This paper studies a new multi-agent deep reinforcement learning (MADRL) approach for unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks, where UAV-mounted servers provide offloading services to mobile users (MUs). We aim to minimize the total energy consumption of MUs by optimizing UAV mobility, UAV-MU association, resource allocation, and task offloading ratios. In the multi-UAV scenario, we model the MEC network as a multi-agent partially observable Markov decision process (POMDP), where each UAV agent operates with limited information for decentralized decision-making. Conventional MADRL methods manually design such UAV interaction messages, thereby incurring performance degradation. To address this issue, we propose a new neural network (NN)-based UAV interaction mechanism that generates autonomously task-oriented messages to minimize energy consumption. Such message-generating NNs are developed under the MADRL framework, which allows for joint optimization of UAV interactions and decentralized decisions in an end-to-end manner. Numerical results demonstrate that our approach outperforms traditional MADRL methods and achieves performance close to ideal centralized schemes while maintaining scalability with varying UAV numbers.

Future wireless networks are expected to incorporate diverse services that often lack general mathematical models. To address such black-box network management tasks, the large language model (LLM) optimizer framework, which leverages pretrained LLMs as optimization agents, has recently been promoted as a promising solution. This framework utilizes natural language prompts describing the given optimization problems along with past solutions generated by LLMs themselves. As a result, LLMs can obtain efficient solutions autonomously without knowing the mathematical models of the objective functions. Although the viability of the LLM optimizer (LLMO) framework has been studied in various black-box scenarios, it has so far been limited to numerical simulations. For the first time, this paper establishes a theoretical foundation for the LLMO framework. With careful investigations of LLM inference steps, we can interpret the LLMO procedure as a finite-state Markov chain, and prove the convergence of the framework. Our results are extended to a more advanced multiple LLM architecture, where the impact of multiple LLMs is rigorously verified in terms of the convergence rate. Comprehensive numerical simulations validate our theoretical results and provide a deeper understanding of the underlying mechanisms of the LLMO framework.

This article presents a cooperative multiagent deep reinforcement learning (MADRL) approach for unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) networks. An UAV with computing capability can provide task offlaoding services to ground Internet of Things devices (IDs). With partial observation of the entire network state, the UAV and the IDs individually determine their MEC strategies, i.e., UAV trajectory, resource allocation, and task offloading policy. This requires joint optimization of decision-making process and coordination strategies among the UAV and the IDs. To address this difficulty, the proposed cooperative MADRL approach computes two types of action variables, namely, message action and solution action, each of which is generated by dedicated actor neural networks (NNs). As a result, each agent can automatically encapsulate its coordination messages to enhance the MEC performance in the decentralized manner. The proposed actor structure is designed based on graph attention networks such that operations are possible regardless of the number of IDs. A scalable training algorithm is also proposed to train a group of NNs for arbitrary network configurations. Numerical results demonstrate the superiority of the proposed cooperative MADRL approach over conventional methods.

This paper presents a flexible deep learning strategy that tackles decentralized optimization tasks in multi-tier networks where wireless nodes are deployed in a hierarchical structure. Practical multi-tier networks have arbitrary node populations as well as their backhaul connections. Thus, node operations in the multi-tier network request versatile inference rules for arbitrary network configurations. To this end, we present a tree-based learning strategy which transforms the multi-tier network optimization into a collaborative inference process over random trees. For the decentralized structure, each node in a tree is equipped with dedicated deep neural network (DNN) modules. A group of these component DNNs builds a tree deep neural network (TNN) where forward pass calculations define the node interaction policy. The TNN is carefully designed such that it can be universally applied to random trees. The training mechanism is developed to involve a number of random tree instances so that the TNN can be generalized to arbitrary network configurations. As a consequence, the TNN can scale up with a large number of nodes which requires only a single training process. The scalability of the proposed framework is validated for various multi-tier network optimization problems. Numerical results demonstrate the effectiveness of the TNN over existing approaches.

This article studies task-oriented edge networks where multiple edge Internet of Things nodes execute machine learning tasks with the help of powerful deep neural networks (DNNs) at a network cloud. Separate edge nodes (ENs) result in a partially observable system where they can only get partitioned features of the global network states. These local observations need to be forwarded to the cloud via resource-constrained wireless fronthual links. Individual ENs compress their local observations into uplink fronthaul messages using task-oriented encoder DNNs. Then, the cloud carries out a remote inference task by leveraging received signals. Such a distributed topology requests a decentralized training and decentralized execution (DTDE) learning framework for designing edge-cloud cooperative inference rules and their decentralized training strategies. First, we develop fronthaul-cooperative DNN architecture along with proper uplink coordination protocols suitable for wireless fronthaul interconnection. Inspired by the nomographic function, an efficient cloud inference model becomes an integration of a number of shallow DNNs. This modulized architecture brings versatile calculations that are independent of the number of ENs. Next, we present a decentralized training algorithm of separate edge-cloud DNNs over downlink wireless fronthaul channels. An appropriate downlink coordination protocol is proposed, which backpropagates gradient vectors wirelessly from the cloud to the ENs. Numerical results demonstrate the viability of the proposed DTDE framework for optimizing task-oriented edge networks.

A sequential fronthaul network, referred to as radio stripes, is a promising fronthaul topology of cell-free MIMO systems. In this setup, a single cable suffices to connect access points (APs) to a central processor (CP). Thus, radio stripes are more effective than conventional star fronthaul topology which requires dedicated cables for each of APs. Most of works on radio stripes focused on the uplink communication or downlink energy transfer. This work tackles the design of the downlink data transmission for the first time. The CP sends compressed information of linearly precoded signals to the APs on fronthaul. Due to the serial transfer on radio stripes, each AP has an access to all the compressed blocks which pass through it. Thus, an advanced compression technique, called Wyner-Ziv (WZ) compression, can be applied in which each AP decompresses all the received blocks to exploit them for the reconstruction of its desired precoded signal as side information. The problem of maximizing the sum-rate is tackled under the standard point-to-point (P2P) and WZ compression strategies. Numerical results validate the performance gains of the proposed scheme.

