This paper investigates energy-minimal data collection from multiple low-power ground devices (GDs) adopting a rotary-wing unmanned aerial vehicle (UAV). Unlike most prior works that assume flexible trajectories, the UAV in our study adheres to a fixed or predetermined flight path, due to practical requirements from e.g. patrol and inspection missions. To improve communication performance and prolong GDs’ operational lifetime, the UAV employs a directional antenna for wirelessly transferring energy to the GDs before collecting their data. We jointly optimize the UAV’s flight speeds, hovering locations, and radio resource allocation along the predefined flight path to minimize the UAV’s total energy consumption. For acyclic flight paths, we show that the UAV’s propulsion energy consumption is a strictly convex function of flight speed. However, the fixed path imposes a stringent nonconvex constraint, complicating the optimization. To overcome this challenge, we decompose the problem into two layers and solve it by proposing a novel monotonic optimization method in polar coordinates, referred to aspolar polyblock approximation. This method guarantees a globally optimal solution under mild conditions. Additionally, we propose a low-complexity suboptimal algorithm to balance system performance and computational efficiency. Simulation results show that both the proposed optimal and suboptimal algorithms can effectively mitigate the limitation of a fixed flight path, resulting in significant reductions in the UAV’s energy consumption during data collection, with more energy savings achieved as antenna directivity increases.

The Internet-of-Things (IoT) enables the connection of myriad wireless devices, generating a massive influx of data that can overwhelm central servers. Federated learning (FL) mitigates these issues by distributing computation tasks across edge devices, thus preserving data privacy by eliminating the need for raw data transmission. However, when deployed in large-scale IoT networks, FL encounters several challenges such as device energy constraints, heterogeneous network conditions, and the straggler effect, which inevitably impedes model convergence. In response, this paper proposes a semi-decentralized hybrid FL (SDHFL) scheme leveraging an unmanned aerial vehicle (UAV) as a mobile data center to collect data from distributed IoT clusters. To further expedite FL convergence, we formulate a non-convex mixed-integer nonlinear programming (MINLP) problem to minimize overall completion time while ensuring quality of service (QoS) requirements, considering both the UAV's energy constraints and network stability. We demonstrate that efficient learning is achieved by optimizing both power allocation and device computational capability. Furthermore, we prove the convergence of the proposed SDHFL scheme and derive the minimum number of global iterations required. Exploiting these insights, we introduce a low-complexity suboptimal algorithm for dynamic cluster selection and resource allocation optimization, leveraging Lyapunov optimization theory to obtain optimal solutions efficiently, demonstrating its scalability for large-scale networks. Our simulation results validate the effectiveness of the proposed SDHFL framework, revealing a non-trivial tradeoff where the overall completion time initially decreases and then increases as the number of devices or clusters grows, indicating the necessity of optimizing both cluster counts and intra-cluster device allocations. Furthermore, compared to several baseline schemes, our proposed optimal algorithms significantly reduce overall completion time and enhance model convergence, demonstrating the potential of SDHFL for enabling efficient and scalable FL in IoT networks.

Dense wireless local area networks (WLANs) have been developed to enable high-capacity indoor wireless communications. Nevertheless, due to the inherent interference among densely deployed access points (APs) and the competitive channel access scheme, the balance of network load among APs may degrade significantly. This challenge motivates the development of a novel association policy exploiting the integrated sensing and communication (ISAC) technique to enhance network fairness. In this paper, we propose a novel optimization framework for ISAC resource allocation in the downlink of dense WLANs to maximize the total network fairness utility function while guaranteeing users' quality-of-service (QoS) requirements. Unlike conventional association policies, our proposed approach effectively determines the associated APs based on signal-to-noise ratio (SNR) and measured angle of sensing signals to ensure network fairness. By leveraging coalition game and binary relaxation techniques, we further transform the non-convex resource allocation design problem and address them via the alternating optimization (AO) technique. Simulation results demonstrate that the proposed ISAC-based resource allocation framework can effectively improve the data rate over the network and, simultaneously ensure fairness among stations.

