Search

This paper addresses the resource allocation (RA) for ultra‐dense network (UDN), where base stations (BSs) are densely deployed to meet the demands of future wireless communications. However, the design of RA in UDN is challenging, as the RA problem is non‐convex and NP‐hard. Therefore, this paper considers and studies a semi‐distributed resource block (RB) allocation scheme, in order to achieve a well‐balanced trade‐off between performance and complexity. In the context of semi‐distributed RB allocation scheme, the problem can be decomposed into the subproblem of clustering and the subproblem of cluster‐based RB allocation. We first improve the K‐means clustering algorithm by employing the Gaussian modified method, which can significantly decrease the number of iterations for carrying out the K‐means algorithm as well as the failure possibility of clustering. Then, bat algorithm (BA) is introduced to attack the problem of cluster‐based RB allocation. In order to make the original BA applicable to the problem of RB allocation, chaotic sequences are adopted to discretize the initial position of the bats, and simultaneously increase the population diversity of the bats. Furthermore, in order to speed up the convergence of BA, the logarithmic decreasing inertia weight is employed for improving the original BA. Our studies and performance results show that the proposed approaches are capable of achieving a desirable trade‐off between the performance and the implementation complexity. [ABSTRACT FROM AUTHOR]

The use of selective partial updating method for reducing computational complexity of adaptive algorithms has been proposed recently. However, its performance analysis has not been studied as extensively and exactly as LMS or RLS algorithms for time‐varying channel estimation. This paper provides performance analysis of the low‐complexity family of affine projection algorithms based on selective partial update method on the estimation of multipath Rayleigh fading channels in the presence of carrier frequency offsets (CFO) and random channel variations. The analysis is based on the calculation of the error correlation matrix of the estimation, the mean‐square weight error (MSWE) and the mean‐square estimation error (MSE) parameters. The analysis does not use strong assumptions like small or large step‐size, and explicit closed‐form expressions for the MSE of estimation are obtained only from common hypotheses in wireless communication context. In this paper, the optimum step‐size parameters minimizing the MSE of estimation are analytically derived without any simplifying assumptions. For the sake of comparison with other analytical approaches, the performance of the introduced algorithms is also investigated using the energy conservation relation. Likewise, for exact performance analysis, all the moment terms that appear in closed form expressions for the MSE of estimation are evaluated. Simulations are conduced to corroborate the presented studies and show that the theoretical results agree well with the simulation results over non‐stationary multipath Rayleigh fading channels. [ABSTRACT FROM AUTHOR]

This paper considers the queue-aware optimal energy minimisation sparse beamforming design (QESB) in a downlink cloud radio access network (C-RAN) system where multi-RRH communicates with multi-user through a central computing cloud via digital front-haul links. The problem is formulated as the joint optimisation problem of the transmission energy consumption, system queue length and front-haul cost with sparse beamforming design. As we know, the beamforming design adaptive to both QSI and CSI is challenging because of the high complexity. Apart from previous works that take queue length as constraints, in this paper we directly minimise the queue length state involved joint optimisation problem with SINR constraints. A smooth function is proposed to approximate the l0-norm function which is discrete and non-convex. To overcome the challenge due to the non-convexity of the optimisation problem, the semidefinite relaxation (SDR) technology is utilised to convert the primitive problem into the difference of convex (DC) programming problem, and convex and concave procedure (CCP) algorithm is used to induce the sparsity of the beamforming control. The simulation results show that the scheme proposed by this paper can obtain a good tradeoff between system energy consumption, queue length and front-haul cost with SINR constraints in C-RAN system. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are widely considered as key enablers for future wireless networks due to their advantages, such as high mobility and flexible deployment. In this paper, the UAV assisted secure communication system is investigated, where the UAV is deployed as the mobile jammer to prevent the eavesdropper from overhearing the confidential message. With the objective of maximizing the secrecy rate, a joint optimization problem involving the trajectory and transmit power of UAV, as well as the transmit power of source node is formulated. Moreover, the effects of Non‐Fly Zone (NFZ) and imperfect estimation on the location of eavesdropper are also taken into consideration. As the original problem is hardly trackable, the worst case secrecy rate (WCSR) assumption is first employed to bypass the uncertainty brought by the estimation error. Then a block coordinate descent (BCD) based algorithm is proposed to decompose the problem into three sub‐ones, where the trajectory of UAV, the transmit power of UAV and the transmit power of source can be obtained in an iterative manner. Simulation results reveal that the proposed algorithm can improve the secrecy performance significantly. In addition, the robustness of the proposed algorithm under the estimation error can also be verified. [ABSTRACT FROM AUTHOR]