Deep learning (DL) techniques have been intensively studied for the optimization of multi-user multiple-input single-output (MU-MISO) downlink systems owing to the capability of handling nonconvex formulations. However, the fixed computation structure of existing deep neural networks (DNNs) lacks flexibility with respect to the system size, i.e., the number of antennas or users. This paper develops a bipartite graph neural network (BGNN) framework, a scalable DL solution designed for multi-antenna beamforming optimization. The MU-MISO system is first characterized by a bipartite graph where two disjoint vertex sets, each of which consists of transmit antennas and users, are connected via pairwise edges. These vertex interconnection states are modeled by channel fading coefficients. Thus, a generic beamforming optimization process is interpreted as a computation task over a weighted bipartite graph. This approach partitions the beamforming optimization procedure into multiple suboperations dedicated to individual antenna vertices and user vertices. Separated vertex operations lead to scalable beamforming calculations that are invariant to the system size. The vertex operations are realized by a group of DNN modules that collectively form the BGNN architecture. Identical DNNs are reused at all antennas and users so that the resultant learning structure becomes flexible to the network size. Component DNNs of the BGNN are trained jointly over numerous MU-MISO configurations with randomly varying network sizes. As a result, the trained BGNN can be universally applied to arbitrary MU-MISO systems. Numerical results validate the advantages of the BGNN framework over conventional methods.

This paper studies a deep reinforcement learning (DRL) approach for the unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks where a UAV-mounted server offloads computation tasks of mobile users (MUs). We aim at minimizing the energy consumption of the MUs by adjusting UAV mobility, UAV-MU association, computation resource allocation, and task offloading rules. This requires an online and joint optimization of different types of variables constructing heterogeneous solution spaces. To realize real-time optimization strategies, we propose an online DRL method based on the twin-delayed deep deterministic policy gradient (TD3) framework. The joint optimization of heterogeneous action variables is tackled by a novel actor neural network that partitions the high-dimensional action set into several solution spaces. In addition, the proposed TD3 framework achieves adaptability to new task offloading requests through our proposed training and execution strategy. Numerical results verify the effectiveness of the proposed DRL architecture over benchmark schemes.

Autoencoder (AE) techniques have been intensively studied for the optimization of wireless transceivers. However, fixed computational structures of existing AE models lack the flexibility to the lengths of message bits and codewords. This work proposes a versatile AE framework, termed by autoencoding graph neural network (AEGNN), where both encoder and decoder are realized by GNNs. The viability of the proposed AEGNN is demonstrated in various application scenarios.

Future Internet of Things (IoT) communication trends toward heterogeneous services and diverse quality-of-service requirements pose fundamental challenges for network management strategies. In particular, multiobjective optimization (MOO) is necessary in resolving the competition among different nodes sharing limited wireless network resources. A unified coordination mechanism is essential such that individual nodes conduct the opportunistic maximization of heterogeneous local objectives for efficient distributed resource allocation. To such a problem, this article proposes an artificial intelligence (AI)-based framework, which is termed as MOO strategy for AI-aided IoT communications (MOSAIC). This framework enables to tackle numerous MOO tasks in IoT network management with simple reconfiguration of learning rules. In this strategy, a component unit associated with an individual network node includes a pair of deep neural networks (DNNs) to learn optimal local functions responsible for calculation and distributed coordination, respectively. The resultant AI module swarm called DNN tiles realizes the node cooperation that collectively seeks distributed MOO calculation rules. The advantage of MOSAIC is characterized by Pareto tradeoffs among conflicting performance metrics in diverse wireless networking configurations subject to severe interference and distinct criteria for multiple targets.

In conventional multi-user multiple-input multiple-output (MU-MIMO) systems with frequency division duplexing (FDD), channel acquisition and precoder optimization processes have been designed separately although they are highly coupled. This paper studies an end-to-end design of downlink MU-MIMO systems which include pilot sequences, limited feedback, and precoding. To address this problem, we propose a novel deep learning (DL) framework which jointly optimizes the feedback information generation at users and the precoder design at a base station (BS). Each procedure in the MU-MIMO systems is replaced by intelligently designed multiple deep neural networks (DNN) units. At the BS, a neural network generates pilot sequences and helps the users obtain accurate channel state information. At each user, the channel feedback operation is carried out in a distributed manner by an individual user DNN. Then, another BS DNN collects feedback information from the users and determines the MIMO precoding matrices. A joint training algorithm is proposed to optimize all DNN units in an end-to-end manner. In addition, a training strategy which can avoid retraining for different network sizes for a scalable design is proposed. Numerical results demonstrate the effectiveness of the proposed DL framework compared to classical optimization techniques and other conventional DNN schemes.

Random access (RA) or preamble collision is one of the crucial problems in massive internet-of-things (IoT) at the network entry stage. Since a massive number of IoT nodes simultaneously attempt RAs on the same physical random access channel (PRACH), preambles may be selected by multiple nodes, incurring preamble collisions at the first step of the RA procedure. However, conventional RA models are limited to binary preamble detections which poses severe RA performance loss in the massive IoT environment. In this paper, we propose a deep learning (DL)-based end-to-end RA framework which has detection and resolution abilities for the collided preambles. In particular, advanced preamble classification and timing advance (TA) classifications are performed using deep neural networks (DNNs) for improving the probability of RA success while reducing the delay of the entire RA procedure. The effectiveness of the proposed DNN-based preamble and TA classifiers are demonstrated through extensive simulations. We further evaluate the system-level performance of the proposed DL-based RA model. It shows a significantly higher probability of instant RA success, which makes every node succeed in RA with very limited reattempts, and also maintains a significantly lower RA delay in massive IoT environment.

This paper presents a machine learning strategy that tackles a distributed optimization task in a wireless network with an arbitrary number of randomly interconnected nodes. Individual nodes decide their optimal states with distributed coordination among other nodes through randomly varying backhaul links. This poses a technical challenge in distributed universal optimization policy robust to a random topology of the wireless network, which has not been properly addressed by conventional deep neural networks (DNNs) with rigid structural configurations. We develop a flexible DNN formalism termed distributed message-passing neural network (DMPNN) with forward and backward computations independent of the network topology. A key enabler of this approach is an iterative message-sharing strategy through arbitrarily connected backhaul links. The DMPNN provides a convergent solution for iterative coordination by learning numerous random backhaul interactions. The DMPNN is investigated for various configurations of the power control in wireless networks, and intensive numerical results prove its universality and viability over conventional optimization and DNN approaches.