This paper studies resource allocation for a reconfigurable intelligent surface (RIS)-assisted mixed near- and far-field communication utilizing a directional antenna. The resource allocation algorithm is designed as a non-convex optimization problem to maximize the data rate in the communication system. To circumvent the problem intractability, the non-convex problem is transformed into a standard semidefinite relaxation (SDR) programming problem by exploiting radiation field electromagnetic theory. This allows us to characterize the solution structure of the joint height and axis of the directional antenna and the RIS phase shift matrix that facilitates the design of an efficient iterative algorithm for obtaining the solution. We reveal that the maximal data rate of RIS-assisted mixed near- and far-field communication is achieved if the axis of the directional antenna aligns the plane that includes both the center of the RIS and the UE's location. Simulation results demonstrate a significant data rate improvement with the proposed resource allocation compared to the two baseline schemes.

Unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) communication presents a promising solution for high data-rate wireless applications in urban environments. However, due to limited energy supply and communication capability, UAVs can only provide temporary communication services. This challenge motivates the exploration of three-dimensional (3D) integrated aerial and ground mmWave communications utilizing the multi-connectivity (MC) technique. By leveraging both ground and aerial mmWave links over licensed and unlicensed mmWave spectrums, respectively, the MC technique can effectively exploit spatial and frequency diversity to enhance the connectivity and reliability of UAV-assisted mmWave communications. We develop a unified framework based on stochastic geometry and Markov chain to analyze the coverage and outage probabilities of the 3D integrated aerial/ground mmWave networks. Furthermore, we show that an optimal UAV flight altitude for maximizing the coverage probability of UAV communication exists and derive it in a closed-form expression. Simulation results demonstrate that UAVs can maintain reliable mmWave connections even when connections from terrestrial mmWave base stations (BSs) are obstructed by buildings, underscoring the benefits of MC in enhancing the robustness of 3D integrated aerial and ground mmWave networks.

Federated learning (FL) over wireless networks offers a promising approach to enable decentralized machine learning among massive mobile edge nodes while ensuring privacy in training data. However, the convergence speed of FL is limited by the straggler effect, which arises from heterogeneous nodes, wireless fading channels, and non-independently and identically distributed (non-IID) training data. In this paper, we consider an adaptive semi-asynchronous FL to mitigate the straggler effect, by dynamically selecting subsets of nodes over time to synchronize the global model. We jointly optimize the node scheduling and computing/communication resource allocation to minimize the completion time required for convergence of the adaptive semi-asynchronous FL. Leveraging the convergence condition of semi-asynchronous FL, we further propose a greedy heuristic policy for node scheduling while tackling the remaining computing/communication resource allocation problem by exploiting a hidden convexity. Simulation results on open datasets demonstrate that, compared with existing FL algorithms, our proposed adaptive semi-asynchronous algorithm can significantly lower the latency of FL convergence.

Fiber-to-the-room (FTTR) wireless local area networks (WLANs) are a promising sixth-generation (6G) technology for extreme broadband low-latency indoor wireless communications. With dense deployment of access points (APs), namely optical network units (ONUs), and efficient spatial frequency reuse across the ONUs, FTTR WLANs enable the mobile devices to flexibly access any ONU in its communication range and reduce the collisions during data packet transmissions. However, FTTR WLANs share a passive optical network (PON) for time division multiplexing (TDM) based backhauling, which may incur long delays for scheduling packet transmissions over the PON. In this paper, the full-link age of information (FL-AoI) is proposed as a new performance metric to analyze the timeliness of indoor communications in FTTR WLANs, taking into account both the carrier sense multiple access with collision avoidance (CSMA/CA) based wireless transmission and the TDM based packet scheduling over the PON. The FL-AoI of FTTR WLANs is analyzed using stochastic geometry and stretched exponential path-loss (SEPL) based indoor wireless channel model. We show that rather than accessing the nearest ONUs, the FTTR WLAN also enable the mobile devices to access further ONUs to reduce the average FL-AoI. Meanwhile, there exists an optimal transmission distance to achieve minimal average FL-AoI in the FTTR WLAN, whose value depends on the deployment density of ONUs.