Sorted QR decomposition (SQRD) has been extensively adopted for various multiple‐input‐multiple‐output (MIMO) detectors, in which the sorting process incurs severe latency when it comes to larger‐scale MIMO situations. This paper proposes a group‐SQRD (GSQRD) algorithm to alleviate the latency problem of general SQRD architectures for larger‐scale MIMO systems. Via predictively sorting a group of 4 columns at one stage, the GSQRD could eliminate the processing latency by 41% for decomposing 16×16 complex‐valued matrices. Additionally, this percentage even rises up to 68% for decomposing 128×128 matrices. To analyse the side effects, the GSQRD is applied in various MIMO detectors in a simulation link, which exhibits a negligible performance degradation for MIMO detection. Moreover, GSQRD is a hardware‐friendly algorithm because the division and square root operations in GSQRD are converted to multiplications for simplifying the hardware implementation. Based on this algorithm, two corresponding hardware architectures, which contains 2 and 4 columns respectively in a sorting group, are also implemented with 65‐nm CMOS technology. These architectures can work at 513 MHz to decompose 16×16 complex‐valued matrices. The processing latencies are respectively 0.32 and 0.26 μs, superior to the state‐of‐art designs. [ABSTRACT FROM AUTHOR]

Indoor human wireless sensing is crucial for many applications such as smart homes and security monitoring. It needs to understand both the number of people and their activities, which is often difficult, especially in large spaces. This paper proposes a combined people number counting and action recognition method by using the channel state information (CSI) of Wi‐Fi signal, which aims to simultaneously estimate the number of people and their actions in wireless sensing applications. First, a jump region removal algorithm is developed to calibrate the phase difference. Second, a cumulative sliding variance algorithm is designed for the detection of moving targets in the environment. Then a multi‐dimensional feature is formed by the amplitude, frequency domain, and phase difference of CSI, and based on it, a two‐level classifier based on the SVM algorithm is used to estimate the number of people and their actions. Experimental results show that the average accuracy of the system for the people counting is 93.5%, and the activity recognition rate of one‐person and two‐person is 99.6% and 94.6% respectively. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) communication can increase network coverage, spectrum efficiency and energy efficiency (EE) within the existing cellular infrastructure, which makes it a promising architecture for the future networks. Due to the diversification of services, heterogeneous statistics quality-of-service constraints are considered in this study, where cellular users are concerned about the delay constraint and D2D user groups pay more attention to the outage probability of data transmission. The power allocation problem of cellular users can be solved by optimising the capacity-payoff power-loss game model. Upon exploiting Lagrange dual decomposition and the Newton iteration method, the power optimisation problem of cellular users is transformed into a parameter optimisation problem. Due to the limited energy resource of D2D users, EE is the focus of D2D user groups. Using fractional programming and convex optimisation techniques, energy-efficient optimal power allocation algorithms of D2D users are proposed subject to the outage probability constraint. As a result, a power allocation algorithm based on hierarchical game is conceived for efficiently solving the power optimisation problem. The simulation results show that the proposed algorithm can obtain a performance improvement compared with other algorithm and converge within a certain number of iterations. [ABSTRACT FROM AUTHOR]

Today, a directional sensor network is a popular environment for solving the target coverage problem. Monitoring all targets in a DSN is a crucial challenge to scholars working in this field of study. Adjusting the angle and range of the sensors can be an efficient technique for improving the network performance. In this way, the network has the most extended lifespan and, at the same time, spends the least time to find the best cover set. In this method, each sensor dynamically adjusts its own sensing angle in order to find the targets by choosing the best range. The present study proposed a continuous learning automata‐based method to choose the optimum sensing angle for the sensors in a DSN. Then, to evaluate the proposed algorithm performance, its results were compared to those of a conventional automata‐based method whose algorithm worked based on continuous automata. The comparative analysis confirmed the superiority of the proposed method over the conventional automata‐based method regarding the extension of the network lifespan. [ABSTRACT FROM AUTHOR]