In this work, we study a scenario where a distant central monitor covertly wiretaps the communication between a pair of suspicious users via several single-antenna full-duplex spoofing relays and a cooperative jammer for wireless surveillance. Under an adaptive transmission policy where the data rate of the suspicious users is determined based on the channel condition at the receiver, the spoofing relays intercept and forward manipulated information to control the data rate of the suspicious users for effective eavesdropping. We first propose an inter-relay interference suppression method to eliminate undesirable signals at the spoofing relays. Then, we provide a joint optimization technique for relay weights and jamming power to maximize the eavesdropping rate by a two-layer semi-definite relaxation approach. Simulation results show that the proposed solution outperforms other practical baseline schemes.

In this paper, we investigate simultaneous wireless information and power transfer (SWIPT) where a multi-antenna transmitter conveys information and energy simultaneously to a multi-antenna receiver equipped with time switching (TS) circuits for an energy harvesting (EH) mode and an information decoding (ID) mode. In contrast with conventional uniform TS (UTS) structure where all the receive antennas at the receiver apply a single TS circuit, to improve the SWIPT performance, we suggest a general dynamic TS (DTS) receiver architecture which consists of an individual TS circuit for each antenna. We aim to analyze the achievable rate-energy (R-E) tradeoff of the DTS system by jointly optimizing the covariance matrices at the transmitter and the time durations for the EH and the ID modes of the receive antennas. To determine the boundary points of the R-E region, we suggest the globally optimal algorithm for the rate maximization problem via convex optimization techniques. Numerical examples verify the efficacy of the proposed DTS over conventional UTS methods.

This paper studies a deep learning (DL) framework for the design of binary modulated visible light communication (VLC) transceiver with universal dimming support. The dimming control for the optical binary signal boils down to a combinatorial codebook design so that the average Hamming weight of binary codewords matches with arbitrary dimming target. An unsupervised DL technique is employed for obtaining a neural network to replace the encoder-decoder pair that recovers the message from the optically transmitted signal. In such a task, a novel stochastic binarization method is developed to generate the set of binary codewords from continuous-valued neural network outputs. For universal support of arbitrary dimming target, the DL-based VLC transceiver is trained with multiple dimming constraints, which turns out to be a constrained training optimization that is very challenging to handle with existing DL methods. We develop a new training algorithm that addresses the dimming constraints through a dual formulation of the optimization. Based on the developed algorithm, the resulting VLC transceiver can be optimized via the end-to-end training procedure. Numerical results verify that the proposed codebook outperforms theoretically best constant weight codebooks under various VLC setups.

In this paper, we investigate a deep learning (DL) approach to solve a generic constrained optimization problem in wireless networks, where the objective and constraint functions can be nonconvex. To this target, the computation process of the solution is replaced by deep neural networks (DNNs). The original problem is transformed to a training task of the DNNs subject to nonconvex constraints. Since existing DL libraries are originally intended for unconstrained training, they cannot be directly applied to our constrained training problem. We propose a constrained training strategy based on the primal-dual method from optimization techniques. The proposed DL approach is deployed to solve transmit power allocation problems in various network configurations. The simulation results shed light on the feasibility of the DL method as an alternative to existing optimization algorithms.

In this work, we investigate a proactive eavesdropping system where a central monitor covertly wiretaps the communications between a pair of suspicious users via multiple intermediate nodes. For successful eavesdropping, it is required that the eavesdropping channel capacity is higher than the data rate of the suspicious users so that the central monitor can reliably decode the intercepted information. Hence, the intermediate nodes operate in two different modes, namely eavesdropping mode and jamming mode, to facilitate eavesdropping. Specifically, the eavesdropping nodes forward the intercepted data from the suspicious users to the central monitor, while the jamming nodes transmit jamming signals to proactively control the data rate of the suspicious users. We propose an efficient deep learning-based approach to identify the optimal mode selection for the intermediate nodes and the optimal transmit power for the jamming nodes. Numerical results confirm the significant performance gain of our proposed method both in terms of performance and time complexity over conventional schemes.

A study of a multi-hop decode-and-forward (DF) simultaneous wireless information and power transfer (SWIPT) sensor network system is presented in this work. In the studied system model, a source communicates with a destination through the aid of multi-hop relays which harvest energy from their received signals. We apply power splitting (PS) based SWIPT relaying protocols for the relays harvesting energy. Focused on DF relaying protocol, we aim to minimize the transmit power at the source under a set end-to-end throughput constraint by optimizing PS ratios at the relays. Based on convex optimization techniques, the globally optimal PS ratio solution is obtained as a closed-form solution. Numerical results demonstrate the efficacy of the proposed optimal design over the conventional fixed PS ratio scheme.

This article studies a multihop decode-and-forward (DF) simultaneous wireless information and power transfer (SWIPT) system where a source sends data to a destination with the aid of multihop relays which do not depend on an external energy source. To this end, we apply power splitting (PS)-based SWIPT relaying protocol so that the relays can harvest energy from the received signals from the previous hop to reliably forward the information of the source to the destination. We aim to solve two optimization problems relevant to our system model. First, we minimize the transmit power at the source under the individual quality-of-service (QoS) threshold constraints of the relays and the destination nodes by optimizing PS ratios at the relays. The second is to maximize the minimum system achievable rate by optimizing the PS ratio at each relay. Based on the convex optimization techniques, the globally optimal PS ratio solution is obtained in closed-form for both problems. By setting the QoS threshold constraint, the same for each node for the source transmit power problem, we discovered that either the minimum source transmit power or the maximum system throughput can be found using the same approach. Numerical results demonstrate the superiority of the proposed optimal SWIPT PS design over conventional fixed PS ratio schemes.