This paper considers optimal resource allocation for data collection from multiple ground devices (GDs) using a rotary-wing unmanned aerial vehicle (UAV). The UAV’s flight path, i.e., the sequence of moving positions, is given a priori due to requirements of e.g. patrol and inspection missions, whereas the UAV’s trajectory, i.e., the path and time schedule of movement, remains dependent on its hovering positions and flying speeds along the path. To improve the spectral and energy efficiency of the GDs, the UAV employs a directional antenna and performs wireless power transfer (WPT) to the GDs before collecting data from them. We jointly optimize the UAV’s flying speeds, hovering locations, and radio resource allocation (including time, bandwidth and transmit power) for minimization of the total energy consumption of the UAV required for completing data collection along the flying path. We show that given any flight path, the propulsion energy consumption of the UAV is a convex function of the flight speeds. However, due to the highly directive transmission, communication and flight of the UAV become strongly coupled and complicates the problem, e.g. the selection of the UAV’s hovering points will affect both the order of serving the GDs and the antenna gain of the UAV. Moreover, nonconvexity in the flight path constraints further obscures an efficient solution to the resource allocation problem. To tackle these challenges, we propose an iterative algorithm based on the branch-and-bound (BnB) method, which can obtain the globally optimal solution when the flight path coincides with the boundary of a convex set. Simulation results show that compared with several baseline algorithms, the proposed algorithm can significantly lower the energy consumption of the UAV during data collection.

This paper presents a stochastic geometry framework for modeling and evaluating the connectivity of uplink transmission in a large-scale intelligent reflecting surface (IRS) assisted millimeter-wave (mmWave) communication network, where the uplink user equipments (UEs) attempt to communicate with the nearest base stations (BSs) either without or with the help of an IRS. We propose a novel elliptical geometry model, which can effectively capture the impact of IRS location and orientation, as well as incident/reflection angle on mmWave signal propagation, while, at the same time, significantly simplifying the analysis of the system performance. Employing the elliptical geometry model, the approximate reflection probability of IRS as well as its upper and lower bounds are derived in closed form. Based on these results, we further analyze the successful connection probability of uplink UEs for IRS-assisted mmWave cellular networks. Our results show that compared with conventional direct UE-to-BS communication without IRS, indirect communication with the aid of IRS exhibits a slower decaying in the connection probability as the communication distance increases, as the latter can significantly increase the connection probability for cell-edge UEs. Moreover, for mmWave BSs with small receiving power thresholds, the deployment of IRS can effectively mitigate the impact of blockages to improve mmWave signal propagation.

The blockchain is a promising technology to enhance the security and resilience of industrial Internet of Things (IIoT) networks. However, generating blockchain for the IIoT devices usually consumes excessive energy which may not be affordable for battery-powered IIoT devices. To address this problem, in this paper, we consider an unmanned aerial vehicle (UAV) assisted private blockchain-based IIoT system. Thereby, a UAV mounted with computing processor is deployed as a multi-access edge computing platform, which is responsible for collecting data from the IIoT devices, generating blocks based on the collected data, and broadcasting the blocks to the IIoT devices. To minimize the energy consumption of the UAV, joint optimization of the central processing unit (CPU) frequencies for data computation and block generation, the amount of offloaded IIoT data, the bandwidth allocation, and the trajectory of the UAV is formulated as a nonconvex optimization problem and solved via a successive convex approximation (SCA) algorithm. Simulation results show that, compared with several baseline schemes, the proposed scheme can significantly lower the energy consumption required for the blockchain generation in IIoT networks.

Solar-powered unmanned aerial vehicles (SUAVs) are a promising solution to increase the flight time of unmanned aerial vehicles (UAVs) in the sky, reducing human interventions for battery charging. Exploiting networked SUAVs for providing long-duration wireless communication cannot only improve the signal transmission reliability but realize energy autonomy. To reap these benefits, in this article, we propose an efficient energy and radio resource management framework based on intelligent power cognition at the SUAVs. Thereby, power-cognitive SUAVs can learn the environment including the spatial distributions of solar energy density, the channel state evolution, and the traffic patterns of wireless communication applications in adaption to the environment changes. These SUAVs intelligently adjust the energy harvesting, information transmission, and flight trajectory to improve the utilization of solar energy for two primary goals: staying aloft over a long time period and achieving high communication performance. We adopt reinforcement learning to compute the optimal decisions for maximization of the total system throughput within the lifetime of the SUAV. Simulation results show that the proposed power cognition scheme can simultaneously improve the communication throughput and the harvested energy for SUAVs.

In this paper, the concept of crowdsourcing is applied to the medical field and a health monitoring mechanism based on photo crowdsourcing is proposed. Specifically, with photo crowdsourcing by many participators, the routine circumstances of users may be represented. However, these photos may include other people than the user, such as the visibility requestor, the invisibility requestor, and the passerby. The visibility and invisibility requestor are the participators in the system, whose identity can be set as visible or invisible, while the passerbys do not participate in the system. Hence, a privacy protection mechanism is proposed for this system, which includes two categories: i) The image fuzzy processing is provided for the invisibility requestor, while the original image is reserved for the visibility requestor. ii) The passerby's image is directly fuzzy processed for privacy protection.

