terahertz (THz) communications, unmanned aerial vehicles (UAVs) communications

A switched-beam based multiple-input-multiple output (MIMO) vehicle-to-infrastructure (V2I) system can grant better system reliability as the signal-to-interference-plus-noise ratio (SINR) can be significantly optimized. In order to maximize the number of served vehicles by the simple switched-beam based V2I system, it is crucial to additionally introduce vehicle-to-vehicle (V2V) communication because (i) more than one vehicle might occupy a beam coverage, and (ii) vehicles may be out of the coverage region of a beam. In this paper, we proposed a cooperative infrastructure-beam allocation (IBA) and location-estimation (LE) scheme to optimize system coverage and reliability. IBA is an SINR optimization algorithm, and LE is a location-based proximity service for V2I and V2V communications respectively. Our simulation results show that a reliable beam coverage region in switched-beam V2I can be achieved with flexible value of SINR threshold. Also, the outage probability and average rates are directly dependent on set SINR thresholds. Finally, cooperation between V2I and V2V communication significantly improves the utility ratio which is further used as a metric for reliability.

It is crucial for emergency communication networks to properly manage and coordinate between rescuers and disaster-affected users. Low-cost and high-mobility drones become important contributors to build up such kind of emergency communication networks. In this paper, we consider the drone as a deployed base station to provide communication for ground users in the post-disaster area. Considering the importance of providing guaranteed service for rescuers (compared to normal users), we have investigated power and subcarrier allocation to maximize the downlink system capacity in the drone-assisted emergency communication system. Due to the complexity of the formulated problem. a suboptimal solution is proposed by dividing users into two priority groups: high-priority users (rescuers) and low-priority users (affected people). According to analyses, the complexity of the proposed scheme is much lower than the optimal scheme. Simulation results have shown that when the transmit power of the drone is not very high, the performance of the proposed resource allocation scheme is very close to the optimal scheme.

Autonomous driving in future vehicle system has imposed a high demand for reliable and power efficient communication to provide safe-driving to vehicular users. In order to achieve reliability and power efficiency, in this paper, a vehicular trellis architecture (VTA) is proposed. VTA allows traffic information to be transmitted either via vehicle-to-infrastructure (V2I), through the dedicated remote radio head (RRH) and its dynamically select vehicle(s) or, direct vehicle-to-vehicle (V2V) communication. We investigated the adaptive switching over V2I/V2V, to enhance efficient system reliability while optimizing the average power consumption of VTA. This paper presents a vehicular trellis algorithm (VTRA), which approximate solutions to the optimization problem. Simulation results demonstrate that the selection of joint V2I/V2V communication reduces the total power consumption of the system for a varying number of threshold distance and vehicles.

In vehicular communication systems, maximizing the number of served vehicles while simultaneously guaranteeing reliable coverage at all the vehicles can be a challenging proposition. A switched-beam based infrastructure can provide better reliability as the signal-to-interference-plus-noise ratio (SINR) can be improved. However, a simple switched-beam based vehicle-to-infrastructure (V2I) system alone may not suffice for serving all the vehicles because (i) the number of vehicles is more than the number of beams, and (ii) a vehicle may be out of the coverage region of a beam. Therefore, introducing vehicle-to-vehicle (V2V) communication becomes crucial in extending the number of served vehicles. In this paper, a joint vehicle-beam allocation (VBA) and vehicular proximity (VP) algorithms for V2I and V2V, respectively are proposed to guarantee reliable coverage for vehicles. VBA is an SINR optimization algorithm, and VP is based on LTE Mode 4, a proximity-based service for V2V communications. It is shown that setting a flexible SINR threshold helps in attaining a reliable beam coverage region in switched-beam based V2I communication. It is proven that the outage probability is also directly dependent on SINR thresholds. Lastly, the concept of utility ratio is also introduced as a metric for reliability. Simulation results show that joint V2I and V2V communication significantly improves the utility ratio.