This paper studies a deep learning (DL) framework to solve distributed non-convex constrained optimizations in wireless networks where multiple computing nodes, interconnected via backhaul links, desire to determine an efficient assignment of their states based on local observations. Two different configurations are considered: First, an infinite-capacity backhaul enables nodes to communicate in a lossless way, thereby obtaining the solution by centralized computations. Second, a practical finite-capacity backhaul leads to the deployment of distributed solvers equipped along with quantizers for communication through capacity-limited backhaul. The distributed nature and the non-convexity of the optimizations render the identification of the solution unwieldy. To handle them, deep neural networks (DNNs) are introduced to approximate an unknown computation for the solution accurately. In consequence, the original problems are transformed to training tasks of the DNNs subject to non-convex constraints where existing DL libraries fail to extend straightforwardly. A constrained training strategy is developed based on the primal-dual method. For distributed implementation, a novel binarization technique at the output layer is developed for quantization at each node. Our proposed distributed DL framework is examined in various network configurations of wireless resource management. Numerical results verify the effectiveness of our proposed approach over existing optimization techniques.

This paper studies simultaneous wireless information and power transfer (SWIPT) techniques for point-to-point multiple-input multiple-output channels, where a multi-antenna transmitter conveys information and energy at the same time to a multi-antenna receiver equipped with time switching (TS) circuits for an energy harvesting (EH) mode and an information decoding (ID) mode. Unlike conventional uniform TS (UTS) structure where all the receive antennas at the receiver employ a single TS circuit, in this paper, we propose a general dynamic TS (DTS) receiver architecture which has an individual TS circuit for each antenna. In the proposed DTS, the operation modes of the antennas can be dynamically changed to improve SWIPT performance. We aim to identify the achievable rate-energy (R-E) tradeoff of the DTS protocol for both linear and non-linear EH models by maximizing the information rate subject to the EH constraint. This results in joint optimization of the transmit covariance matrices and the time durations for the EH and the ID modes of the receive antennas, which is jointly non-convex in general. To tackle the non-convexity of the original problem, the successive convex approximation technique is adopted by addressing a series of approximated convex problems. As a result, efficient optimization algorithms are proposed for determining the boundary points of the achievable R-E region. We also provide a low-complexity algorithm which achieves near-optimal performance with much reduced complexity. Numerical results demonstrate that the proposed DTS presents significant performance gains over conventional UTS approaches.

In this paper, we study a legitimate proactive eavesdropping scenario where a central monitor covertly wiretaps the communications between a pair of suspicious users via multiple intermediate nodes. For this system, it is necessary to ensure the eavesdropping channel capacity higher than the data rate of the suspicious users so that the central monitor can reliably decode the intercepted information. To this end, the intermediate nodes operate in either eavesdropping or jamming modes. The eavesdropping nodes forward the intercepted data from the suspicious users to the central monitor, while the jamming nodes transmit jamming signals to control the data rate of the suspicious users if necessary. We optimize the mode selection and transmit power of each intermediate node to achieve the maximum eavesdropping rate. Two different scenarios are investigated, in which the intermediate nodes communicate with the central monitor through wired links or wireless channels. For both configurations, globally optimal solutions are developed for joint mode selection and transmit power optimization problems. We also propose low-complexity methods which achieve near-optimal performance with reduced computational complexity. The numerical results validate the efficiency of our proposed algorithms.

This paper studies unmanned aerial vehicle (UAV) aided wireless communication systems where a UAV serves uplink communications of multiple ground nodes by flying the area of the interest. We aim to maximize the minimum average rate of the UAV by jointly optimizing the UAV trajectory and the ground nodes' uplink transmit power. However, this problem is shown to be non-convex in general, and thus existing convex optimization techniques and algorithms cannot be directly applied. By employing the successive convex approximation (SCA) techniques, we present an efficient algorithm which is guaranteed to converge to at least a local optimal point for the non-convex problems. To this end, proper convex approximations are derived for the non-convex constraints. Numerical results demonstrate the proposed algorithm performs better than baseline scheme.

In this paper, we investigate simultaneous wireless information and power transfer (SWIPT) where a multi- antenna transmitter sends data and energy to single antenna receivers with a time switching (TS) circuit. In this system, a general joint TS protocol is introduced which includes conventional TS schemes as special cases. We aim to analyze the achievable rate region of the joint TS under energy harvesting constraint at the receivers by jointly optimizing the TS ratios and the transmit covariance matrices. To tackle non-convex rate region characterization problems, we first decouple the original problems into several subproblems with fixed auxiliary variables. Then, the globally optimal TS ratios and the transmit covariance matrices are computed via convex optimization techniques. Numerical examples verify the efficacy of the proposed joint TS over conventional methods.

In this paper, we consider a legitimate proactive eavesdropping scenario where a central monitor tries to intercept the information exchanged between a pair of suspicious entities through amplify-and-forward full- duplex relays and a cooperative jammer. Specifically, the eavesdropping relays simultaneously listen to the suspicious transmitter and forward the eavesdropped information to the central monitor. At the same time, the jammer broadcasts the jamming signal to maintain the suspicious data rate below the channel capacity of the central monitor so that the eavesdropped information can be successfully decoded by the central monitor. In this system, we jointly design the relay precoders at the eavesdropping relays and the transmit covariance matrix at the jammer to maximize the eavesdropping rate by a two-layer semi-definite relaxation approach. Simulation results verify the efficiency of our proposed solution and show considerable performance gains over conventional schemes.

This work deals with a joint downlink and uplink design for wireless powered cloud radio access network, where a baseband processing unit (BBU) communicates with downlink and uplink users through multiple remote radio heads (RRHs) connected to the BBU via finite-capacity fronthaul links. In the downlink, the RRHs send information to the downlink users and transfer energy to the uplink users (ULUs). By using the harvested energy, each ULU transmits information to the BBU through the uplink channels. In this work, we maximize the uplink sum-rate of the ULUs subject to the minimum downlink rate constraint as well as the per-node transmit power and the fronthaul capacity constraints. Numerical results confirm the advantages of the proposed algorithm compared to baseline schemes.