In this work, we develop a tractable modeling paradigm for outage probability on device-to-device (D2D) enhanced K-tier heterogeneous cellular network (HCN). To offload traffic in the HCNs, mobile user equipments (UEs) can engage in D2D communications, whose activity is determined by a carrier sensing multiple access (CSMA) scheme. Due to the limited transmit power, the UEs employ a truncated channel inversion power control policy with a cutoff threshold and we show that there exists an optimal threshold for maximizing the probability of success D2D communication. We obtain the expressions for truncation outage probability and signal-to- interference-plus-noise-ratio (SINR) outage probability in the D2D enhanced HCNs. In addition, we evaluate how the D2D communications impact on the system performance.

Exchange of crucial and confidential information leads to the unprecedented attention on the security problem in wireless networks. Even though the security has been studied in a number of works, the joint optimization of the physical layer security and the end-to-end delay management, which requires a meticulous cross-layer design, has seldom been evaluated. In this paper, by combining the tools from stochastic geometry and queueing theory, we analyze the tradeoff between the delay and the security performance in large wireless networks. We further propose a simple transmission mechanism which splits a message into two packets and evaluate its effect on the mean delay and the secrecy outage probability. Our numerical results reveal that the security performance is better for larger path loss exponent when the density of legitimate nodes is large, and it is reverse when the density is small. Moreover, it is observed that by introducing the simple mechanism of message split, the security performance is greatly improved in the backlogged scenario and slightly improved in the dynamic scenario when the density of legitimate transmitters is large. In summary, this paper provides an understanding and a rule-of-thumb for the practical design of wireless networks where both the delay and the security are key concerns.

This paper studies resource allocation for energy-efficient device-to-device (D2D) communications in an overlay wireless cellular networks, where a D2D transmitter first harvests energy from a base station (BS) and then communicates with a D2D receiver. The resource allocation algorithm design is formulated as a non-convex optimization problem for the maximization of the energy efficiency (bits/Joule) of the D2D system. The proposed problem formulation takes into account the minimum required harvested energy, maximum duration of signal transmission, and minimum required system throughput. Exploiting fractional programming theory, we transform the non-convex problem into a standard convex optimization problem. This allows us to characterize the optimal solution structure of joint time, frequency, and power allocation and to derive an efficient iterative algorithm for obtaining the optimal solution. We reveal that the optimal energy-efficient D2D communication in this framework should occur if the D2D transmitter consumes all the harvested energy from the BS. Simulation results demonstrate the energy efficiency improvement brought by the proposed optimal resource allocation compared to two baseline schemes.

Because of a scarcity of bandwidth due to explosive growth of mobile devices in 5G, offloading computing workload to WiFi devices can be a feasible solution. In addition, for reasons of battery capacity limitation of mobile device, wireless energy harvesting technologies play an important role in case the battery runs out in the middle of offloading processes. Thus, in this paper, to reduce both the problems, we propose the selective offloading approach for 5G mobile users to reduce execution time of a task considering proper wireless energy harvesting technology. The experimental results have shown the superiority of the proposed approach.

Energy efficiency of uplink users is critical for evaluating the energy consumption of heterogeneous cellular networks (HCNs) with heavy loading. The energy efficiency of user equipments (UEs) in HCNs depends heavily on power control scheme as well as maximum power constraint. This paper investigate the energy efficiency of uplink UEs in HCNs taking into account the flexible power control policy along with maximum transmission power limitation. We show that there exists a transfer path loss in the uplink energy efficiency which depends on the density of BS λ, threshold of receiver ρ0and power control coefficient ε. The energy efficiency of users, which have small path loss to the BS, highly depends on the values of path loss and power control coefficient. On the other hand, the energy efficiency of users with large path loss is impacted by path loss and power control coefficient independently. We investigate the bound of transfer path loss and simulation results show the uplink energy efficiency affected by system parameters, such as the density of BS and power control coefficient.