In this paper, the performance analysis of non-orthogonal multiple access (NOMA) in heterogeneous cloud radio access networks (H-CRAN) is carried out, where coordination of macro base station (MBS) and remote radio heads (RRHs) for H-CRAN with NOMA is introduced to improve network performance. We formulate the problem of jointly optimizing user association, coordinated scheduling and power allocation for NOMA-enabled H-CRANs. To efficiently solve this problem, we decompose the joint optimization problem into two subproblems as 1) coordinated scheduling 2) power allocation optimization. Firstly the users are divided based on interferences they suffer from. This interference-aware NOMA approach account for the inter-tier interference. Based on the user scheduling scheme, optimal power allocation optimization is performed by the hierarchical decomposition approach. Simulation results show that the proposed NOMA-enabled H-CRAN outperforms orthogonal multiple access (OMA)-based H-CRANs in terms of total achievable rate, interference mitigation and can achieve significant fairness improvement.

Rapid deployment is crucial for building up drone aided emergency communications to ensure the coverage and service support after the disaster. The chaos in the post-disaster area, such as the number of ground users and scattered locations, makes difficult on the decision of drone deployment. In this paper, an unsupervised machine learning method is conducted for drone deployment in drone aided emergency communications. Considering the importance of sustainable services for drones, the drone deployment problem is formulated with the aim of minimizing the total power of drones with all users’ coverage while maintaining their rate requirements, with constraints on drones’ coverage area, capacity and limited power. The problem is solved by two steps. A modified k-means clustering algorithm is proposed to obtain the number of drones and an optimal altitude and minimum transmit power algorithm is then derived. Simulation results show that although the number of drones obtained by the modified algorithm is more than that of the original k-means algorithm, all users are served and the minimum power of drones is guaranteed by proposed algorithms.

Deep learning (DL) based autoencoder is a promising architecture to implement end-to-end communication systems. One fundamental problem of such systems is how to increase the transmission rate. Two new schemes are proposed to address the limited data rate issue: adaptive transmission scheme and generalized data representation (GDR) scheme. In the first scheme, an adaptive transmission is designed to select the transmission vectors for maximizing the data rate under different channel conditions. The block error rate (BLER) of the first scheme is 80% lower than that of the conventional one-hot vector scheme. This implies that higher data rate can be achieved by the adaptive transmission scheme. In the second scheme, the GDR replaces the conventional one-hot representation. The GDR scheme can achieve higher data rate than the conventional one-hot vector scheme with comparable BLER performance. For example, when the vector size is eight, the proposed GDR scheme can double the date rate of the one-hot vector scheme. Besides, the joint scheme of the two proposed schemes can create further benefits. The effect of signal-to-noise ratio (SNR) is analyzed for these DL-based communication systems. Numerical results show that training the autoencoder using data set with various SNR values can attain robust BLER performance under different channel conditions.

Spatial modulation (SM) is a particularly important form of multiple-input-multiple-output (MIMO). Unlike traditional MIMO, it uses both modulation symbols and antenna indices to carry information. In this paper, to avoid the high cost and circuit complexity of fully-digital SM, we mainly consider the hybrid SM system with a hybrid precoding transmitter architecture, combining a digital precoder and an analog precoder. Here, the partially-connected structure is adopted with each radio frequency chain (RF) being connected to a transmit antenna subarray (TAS). In such a system, we made an investigation of secure hybrid precoding and transmit antenna subarray selection (TASS) methods. Two hybrid precoding methods, called maximizing the approximate secrecy rate (SR) via gradient ascent (Max-ASR-GA) and maximizing the approximate SR via alternating direction method of multipliers (Max-ASR-ADMM), are proposed to improve the SR performance. As for TASS, a high-performance method of maximizing the approximate SR (Max-ASR) TASS method is first presented. To reduce its high complexity, two low-complexity TASS methods, namely maximizing the eigenvalue (Max-EV) and maximizing the product of signal-to-interference-plus-noise ratio and artificial noise-to-signal-plus-noise ratio (Max-P-SINR-ANSNR), are proposed. Simulation results will demonstrate that the proposed Max-ASR-GA and Max-ASR-ADMM hybrid precoders harvest substantial SR performance gains over existing method. For TASS, the proposed three methods Max-ASR, Max-EV, and Max-P-SINR-ANSNR perform better than existing leakage method. Particularly, the proposed Max-EV and Max-P-SINR-ANSNR is low-complexity at the expense of a little performance loss compared with Max-ASR., 13 pages, 8 figures