This paper studies beamforming design problems for multi-cell multi-user downlink networks with simultaneous wireless information and power transfer. In this system, base stations (BSs) concurrently transfer information and energy to multiple single-antenna information decoding (ID) and energy harvesting (EH) users. We aim to maximize energy harvesting efficiency (EHE), which is defined as the ratio of the harvested energy at the EH users to the amount of energy consumption at the BSs, while guaranteeing quality-of-service constraint for each ID user. First, for the centralized case where global channel state information (CSI) is available at all BSs, we propose a centralized beamforming method based on the semi-definite relaxation techniques. Next, in order to reduce the backhaul signaling overhead, a decentralized algorithm is presented where each BS computes its beamforming vector by only using local CSI. Simulation results show that the proposed algorithm offers a significant EHE performance gain over conventional schemes.

In this work, we investigate a wireless powered communication network (WPCN) where multiple eavesdroppers attempt to intercept the information between a hybrid access-point (H-AP) and an energy harvesting (EH) user. During the first energy transfer (ET) phase, the EH user and an EH cooperative jammer harvest energy from the transmitted signals of the H- AP. Then, in the next information transfer (IT) phase, the user sends confidential information to the H-AP while the jammer broadcasts artificial noises to the eavesdroppers by utilizing their previously harvested energy. We particularly consider optimization of the time allocation between the ET and the IT phases by which the secrecy rate is maximized. To cut down a computational burden, a low-complexity closed-form solution of the time allocation factor with some interesting behaviors will be proposed by a worst-case approximation. Through simulation results, we evaluate the performance of our proposed scheme and show that a performance gain compared to conventional schemes becomes clearer with the increased number of eavesdroppers.

This paper investigates an unmanned aerial vehicle (UAV)-aided wireless powered communication network where a mobile hybrid access point serves multiple energy-constrained ground terminals (GTs) in terms of wireless energy transfer and data collection. Specifically, to support information transmission of the GTs, the mobile UAV first transfers wireless energy in the downlink. Then, by harvesting this wireless energy, the GTs transmit their uplink information signals to the UAV in a time division multiple access manner. In this system, we jointly optimize the trajectory of the UAV and the uplink power control policy in order to maximize the minimum throughput of the GTs. By applying the concave-convex procedure, we propose an iterative algorithm which efficiently identifies a locally optimal solution. Simulation results verify the efficiency of the proposed algorithm compared to conventional schemes.

This paper studies device-to-device wireless communications, where two energy-constrained Internet-of-Things (IoT) nodes, which do not have constant power supplies, wish to exchange their information with each other. Because of small form factor, the IoT nodes are normally equipped with simple energy storages, which might suffer from a high self-discharging effect. Therefore, the energy stored in each node would not be available after a few time duration. In this system, we investigate power splitting (PS)-based energy exchange methods by exploiting radio frequency (RF) wireless energy transfer techniques, and propose a new concept called wireless information and power exchange (WIPE). In this WIPE protocol, each node operates either in a transmit mode and a receive mode at each time slot. First, a transmit node sends the information signal to a receive node which utilizes a PS circuit for information decoding and energy harvesting. Then, the harvested energy of the receive node is stored in the energy storage. At the consecutive time slot, two nodes switch their operations, i.e., the receive node in the previous time slot now operates in a transmit mode which transfers RF signals by using the harvested energy. This procedure continues by changing the operations of two nodes at each time slot. For the proposed WIPE protocol, we provide two different PS ratio optimization schemes which maximize the weighted sum throughput performance according to the level of channel state information (CSI) knowledge. For the ideal full CSI case where the CSI for all time slots is known in advance, the globally optimal PS algorithm is presented by applying convex optimization techniques. Also, for a practical scenario where only the causal CSI is available, we propose an efficient PS optimization method which achieves performance almost identical to the ideal full CSI case. Simulation results verify that the WIPE protocol with the proposed PS optimization techniques performs better than conventional schemes.

This paper studies beamforming design problems for multi-cell multi-user downlink networks with simultaneous wireless information and power transfer (SWIPT). In this system, multi-antenna base stations (BSs) concurrently transfer information and energy to multiple single-antenna information decoding (ID) and energy-harvesting (EH) users. We aim to maximize EH efficiency (EHE) that is defined as the ratio of the harvested energy at the EH users to the amount of energy consumption at the BSs while guaranteeing the quality-of-service constraint for each ID user. The EHE metric quantifies the efficiency of the power transfer capability of the SWIPT network. For the EH operation, both an ideal linear model and a practical non-linear model are individually investigated. We optimally solve this non-convex problem in two different scenarios according to the cooperation level among the BSs. First, for the centralized case, where global channel state information (CSI) is available at all BSs, we propose a centralized beamforming method based on the semi-definite relaxation and the successive convex approximation techniques. Next, in order to reduce the backhaul signaling overhead, decentralized algorithms are presented where each BS computes its beamforming vector by only using local CSI. The simulation results show that the proposed SWIPT beamforming algorithms offer a significant EHE performance gain over conventional schemes.

In this paper, we study a wireless powered communication network (WPCN) in a generalN-user interference channel (IFC), where a hybrid access-point (H-AP) in each cell supports its corresponding user. In this multi-cell environment, the H-AP first sends the energy signal to charge users in the downlink (DL) phase, while in the subsequent uplink (UL) phase, each user transmits its information signal to the corresponding H-AP utilizing the previously harvested energy. For the WPCN in this IFC scenario, cross-link interference occurs due to asynchronous time allocation of the DL and the UL amongNcells which significantly affects the overall performance. To handle the interference issue efficiently, we jointly optimize the DL and UL time allocation of each cell as well as the transmit power allocation at the H-APs and the users so that the weighted sum-rate of UL information transmission is maximized. To tackle non-convexity of the weighted sum-rate maximization problem, we propose an iterative algorithm where the time allocation and the transmit power are updated based on the weighted minimum mean square error criteria and the gradient projection method, respectively. Furthermore, we consider two simple protocols where the DL time allocation of each cell is synchronized and present resource allocation method, respectively. In simulation results, we verify that the proposed algorithm for the asynchronous protocol outperforms conventional schemes.