This paper evaluates the energy efficiency of uplink transmission in heterogeneous cellular networks (HetNets), where fractional power control (FPC) is applied at user equipments (TIEs) subject to a maximum transmit power constraint. We first consider an arbitrary deterministic HetNet and characterize the properties of energy efficiency for TIEs in different path loss regimes, or different access regions. By introducing the notion of transfer path loss, we reveal that, for TIE whose path loss is below the transfer path loss, its energy efficiency highly depends on the value of power control coefficient adopted by FPC. In contrast, for TIE with path loss above the transfer path loss, the uplink energy efficiency asymptotically decreases inversely with path loss, independent of the adopted power control coefficient. Based on these properties, we characterize the optimal power control coefficients for maximizing the energy efficiency of FPC in different access regions. Next, we extend the analysis to stochastic HetNets where TIEs and BSs are distributed as independent Poisson point processes, and investigate the distribution of transmit power for uplink TIEs. Moreover, the probability of truncation outage due to constrained maximal transmit power, as well as the average energy efficiency of TIEs are analytically derived as functions of the BS and TIE densities, power control coefficient, and receiver threshold. Simulation results validate the analytical results, show the consistency between deterministic and stochastic analyses, and suggest suitable power control coefficient for achieving energy efficient uplink transmission by FPC in HetNets.

Dense heterogeneous cellular networks (HCNs) with energy harvesting nodes are promising solutions to meet both the capacity and energy efficiency needs in the next generation cellular networks. This paper studies the system performance of both energy harvesting and information receiving in a K-tier cellular networks with time-switching simultaneous wireless information and power transfer (SWIPT). Based on stochastic geometry theory, we derive the closed-form formulas for the average harvested energy, the average transmission rate and the total power and information gains. Moreover, we propose a scheme that can optimally adjust the bias factors of tier selection while leveraging the system between energy harvesting and information receiving.

In this paper, the average uplink transmission rate and outage probability of UEs are analyzed in heterogeneous small cell networks. The impact of the power control coefficient on both system performances is evaluated taking into account the intercell interference in a stochastic geometry network. Furthermore, we propose an energy-efficient uplink power control scheme, which adjusts the power control coefficient for maximizing the network energy efficiency subject to a QoS constraint in terms of maximum outage probability requirement. Numerical results demonstrate that the proposed power control schemes can improve the network throughput and energy efficiency dramatically.

In practical mobile communication engineering applications, surfaces of antenna array deployment regions are usually uneven. Therefore, massive multi-input-multi-output (MIMO) communication systems usually transmit wireless signals by irregular antenna arrays. To evaluate the performance of irregular antenna arrays, the matrix correlation coefficient and the ergodic received gain are defined for massive MIMO communication systems with mutual coupling effects. Furthermore, the lower bound of the ergodic achievable rate, symbol error rate, and average outage probability is first derived for multi-user massive MIMO communication systems using irregular antenna arrays. Asymptotic results are also derived when the number of antennae approaches infinity. Numerical results indicate that there exists a maximum achievable rate when the number of antennae keeps increasing in massive MIMO communication systems using irregular antenna arrays. Moreover, the irregular antenna array outperforms the regular antenna array in the achievable rate of massive MIMO communication systems when the number of antennae is larger than or equal to a given threshold.

We consider a cooperative two-path relay channel (TPRC) where a data source transmits new packets to a corresponding destination, with the assistance of two intermediate relays alternatively. When the transmitted source packet is successfully decoded by a relay, the relay proceeds to forward this packet in the subsequent time slot, otherwise it simply stays silent. Due to the inter-relay channels, the decoding result at a relay in the current time slot depends on the decoding result at the other relay in the previous time slot and not on that preceded it. In view of this property, we employ a Markov framework to analyze the decoding performance at the relays. The decoding of successive packets received at the destination can be similarly analyzed by using a Markov chain. Closed-form expressions of the outage probability are derived for TPRC by exploiting the properties of a Markov chain. Numerical results demonstrate that with the proposed scheme, a reasonably good performance is achieved with only a single-slot delay and relatively low complexity.

To satisfy the massive wireless traffic transmission generated by multimedia applications, the massive multi-input-multi-output (MIMO) wireless system has emerged as a possible solution for future 5G wireless communication systems. However, the mutual coupling effect of massive MIMO systems has a negative effect potential on the wireless capacity. In this paper, the receive diversity gain is first defined and analyzed for massive MIMO wireless systems. Furthermore, we propose an effective capacity with the mutual coupling effect and the quality of service (QoS) statistical exponent constraint for multimedia massive MIMO wireless systems. Based on numerical results, the effective capacity approaches the effective capacity with the antenna spacing of d = 0.5λ when the antenna spacing is increased in the massive MIMO antenna array.