Reducing energy consumption of the internet becomes more and more important. The authors propose a novel approach, called predictive energy-saving approach (PESA), to put near-idle nodes of a network into sleep mode to save energy. In PESA, they propose an online spatial hidden Markov model (OSHMM) to describe the distribution of the network traffic and predict the lowest-loaded nodes. Those nodes will be put into sleep mode under the constraints that the network's full connectivity is kept and no new congested nodes are generated. To reduce the computation, they leverage on an online training technique and M-algorithm in OSHMM. They evaluate the approach over a randomly generated topology and a real ISP backbone topology with a real traffic dataset. The results show that the proposed approach is energy efficient with low computation complexity. [ABSTRACT FROM AUTHOR]

In this study, a closed-form solution is presented for the constrained water-filling problem (CWFP) where in powers allocated to the user resources maximise the user's capacity under an interference constraint and a total power constraint. Total power constraint in CWFP is to save the energy in wireless network. To solve CWFP, erstwhile algorithms compute powers for all the resources iteratively. Unlike existing algorithms, the proposed method calculates the number of resources that gets positive (or non-zero) powers using the concepts of traditional water-filling problem. Later, powers are allocated to the resolved resources (that gets positive powers) alone using the closed-form solution, which reduces the number of computations. It can be discerned that the computational complexity of the given solution is of the order of NS where NS is the total number of resources; which is remarkably lower than that of the prior algorithms specified by an order of 103 where NS is the total number of resources; which is remarkably lower than that of the prior algorithms specified by an order of 105 × NS 3 . [ABSTRACT FROM AUTHOR]

In this study, the authors focus on segment characteristic analysis and joint optimisation of modulation and coding scheme (MCS) selection, resource block (RB) assignment and the block error ratio (BLER) determination and segment adaptation scheme to satisfy dynamic adaptive streaming over HTTP (DASH) clients over wireless networks. First, the authors define a utility function as the continuous playback time that the packets scheduled with the allocated resource can support. Then, the authors formulate the MCS selection, RB assignment and BLER determination into a mathematical model. The relationship among the above three factors can be explored instead of performing MCS selection and RB assignment with the fixed BLER. By decomposing the original problem into some sub-problems, the authors can get a solution to the original problem with low complexity. At the client level, the authors develop an adaptive segment request strategy based on the playback information, the segments' characteristics and the estimated transmission rate. To decrease the influence of the inaccurate estimations, an adaptive guard time interval based on the real playback information and transmission information of the previous segments is introduced. Simulation results show that the proposed algorithm can efficiently improve playback continuity and video quality over the existing algorithms. [ABSTRACT FROM AUTHOR]

In this study, an energy-efficient optimisation scheme for a large-scale multiple-antenna system with wireless power transfer (WPT) is presented. In the considered system, the user is charged by a base station with a large number of antennas via downlink WPT and then utilises the received power to carry out uplink data transmission. Novel antenna selection, time allocation and power allocation schemes are presented to optimise the energy efficiency of the overall system. In addition, the authors also consider channel state information cannot be perfectly obtained when designing the resource allocation schemes. The non-linear fractional programming-based algorithm is utilised to address the formulated problem. Their proposed schemes are validated by extensive simulations and it shows superior performance over the existing schemes. [ABSTRACT FROM AUTHOR]

Recently, a considerable research interest has grown up in the millimetre wave wireless system as the most promising technologies in the next generation communication. Since high-frequency channels of the millimetre wave are easily attenuated in space, beamforming technology relying on the massive multi-input-multi-output system is introduced to transmit the millimetre wave in a very narrow directional beam, so as to greatly improve the transmit efficiency. Then a challenging problem lies in that how to optimise the overall throughput by allocating the antenna resources to different mobile users in the massive MIMO antenna system. In this study, the authors handle such a difficult problem in two different cases. They first begin with the one-direction case, i.e. all sub-arrays are deployed in several parallel rows along the edge of a rectangle antenna array. They decompose the problem and solve it gradually. Then they generalise the authors' result to the two-dimensional case, where the sub-arrays can be deployed in orthogonal directions. They apply the similar scheme, decompose the problem and solve each sub-problem progressively. Both NP-hard problems are solved with time efficient approximation algorithms. Simulation results demonstrate the efficiency of the proposed algorithms in different cases. [ABSTRACT FROM AUTHOR]