This paper considers multiple-input single-output simultaneous wireless information and power transfer (SWIPT) broadcast channels (BCs) where a multi-antenna transmitter serves single antenna receivers each equipped with a time switching (TS) circuit for information decoding (ID) and energy harvesting (EH). To be specific, we study a scheme which jointly determines the time durations allocated for the ID and the EH modes at each receiver and the transmit covariance matrices at the transmitter. Then, we present a general joint TS protocol for the SWIPT BC which includes conventional TS schemes as special cases. In order to fully characterize the performance of the proposed joint TS systems, the achievable rate region is analyzed under EH constraint at the receivers. By applying the rate profile methods, we identify the optimal TS ratios and the optimal transmit covariance matrices which achieve the boundary points of the rate region. Then, the boundary points are obtained by solving the average transmit power minimization problems with individual rate constraints at the receivers. To solve these non-convex problems, the original problems are decoupled into subproblems with fixed auxiliary variables. Then, the globally optimal TS ratios and the transmit covariance matrices are computed by finding the optimal auxiliary variables via convex optimization techniques. Numerical results demonstrate that the proposed joint TS scheme outperforms conventional TS methods.

In this paper, we consider a legitimate proactive eavesdropping scenario where a distant central monitor covertly wiretaps the communication between a pair of suspicious users via several multi-antenna full-duplex spoofing relays and a multi-antenna cooperative jammer. Assuming an adaptive transmission policy at the suspicious users, the spoofing relays not only intercept but also forward the manipulated information to control the data rate of the suspicious users in collaboration with the jammer. We provide a technique which jointly optimizes the receive combining vector at the central monitor, the precoders at the relays, and the transmit covariance matrix at the jammer for maximizing the eavesdropping rate. To reveal some fundamental properties of the optimal operation, we first study a single-relay system. It is shown that when the central monitor experiences a poor eavesdropping channel link, the spoofing relay and the jammer should transmit destructive signals and jamming signals, respectively. In this case, the suspicious users are forced to decrease their data rate, while the intercepted information can be successfully decoded by the central monitor. On the other hand, when the eavesdropping channel condition is favorable, the spoofing relay forwards constructive signals to further increase the data rate at the suspicious users in a way that more information can be intercepted from the suspicious users. We then formulate a general eavesdropping rate maximization problem for multiple relays and present a semi-definite relaxation approach. A low-complexity design is also proposed based on our analysis for the single-relay system. Simulation results verify the efficiency of the proposed solutions compared to other baseline schemes in various practical setups.

In this paper, we consider a proactive eavesdropping scenario, where a central monitor tries to intercept the information exchanged between a pair of suspicious entities through amplify-and-forward full-duplex relays and a cooperative jammer. Specifically, the eavesdropping relays simultaneously listen to the suspicious transmitter and forward the eavesdropped information to the central monitor. At the same time, the jammer broadcasts jamming signals to maintain the data rate of the suspicious users below the channel capacity of the central monitor so that the eavesdropped information can be successfully decoded by the central monitor. In this system, we jointly design the receive combining vector at the central monitor, the relay precoders at the eavesdropping relays and the transmit covariance matrix at the jammer to maximize the eavesdropping rate. First, we examine the case of a single eavesdropping relay equipped with a single antenna and provide some useful insights. Also, an effective two-layer optimization method is proposed to obtain the globally optimal solution. Then, we study a general case of multiple eavesdropping relays with multiple antennas, and solve a semi-definite relaxation problem. Numerical results verify the efficiency of our proposed solutions for both single and multiple relay cases and show considerable performance gains over conventional schemes.

In this paper, we study vector parameter estimation in multiple-input multiple-output wireless-powered sensor networks (WPSNs) where sensor nodes operate by harvesting the radio frequency signals transmitted from energy access points (E-APs). We investigate a joint design of sensor data precoders, a fusion rule, and energy covariance matrices to minimize the mean square error (MSE) of the parameter estimate based on a non-linear energy harvesting model. First, we propose a centralized algorithm to solve the MSE minimization problem. Next, to reduce the computational complexity at the fusion center (FC) and feedback overhead from the sensors to the FC, we present a distributed algorithm to locally compute the precoders and the energy covariance matrices. We employ the alternating direction method of multipliers technique to minimize the MSE in a distributed manner without any coordination from the FC. In the proposed distributed algorithm, each sensor node calculates its own precoders and determines the local information of the fusion rule, and then messages are broadcast to other sensor nodes and E-APs. Simulation results demonstrate that the distributed algorithm performs close to the centralized algorithm with reduced complexity. Moreover, the proposed methods exhibit superior estimation performance over conventional techniques in WPSNs.

This paper considers point-to-point wireless communications where an energy-constrained node, which has insufficient energy for data transmission, wants to exchange messages with a node with enough energy. In this system, we study power splitting (PS) based energy cooperation methods by exploiting wireless energy transfer techniques and propose a new concept called sustainable wireless information and power exchange (SWIPE). In this SWIPE protocol, the node which has sufficient energy first transmits the information signal to the energy-constrained node. Then, the received signal at the energy-constrained node is utilized for both information decoding and energy harvesting via a PS circuit. At the consecutive time slot, by using the harvested energy, the energy- constrained node is now able to send a signal to the other node which employs a similar PS technique. This procedure continues by switching the operations of two nodes at each time slot. For the proposed SWIPE protocol, we present the optimal PS ratio computation algorithm in order to maximize the weighted sum throughput performance. Simulation results confirm the efficacy of the proposed SWIPE protocol over conventional schemes.

In this paper, we study self energy recycling techniques for point-to-point multiple-input multiple-output systems where a full-duplex transmitter with multiple antennas communicates with a multi-antenna receiver. Due to the full-duplex nature, the transmitter receives a signal transmitted by itself through a loop-back channel. Then, the energy of the signal is harvested and stored in an energy storage. Assuming time-slotted systems, we propose a new communication protocol in which the harvested energy at the transmitter is recycled for future data transmissions to the receiver. Under this setup, we present a transmit covariance matrix optimization method in order to maximize the sum rate performance for two different cases. First, for a perfect channel state information (CSI) case, the globally optimal algorithm for the sum rate maximization problem is proposed. Next, for an imperfect CSI case, we provide a robust covariance matrix optimization approach where the worst-case sum rate performance can be maximized. Numerical results demonstrate that the proposed methods offer a significant performance gain over conventional schemes.