In this paper, the uplink outage probability and energy efficiency of user terminals are investigated for heterogeneous networks (HetNets). In order to characterize the performance of users in small cells, the probability distribution function (PDF) of the distance between a user terminal and the access point (AP) in the typical small cell is derived in closed-form expressions. On this basis, the signal-to-interference ratio (SIR) of uplink terminals is analyzed to evaluate the performance with APs turning on in small cells. Simulation results show that user's density in small cells has a great impact on the uplink energy efficiency. These results provide some guidelines for developing new energy saving schemes in practical HetNet deployments.

A multiuser massive MIMO system with mutual coupling is investigated in finite-dimensional channel scenarios. The uplink ergodic achievable rate is analytically derived for a multiuser massive MIMO system equipped with a rectangular planar uniform antenna array at the base station (BS). Numerical results show that the mutual coupling effect reduces the uplink achievable rate of multiuser massive MIMO systems when the antenna distance decreases. But when the size of the antenna array is fixed, the uplink achievable rate increases with the growing of antenna number.

It is of great importance for service providers to evaluate the impact of inter-cell co-channel interference on the achievable data rate in cellular networks. In this paper, the cumulative distribution function (CDF) of the inter-cell interference factor is derived. On this basis, closed-form expression of the maximum achievable rate subject to the interference from finite planar cells is analyzed over Nakagami-m fading uplink channels. Numerical results show that besides Nakagami-m fading severity parameters and path loss coefficient, the maximum achievable rate is also affected by the number of interference cells. According to the numerical results, with an increase in the number of interference cells, the maximum achievable rate is degraded.

Recently, issues of power consumption at base stations (BSs) in wireless cellular networks have attracted great interest in both research communities and the industry. In this paper, we investigate the BS power consumption in multiple-input single-output (MISO) Poisson-Voronoi tessellation (PVT) random cellular networks. Taking into account the inter-cell interference, the impact of the traffic demands of users and the spatial traffic intensity on the BS power consumption are jointly considered for MISO PVT random cellular networks. Furthermore, the power consumption required at the BS in a typical PVT cell is modeled through characteristic functions. Simulation results are employed to evaluate the BS power consumption and the performance of the random cellular networks.

It is of great importance and interests for network operators to evaluate the impact of inter-cell co-channel interference on the achievable data rate in cellular networks. In this paper, a closed-form maximum achievable rate over Nakagami-m fading uplink channels based on multicell linear Wyner model is derived. Moreover, the maximum achievable rates in some special cases are investigated. Numerical results show that the maximum achievable rate decreases with the increase of the inter-cell signal interference factor and is also affected by Nakagami-m fading severity parameters and number of users in a cell.

Cooperative communication technologies can improve the system throughput energy efficiency and reliability in dynamic wireless networks. For practical multi-cell multi-antenna mobile cellular networks, co-channel interference is a critical issue affecting cooperative transmission (Co-Tx) performance. In this paper, we first derive a cooperative outage probability model and a cooperative block error rate (BLER) model incorporating a binary differential phase shift keying modulation for performance analysis in such cooperative cellular networks. Based on them, a cooperative energy efficiency model is proposed and analyzed under different Co-Tx scenarios, interference levels and wireless channel conditions. As demonstrated by numerical results, our analytical models show that Co-Tx is an effective approach to mitigate co-channel interference and improve the energy efficiency, BLER and overall outage probability performance in multi-cell multi-antenna cooperative cellular networks.

Motivated by the demand for energy efficiency improvement in mobile communication industry, we explore an idea of optimizing energy efficiency for MIMO-OFDM wireless communication systems while maintaining users' quality of service (QoS) requirement. Based on the binary power control scheme,a power allocation criterion for energy efficiency optimization is derived under the total power constraint. From a bit error rate (BER) point of view, a protection constraint is configured to guarantee the system QoS. With the aim of energy efficiency optimization under QoS guarantee in MIMO-OFDM wireless communication systems, an energy-efficient binary power control with BER constraint (EBPCB) algorithm is proposed based on the power allocation criterion and QoS constraint. Simulations results demonstrate the energy efficiency improvement of EBPCB.

In the above titled paper (ibid., vol. 7, no. 2, pp. 329-332, Apr. 10), we omitted the sources of funding for our work. That is provided here. We also add an additional author, Xiaohu Ge, who assumes now the role of corresponding author.