In this paper, a cooperative task computation framework exploits the computation resource in UEs to accomplish more tasks meanwhile minimizes the power consumption of UEs. The system cost includes the cost of UEs' power consumption and the penalty of unaccomplished tasks and the system cost is minimized by jointly optimizing binary offloading decisions, the computational frequencies, and the offloading transmit power. To solve the formulated mixed-integer non-linear programming problem, three efficient algorithms are proposed, i.e., integer constraints relaxation-based iterative algorithm (ICRBI), heuristic matching algorithm, and the decentralized algorithm. The ICRBI algorithm achieves the best performance at the cost of the highest complexity, while the heuristic matching algorithm significantly reduces the complexity while still providing reasonable performance. As the previous two algorithms are centralized, the decentralized algorithm is also provided to further reduce the complexity, and it is suitable for the scenarios that cannot provide the central controller. The simulation results are provided to validate the performance gain in terms of the total system cost obtained by the proposed cooperative computation framework., Comment: 30 pages,11 figures

In this paper, several novel test methods are proposed for 5G mobile network field trials. As it is known, field trial plays an important role in wireless network commercialization process. However, several new technologies have been considered in 5G communication networks, such as massive MIMO. Therefore, conventional filed trial methods used in 4G mobile networks cannot fulfill the new test requirements for 5G mobile networks. Four new field trial methods have been discussed in our work. Moreover, real measurement data has been collected to demonstrate the proposed methods from 5G large scale filed trial supported by China Mobile Communications Group Co., Ltd.

Content caching at mobile user devices (UDs) utilizing device to device (D2D) communications is a promising technology to enhance the performance of mobile networks, in terms of latency, throughput, energy consumption, and so on. In this paper, a novel method of content delivery using multiple devices to single device (MDSD) communications through D2D links is presented. In this method, the Zipf distribution with exponent shape parameter is adopted to model the content caching popularity for the analysis of the achievable signal to interference plus noise ratio (SINR). In order to investigate the advantage of the proposed MDSD method, firstly, a closedform expression of the outage probability is theoretically derived for a single D2D communication to evaluate the success of content delivery to a reference UD. Secondly, the expression of the outage probability for MDSD communication is derived, where the outage probability is modeled as a function of content caching popularity, the density of UDs, and the size of cooperative area. The research work is further extended to address the frequency reuse among different UDs in one cell, where a frequency band factor is introduced, and the optimal radius of the cooperative area is introduced and analysed. The analytical results, validated by the simulation results, show that the outage probability decreases drastically when the popularity of the content increases, or the radius of the cooperative area increases. Using the given closed-form expression of the outage probability, the area spectral efficiency (ASE) of the system is presented. Furthermore, the results show that as the frequency band factor increases, the outage probability decreases, as well as the ASE decreases. Finally, it is shown that the MDSD outperforms the single D2D-based method.