In this paper, we study a wireless powered communication network (WPCN) in a two-user interference channel, where two hybrid access-points (H-APs) support a user in each cell. In this two cell scenario, the H-APs first transmit the energy signal to charge both users in the downlink (DL) phase. Then, in the subsequent uplink (UL) phase, each user sends its information signal to the corresponding H-AP utilizing the harvested energy. Due to asynchronous time allocation of the DL and the UL between two cells, cross-link interference affects the overall performance. In this system, we aim to maximize the sum-rate by jointly optimizing the time durations for the DL and the UL phases of each cell, and the UL transmit power of all users. As the sum-rate maximization problem becomes non-convex, it is difficult to obtain an optimal solution. To solve this problem, we propose a new algorithm where the time allocation and the transmit power are alternatively updated based on the weighted sum-minimum mean square error criteria and the projected gradient method. In simulation results, we verify that the proposed algorithm for the asynchronous protocol outperforms conventional schemes.

In this paper, we study parameter estimation in multiple-input multiple-output (MIMO) wireless powered sensor networks (WPSN). The sensor nodes are powered exclusively by harvesting the radio frequency signals transmitted from the energy access points. We propose a joint design of the sensor data precoders and energy covariance matrices to minimize the mean square error (MSE) of the parameter estimate. This design also incorporates optimal allocation of the harvested power for data acquisition and data transmission. We employ a zero-forcing precoding based estimation framework and the alternating minimization technique to compute the precoders, power allocation, and energy covariance matrices. Simulation results demonstrate that the proposed method achieves a superior estimation performance in comparison to the conventional energy transfer techniques for estimation in WPSNs.

In this paper, we consider a wireless powered communication network with an energy harvesting (EH) jammer where eavesdroppers try to wiretap the communication between users and a hybrid access-point (H-AP). In our system, the H-AP first transmits an energy signal to recharge the batteries of the EH users and the EH jammer in the energy transfer (ET) phase. Then, in the subsequent information transfer (IT) phase, each user sends information to the H-AP in a time division multiple access manner, while the jammer generates jamming signals to interfere the eavesdroppers. We adopt two different secrecy performance measurements according to the level of channel state information (CSI) of the eavesdroppers. First, with a single user, we maximize the secrecy rate by optimizing the time allocation between the ET and the IT phase when perfect CSI of the eavesdroppers is available at all nodes. In contrast, when the instantaneous CSI of the eavesdroppers is not available at legitimate nodes, we analyze and minimize the secrecy outage probability. We also extend the single user analysis to a more general multi-user situation with an additional consideration of the transmit power allocation at the jammer. Finally, we evaluate the performance of our proposed solutions through simulations and demonstrate that a performance gain compared to conventional schemes becomes more pronounced with the increased number of eavesdroppers and users.

In this paper, we study multi-user multi-input multi-output cognitive wireless powered communication networks (WPCN), in which a secondary WPCN shares spectrum with a primary wireless information transfer system. A typical WPCN consists of two different phases. In the first downlink phase, a hybrid access point (H-AP) transfers energy to charge users, and then in the subsequent uplink phase, the users send information by using the harvested energy to the H-AP. We consider two different cognitive WPCN protocols depending on the cooperation level between the primary transmitter and the secondary H-AP. For both cases, we formulate sum throughput maximization problems by taking the interference leakage to the primary network into consideration. The problems are generally non-convex due to coupled variables in the WPCN. To tackle this issue, we first convert the problems into equivalent convex forms, and then identify the global optimal solutions by applying the proposed iterative optimization algorithms. Finally, the simulation results demonstrate that the proposed algorithms outperform conventional schemes.

This paper investigates multi-user multi-input multi- output cognitive wireless powered communication networks (WPCN), in which a secondary WPCN shares spectrum with a primary wireless information transfer system. A typical WPCN consists of two different phases. In the first downlink phase, a hybrid access point (H-AP) transfers energy to charge users, and then in the subsequent uplink phase, the users send information by using the harvested energy to the H-AP. We consider underlay cognitive WPCN without cooperation between the primary transmitter and the secondary H-AP. In this case, we formulate sum throughput maximization problem by taking the interference leakage to the primary network into consideration. The problem is generally non-convex due to coupled variables in the WPCN. To tackle this issue, we first convert the problem into equivalent convex form, and then identify the global optimal solution by applying the proposed iterative optimization algorithm. Finally, simulation results demonstrate that the proposed algorithm outperforms conventional schemes.

In this work, we consider a wireless powered communication network (WPCN) with an energy harvesting (EH) jammer where an eavesdropper tries to wiretap the communication between a user and a hybrid access-point (H-AP). In our system, the H-AP first transmits an energy signal to recharge the batteries of the EH user and the EH jammer in the energy transfer (ET) phase. Then, in the subsequent information transfer (IT) phase, the user sends its information signal to the H-AP, while the jammer generates the jamming signal to interfere the eavesdropper utilizing the harvested energy in the ET phase. Assuming only the channel distribution information (CDI) of the eavesdropper is available at the legitimate nodes, we analyze and minimize the secrecy outage probability by optimizing the time allocation between the two phases. To reduce the complexity, we also provide a simple closed-form solution, and the simulation results verify that its performance approaches the optimum.

This paper studies multi-user wireless powered communication networks, where energy constrained users scavenge energy of the radio frequency signals radiated from a hybrid access point (H-AP). The energy is then utilized for the users' uplink information transmission to the H-AP in time division multiple access mode. In this system, we aim to maximize the uplink sum rate performance by jointly optimizing energy and time resource allocation for multiple users. To this end, we first derive the optimal downlink energy transmission policy at the HAP. Based on this result, analytical resource allocation solutions are obtained. Simulation results confirm that the proposed algorithms offer significant sum rate performance gain over conventional schemes.

In this paper, we present a new beamforming design at the relay in AF two-way relaying systems where a relay node with Nr antennas serves two source nodes equipped with a single antenna. In this system, we design low complexity relay beamforming which minimizes the mean squared error (MSE). To this end, unlike conventional designs based on perfect self-interference cancellation at each source whose computational effort grows with an order of O(N6r), we determine the level of SIC in an MSE optimization problem, thereby obtaining a new insightful closed-form solution with complexity of O(N2r). It is also shown that the channel estimation overhead can be reduced by our methods. Finally, simulation results demonstrate that the proposed design method achieves the minimum MSE with reduced complexity.