Joint link and power allocation for device-to-device (D2D) communication underlaying cellular networks are necessary to coordinate the mutual interference. Most of the works consider the sum interference from D2D pairs. However, mobile devices are carried by human beings who are connected with social ties. For one cellular user, the strengths of the social ties with D2D pairs are different. Thus, his interference tolerance temperatures (ITTs) are various. In this study, we distinguish D2D pairs through the social ties, and propose a social aware pricing-based joint link and power allocation scheme. Specifically, the base station (BS) prices the interference on every link and updates the prices according to the ITTs of cellular users. Subsequently, with the prices, the competition of D2D pairs for the links is modelled as a non-cooperative game. The authors prove the existence and the uniqueness of the Nash equilibrium (NE), and demonstrate that the NE is Pareto optimal. Moreover, an iterative decentralised algorithm is designed to solve the game. Especially, if the number of the price on one link for one D2D pair being updated is up to an upper bound, the power is forced to be 0. Numerical simulations verify the effectiveness of the authors' proposed scheme. [ABSTRACT FROM AUTHOR]

Despite extensive studies on optimal power allocation, how to design an efficient joint user scheduling and power allocation scheme for uplink multiuser networks remains largely unexplored. This study investigates joint opportunistic user scheduling and power allocation in uplink multiuser networks to maximise user throughput subject to the power and resource sharing constraints. By exploiting the cumulative distribution function-based scheduling method, the authors first characterise the optimal power allocation subject to both long-term and short-term power constraints. Instead of calculating the transmit power in an iterative and central manner, users can independently decide their instantaneous transmit power in the proposed scheme, which facilitates the algorithm implementation for each user in uplink networks. The closed-form throughput of the proposed scheme is also derived, which can provide an efficient way to estimate and evaluate user performance. Numerical results reveal that compared with several benchmark schemes, the proposed scheme improves throughput performance significantly. [ABSTRACT FROM AUTHOR]

The rapid development of wireless communication comes at the cost of dramatically increasing energy consumption, which makes the optimisation of energy efficiency (EE) extremely important. In this study, the authors focus on maximising EE of large-scale multiple-input--multiple-output system by optimising the transmit power and the number of selected base station antennas. First, the EE optimisation problem, i.e. finding the optimal EE-based values of each parameter, when the other one is fixed, is investigated. Then, the optimisation problem jointly considering these two parameters to maximise EE is studied. Moreover, the EE optimisation problem is extended to multi-user (MU) system. Owing to the formulation of EE, the EE optimisation problem is reformulated as the fractional programming problem, and the Dinkelbach method is proposed to deal with this problem instead of the binary search algorithm (BSA) by switching the objective function to a weighted sum of rate and power. Simulation results show that the EE can be improved with the proposed Dinkelbach method, and it has a faster convergence speed than the BSA without any performance loss for both single-user system and MU system. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a user association scheme with quality of service support for load balancing in heterogeneous cellular networks (HCNs), which jointly considers user's achievable rate and load level of each BS instead of only utilising the former. To reveal how HCNs should self-organise, the authors formulate it as a network-wide weighted utility maximisation problem. Note that the formulated problem is a non-linear mixed-integer one, and its optimal solutions may be very difficult to be found when it is large-scale. To solve the proposed problem, the authors design a low-complexity distributed algorithm via dual decomposition. Numerical results show that, compared with the range expansion association (REA) and best power association (BPA), the strategy has a higher load balancing level (LBL) and a lower call blocking probability (CBP). Meanwhile, the proposed algorithm occupies a very fast convergence rate when its parameters are set properly. [ABSTRACT FROM AUTHOR]

This study investigates sensing-based spectrum sharing access (SSSA) and sensing-based spectrum opportunistic access (SSOA) schemes in cooperative orthogonal frequency division multiplexing (OFDM)-based cognitive radio networks with imperfect spectrum sensing. The optimal resource allocation strategy, including sensing time and transmit power, is designed to maximise the ergodic throughput of the secondary system. For a two-hop cooperative communication in the secondary system, the authors adopt the amplify-and-forward relay protocol and enable the source and relay to sense the state of the primary user jointly. To protect the PU effectively from harmful interference, they consider the average interference power constraint in each hop. The total average transmit power constraint of the source and relay is considered. Two simplified versions for the SSSA and SSOA schemes are employed because of the complexity of the problem. They then propose two algorithms that acquire the optimal sensing time and power allocation for both schemes. Finally, simulation results are presented to compare the performance of the two schemes. [ABSTRACT FROM AUTHOR]