We investigate the uplink and downlink achievable rates of full-duplex (FD) massive multi-input multi-output (MIMO) systems with low-resolution analog-digital converters/digital-to-analog converters (ADCs/DACs), where maximum ratio combining/maximum ratio transmission (MRC/MRT) processing are adopted and imperfect channel state information (CSI) is assumed. In this paper, the quantization noise is encapsulated as an additive quantization noise model (AQNM). Then, employing the minimum mean-square error (MMSE) channel estimator, approximate expressions of the uplink and downlink achievable rates are derived, based on the analysis of the quantization error, loop interference (LI), and the inter-user interference (IUI). It is shown that the interference and noise can be eliminated by applying power scaling law properly and increasing the number of antennas. Moreover, given the number of antennas, it is found that the uplink and downlink approximate achievable rates will converge to a constant when the number of quantization bit tends to infinity. Therefore, the system performance that can be improved by increasing ADC/DAC resolution is limited, implying that it is reasonable to adopt low-resolution ADCs/DACs in FD massive MIMO systems.

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-single-output (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for real-time implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems.

In this paper, the user cooperative task computation is explored by sharing the computing capability of the user equipments (UEs) so as to enhance the performance of mobile edge computing (MEC) networks. The number of completed tasks is maximized while minimizing the total power consumption of the UEs by jointly optimizing the user task offloading decision, the computational speed for the offloaded task and the transmit power for task offloading. An iterative algorithm based on the linear programming relaxation is proposed to solve the formulated mixed integer non-linear problem. The simulation results show that the proposed user cooperative computation scheme can achieve a higher completed tasks ratio than the non-cooperative scheme.

The demand for wideband communication in the coastal area (i.e., ≤ 100 km from the coastline) has been rapidly increasing in recent years. Compared to the terrestrial scenario, the coastal environment has long-distance and highly dynamic channels, and the communication devices are more strictly constrained by energy supplies. While the RLNC has the fountain erasure-correction property and is suitable for transmissions over the long-distance dynamic channels, it suffers from high coding coefficient delivery cost and decoding complexity. In this article, we look into the application of sparse network coding in coastal communication systems. We identify two typical multicast scenarios that may appear in coastal communications, namely the relay-aided multicast and multicast from a shore-based base station with D2D communication enabled among the subscribers. We provide a detailed comparison of existing sparse network coding schemes. Based on that, we demonstrate through simulations that an appropriate choice of sparse codes is critical to meet the unique requirements in coastal communication systems. We show that batched sparse code is suitable for relay-aided multicast, and subset-based sparse codes are preferable for D2D-enabled multicast.

In this paper, optimal content caching strategy is proposed to jointly minimize the cell average outage probability and fronthaul usage in cloud radio access network (Cloud-RAN). At first, an accurate closed form expression of the outage probability conditioned on the user's location is presented, and the cell average outage probability is obtained through the composite Simpson's integration. The caching strategy for jointly optimizing the cell average outage probability and fronthaul usage is then formulated as a weighted sum minimization problem, which is a nonlinear 0-1 integer NP-hard problem. In order to deal with the NP-hard problem, two heuristic algorithms are proposed in this paper. Firstly, a genetic algorithm (GA) based approach is proposed. Numerical results show that the performance of the proposed GA-based approach with significantly reduced computational complexity is close to the optimal performance achieved by exhaustive search based caching strategy. Secondly, in order to further reduce the computational complexity, a mode selection approach is proposed. Numerical results show that this approach can achieve near-optimal performance over a wide range of the weighting factors through a single computation., Comment: Accepted in IEEE TVT

—Massive multiple-input-multiple-output (MIMO)\ud has become a promising technique to provide high-data-rate\ud communication in fifth-generation (5G) mobile systems, thanks to\ud its ability to form narrow and high-gain beams. Among various\ud massive MIMO beamforming techniques, the fixed-beam scheme\ud has attracted considerable attention due to its simplicity. In this\ud paper, we focus on a fixed-beam based multiuser massive MIMO\ud system where each user is served by a beam allocated to it. To\ud maximize the sum data rate, a greedy beam allocation algorithm\ud is proposed under the practical condition that the number of\ud radio frequency (RF) chains is smaller than the number of users.\ud Simulation results show that our proposed greedy algorithm\ud achieves nearly optimal sum data rate. As only the sum data rate\ud is optimized, there are some “worst-case” users who could suffer\ud from strong inter-beam interference and thus experience low\ud data rate. To improve the individual data rates of the worst-case\ud users while maintaining the sum data rate, an adaptive frequency\ud reuse scheme is proposed. Simulation results corroborate that our\ud proposed adaptive frequency reuse strategy can greatly improve\ud the worst-case users’ data rates and the max-min fairness among\ud served users without sacrificing the sum data rate.

Universal filtered multi-carrier (UFMC) is a potential waveform technology, which can efficiently combat different carrier frequency offsets (CFOs) of multiple users. First, adaptive modulation and power allocation are applied for each subcarrier to meet a preset bit error rate (BER) requirement by ignoring CFOs. Then, we prove that different CFOs will cause different interference variances to adjacent users, which results in performance degradation in UFMC systems. To reduce the interference caused by CFOs and improve achievable rate, a novel adaptive filter configuration algorithm is proposed to adaptively design the parameters of the finite impulse response filters. Specifically, the proposed algorithm is available for the UFMC systems, where the user equipments are allocated with different bandwidths. Finally, simulation results show that the proposed adaptive filter configuration algorithm can dramatically eliminate the interference caused by different CFOs, and achieve better BER performance and a higher achievable rate than the conventional scheme.

In this paper, the closed-form parameterizations to the Pareto boundary for the two-user multiple-input single-output interference channel are studied. Firstly, for the equivalent channel model with each transmitter having only two antennas, the weighted sum-rate maximization (WSRMax) problem is reformulated with newly defined angle variables. Then, a centralized weighted leakage-plus-noise-to-signal ratio minimization (WLNSRMin) algorithm is proposed to find a locally optimal weighted sum-rate point. Each step of the algorithm is solved by evaluating closed-form expressions. A distributed algorithm is also given to avoid the exchange of the channel state information (CSI) between transmitters. Numerical results show that the centralized WLNSRMin algorithm converges to a local optimum of the WSRMax problem after a few iterations and the distributed algorithm achieves a performance very close to that of the centralized algorithm with only local CSI.

This paper addresses the beam allocation problem in a switched-beam based massive multiple-input-multiple-output (MIMO) system working at the millimeter wave frequency band, with the target of maximizing the sum data rate. This beam allocation problem can be formulated as a combinatorial optimization problem under two constraints that each user uses at most one beam for its data transmission and each beam serves at most one user. The brute-force search is a straightforward method to solve this optimization problem. However, for a massive MIMO system with a large number of beams $N$ , the brute-force search results in intractable complexity $O(N^{K})$ , where $K$ is the number of users. In this paper, in order to solve the beam allocation problem with affordable complexity, a suboptimal low-complexity beam allocation (LBA) algorithm is developed based on submodular optimization theory, which has been shown to be a powerful tool for solving combinatorial optimization problems. Simulation results show that our proposed LBA algorithm achieves nearly optimal sum data rate with complexity $O(K\log N)$ . Furthermore, the average service ratio, i.e., the ratio of the number of users being served to the total number of users, is theoretically analyzed and derived as an explicit function of the ratio $N/K$ .

In intensity-modulation and direct-detection (IM/DD) multiple-input and multiple-output (MIMO) visible light communication (VLC) systems, spatial subchannels are usually correlated, and spatial modulation is a good choice to achieve the advantages of MIMO technology. Peak-to-average power ratio (PAPR) is a key issue in VLCs due to the limited linear dynamic range of light emitting diodes (LEDs). Single-carrier communication systems have a lower PAPR than orthogonal frequency division multiplexing (OFDM) communication systems. However, it is challenging to design a single-carrier spatial modulation for high-rate transmissions because of the time domain intersymbol interference. This paper develops an optical spatial modulation (OSM) scheme based on bipolar pulse amplitude modulation (PAM) and spatial elements for high-rate indoor VLC systems. Multiple data streams can be transmitted simultaneously in the proposed scheme. Based on the transmit strategy, we develop a low-complexity receiver algorithm that achieves better bit-error rate performance than reference schemes, and the proposed OSM scheme has a much lower PAPR than OFDM based OSM schemes. When the spatial subchannels are highly correlated, a spatial area division strategy is applied, and the receiver algorithm is investigated. The symbol-error rate expression of the proposed OSM scheme is derived, and the computational complexity is analyzed.

We consider an underlay wiretap cognitive two-way relay network (CTWRN), where two secondary sources exchange their messages via multiple secondary decode-and-forward digital network coding relays in the presence of an eavesdropper by using a three-phase time division broadcast protocol and sharing the licensed spectrum of primary users. To mitigate eavesdropping attacks, an artificial noise (AN)-aided opportunistic relay selection scheme, called generalized max-min (GMM) relay selection is proposed to enhance physical layer security for the wiretap CTWRNs. The performance of the GMM scheme is analyzed, and evaluated by the exact closed-form outage probability and intercept probability. Additionally, we also provide asymptotic approximations for the outage probability and intercept probability at high signal-to-noise ratio. For comparison, we analyze the performance of the conventional max-min (MM) relay selection scheme as well. It is shown that the GMM scheme outperforms the MM scheme in terms of the security-reliability tradeoff (SRT), where the security and reliability are quantified by the intercept probability and outage probability, respectively. Moreover, the SRTs of the MM and GMM schemes can be substantially improved by increasing the number of secondary relays, while the improvement of the GMM scheme is more evident than that of the MM scheme.

In this paper, a large-scale distributed antenna system (DAS) with network-assisted full duplexing (NAFD) is investigated. Our model assumes that imperfect channel state information (CSI) is available and channels have independent spatial correlations. Based on large dimensional random matrix theory (RMT), the deterministic equivalent expressions for the ergodic uplink (UL) sum-rate with minimum-mean-square-error (MMSE) receiver as well as the ergodic downlink (DL) sum-rate with regularized zero-forcing (RZF) beamforming are derived. With the cancellation of downlink-to- uplink interference, the spectral efficiency of NAFD system is further improved. Numerical simulations indicate that under various environment settings, the deterministic equivalent results are accurate in both large-scale system and system with finite number of antennas.

Network assisted full-duplex (NAFD) is a spatial-division duplex technique for future wireless networks with cell-free massive multiple-input multiple-output (CF massive MIMO) network, where a large number of remote antenna units (RAUs), either using half-duplex or full-duplex, jointly support truly flexible duplex including time-division duplex, frequency-division duplex and full duplex on demand of uplink and downlink traffic by using network MIMO methods. With NAFD, bi-directional data rates of the wireless network could be increased and end-to-end delay could be reduced. In this paper, the spectral efficiency of NAFD communications in CF massive MIMO network with imperfect channel state information (CSI) is investigated under spatial correlated channels. Based on large dimensional random matrix theory, the deterministic equivalents for the uplink sum-rate with minimum-mean-square-error (MMSE) receiver as well as the downlink sum-rate with zero-forcing (ZF) and regularized zero-forcing (RZF) beamforming are derived. Numerical results show that under various environmental settings, the deterministic equivalents are accurate in both a large-scale system and system with a finite number of antennas. It is also shown that with the downlink-to-uplink interference cancellation, the uplink spectral efficiency of CF massive MIMO with NAFD could be improved. The spectral efficiencies of NAFD with different duplex configurations such as in-band full-duplex, and half-duplex are compared. With the same total numbers of transmit and receive antennas, NAFD with half-duplex RAUs offers a higher spectral efficiency. To alleviate the uplink-to-downlink interference, a novel genetic algorithm based user scheduling strategy (GAS) is proposed. Simulation results show that the achievable downlink sum-rate by using the GAS is greatly improved compared to that by using the random user scheduling.

The problem of resource allocation in non-orthogonal multiple access (NOMA) systems is nondeterministic polynomial time (NP)-hard and requires exhaustive search. Hence, fast and computationally efficient algorithms that provide a performance close to optimal are needed. In this paper, the problem of resource allocation is divided into the sub-problems of subcarrier assignment and power allocation. The optimal users' channel condition relationship is derived in terms of the effective system sum-rate, and a fast ideal partner search algorithm (IPSA) is proposed in terms of that relationship. The performance of IPSA is verified under fractional transmit power allocation (FTPA). Simulation results show that our algorithm outperforms the strongest user-weakest user (SUWU) scheme, user grouping (UG) and random subcarrier and power allocation (RSPA) in terms of achievable system effective sum-rate, average user bit error rate (BER) and number of users with a poor BER.

In this paper, we propose a joint optimization design for a non-orthogonal multiple access (NOMA)-based satellite-terrestrial integrated network (STIN), where a satellite multicast communication network shares the millimeter wave spectrum with a cellular network employing NOMA technology. By assuming that the satellite uses multibeam antenna array and the base station employs uniform planar array, we first formulate a constrained optimization problem to maximize the sum rate of the STIN while satisfying the constraint of per-antenna transmit power and quality-of-service requirements of both satellite and cellular users. Since the formulated optimization problem is NP-hard and mathematically intractable, we develop a novel user pairing scheme so that more than two users can be grouped in a cluster to exploit the NOMA technique. Based on the user clustering, we further propose to transform the non-convex problem into an equivalent convex one, and present an iterative penalty function-based beamforming (BF) scheme to obtain the BF weight vectors and power coefficients with fast convergence. Simulation results confirm the effectiveness and superiority of the proposed approach in comparison with the existing works.

The mobility management will be more complex and will have a great impact on the quality of service (QoS) in the future cellular networks, as these networks will have to handle a huge number of user equipment (UEs) and their frequent handoffs due to very dense short-footage small cells. This paper presents a framework to model and derive the coverage of small cells, the cell sojourn time and the handoff rate in multi-tier small cell networks. The distribution of the small cells around a reference UE’s path is studied by taking into consideration\ud the overlaps among the small cells. Two types of handoff rates are introduced to estimate the load managed by different cells, where inter-frequency handoff (IRH) rate and intra-frequency handoff (IAH) rate represent the fraction of handoffs managed by the first tier and the other tiers, respectively. Our analysis shows that ignoring the overlaps among the small cells affects the accuracy of the results ignificantly. The simulation results validate the accuracy of the analytical results and also show the impact of different parameters such as the small cell density, the number of tiers and the size of the small cells on the small cell sojourn time, the macro cell sojourn time and the handoff rate.

To achieve a better effect of interference on eavesdropper with an enhanced security, a secure precise jamming (PJ) and communication (SPJC) is proposed and its basic idea is to force the transmit energy of artificial noise (AN) and confidential message into the neighborhoods of Eve and Bob by using random subcarrier selection (RSS), directional modulation, and beamforming under phase alignment (PA) constraint (PAC). Here, we propose two high-performance beamforming schemes: minimum transmit power (Min-TP) and minimum regularized transmit power (Min-RTP) to achieve SPJC under PAC and orthogonal constraint (OC), where OC means that AN and CM are projected onto the null-spaces of the desired and eavesdropping channels, respectively. Simulation results show that the proposed Min-TP and Min-RTP methods perform much better than existing equal amplitude (EA) method in terms of both bit-error-rate (BER) and secrecy rate (SR) at medium and high signal-to-noise ratio regions. The SR performance difference between the proposed two methods becomes trivial as the number of transmit antennas approaches large-scale. More importantly, we also find the fact that all three schemes including EA, Min-TP, and Min-RTP can form two main peaks of AN and CM around Eve and Bob, respectively. This achieves both PJ and secure precise wireless transmission (SPWT), called SPJC.