In this paper, we propose a new user selection algorithm for full-duplex (FD) multiuser multiple-input single-output (MU-MISO) systems where a FD base station (BS) communicates with multiple half-duplex (HD) users in both downlink and uplink channels simultaneously. Due to self-interference at the BS and co-channel interference among users, a joint downlink and uplink user selection to maximize system performance incurs high search complexity. To reduce the complexity, we introduce a two step user selection algorithm which successively chooses downlink users followed by uplink users based on the decomposed sum rate of the FD systems. From the numerical results, we confirm that the proposed user selection algorithm for the FD MU systems exhibits a small performance loss compared to the optimal user selection algorithm with much reduced complexity.

In simultaneous wireless information and power transfer (SWIPT) systems, dedicated energy signals only convey wireless energy, but not information. For this reason, the energy-carring signals in the SWIPT can be pre- determined in advance and is shared among communication nodes. By exploiting this nature, this paper designs the optimal transmit beamforming vectors for the multiple-input single-output SWIPT interference channel with signal cooperation (IFC-SC), where the energy- carrying signal waveforms are known to transmitters and receivers. Specifically, we aim to identify the optimal tradeoff between the information rate and the harvested energy. To this end, an information rate maximization problem is formulated under minimum required harvested energy constraint, which is non-convex in general. To solve the problem, a new parameterization technique is introduced, and we can decouple the original problem into two subproblems, which yields closed-form beamforming solutions by addressing the line search method for the parameter. Simulation results confirms that the proposed optimal IFC-SC beamforming vectors outperform conventional SWIPT IFC systems.

In this paper, we investigate wireless powered communication network for OFDM systems, where a hybrid access point (H-AP) broadcasts energy signals to users in the downlink, and the users transmit information signals to the H-AP in the uplink based on orthogonal frequency division multiple access. We consider a full-duplex H-AP which simultaneously transmits energy signals and receives information signals. In this scenario, we address a joint subcarrier scheduling and power allocation problem to maximize the sum-rate under two cases: perfect self-interference cancellation (SIC) where the H-AP fully eliminates its self-interference (SI) and imperfect SIC where residual SI exists. In general, the problems for both cases are nonconvex due to the subcarrier scheduling, and thus it requires an exhaustive search method, which is prohibitively complicated to obtain an optimal solution. In order to reduce the complexity, for the perfect SIC scenario, we jointly optimize subcarrier scheduling and power allocation by applying the Lagrange duality method. Next, for the imperfect SIC case, the problem becomes more complicated due to the SI at the H-AP. To solve this problem, we propose an iterative algorithm based on the projected gradient method. Simulation results show that the proposed algorithm for the case of perfect SIC exhibits almost the same sum-rate performance compared to the optimal algorithm, and the proposed iterative algorithm for the imperfect SIC case offers a significant performance gain over conventional schemes.

This paper studies multiuser wireless powered communication networks, where energy constrained users charge their energy storages by scavenging energy of the radio frequency signals radiated from a hybrid access point (H-AP). The energy is then utilized for the users' uplink information transmission to the H-AP in time division multiple access mode. In this system, we aim to maximize the uplink sum rate performance by jointly optimizing energy and time resource allocation for multiple users in both infinite capacity and finite capacity energy storage cases. First, when the users are equipped with the infinite capacity energy storages, we derive the optimal downlink energy transmission policy at the H-AP. Based on this result, analytical resource allocation solutions are obtained. Next, we propose the optimal energy and time allocation algorithm for the case where each user has finite capacity energy storage. Simulation results confirm that the proposed algorithms offer about 30% average sum rate performance gain over conventional schemes.

In this paper, we investigate wireless powered communication network for OFDM systems, where a hybrid access point (H-AP) broadcasts energy signals to users in the downlink, and the users transmit information signals to the H-AP in the uplink based on an orthogonal frequency division multiple access scheme. We consider a full-duplex H-AP which simultaneously transmits energy signals and receives information signals, and a perfect self-interference cancellation where the H-AP fully eliminates its self interference. In this scenario, we address a joint subcarrier scheduling and power allocation problem to maximize the sum-rate. In general, the problem is on-convex due to subcarrier scheduling, and thus it requires an exhaustive search method, which is prohibitively complicated to obtain the globally optimal solution. In order to reduce the complexity, we jointly optimize subcarrier scheduling and power allocation by applying the Lagrange duality method. Simulation results show that the proposed algorithm exhibits only negligible sum-rate performance loss compared to the optimal exhaustive search algorithm and a significant performance gain over conventional scheme.

This paper investigates simultaneous wireless information and power transfer in multiple-input single-output interference channels, and designs transmit beamforming vectors which achieves the optimal tradeoff between the information rate and the harvested energy. To this end, the problem for maximizing the information rate is formulated with minimum required harvested energy constraint. In order to solve this nonconvex problem, we introduce parameterization techniques for characterizing the achievable rate- energy (R-E) region. As a result, the original problem is separated into two subproblems, for which closed- form solutions are obtained by addressing the line search method. Finally, we provide numerical examples for the achievable R-E region through simulations.

This paper investigates the optimal transmit beamforming designs for simultaneous wireless information and power transfer (SWIPT) in multiple-input single-output interference channels (IFC). Based on cooperation level among transmitters and receivsers, we classify the SWIPT IFC systems into two categories. First, we consider the IFC with partial cooperation, where only channel state information (CSI) is available at transmitters and receivers, but not the signal waveform. Second, we examine the IFC with signal cooperation, where both the CSI and the signal waveforms are known to transmitters and receivers. Then, for the both scenarios, we identify the Pareto boundary of the achievable rate-energy (R-E) region which characterizes the optimal tradeoff between the information rate and the harvested energy. To this end, the problems for maximizing the information rate are formulated with minimum required harvested energy constraint. To solve these non-convex problems, we introduce parameterization techniques for characterizing the R-E region. As a result, the original problem is separated into two subproblems, for which closed-form solutions are obtained by addressing the line search method. Finally, we provide numerical examples for the Pareto boundary of the R-E region through simulations.

In this paper, we analyze the ergodic capacity performance for diversity reception schemes over composite fading channels using a mixture gamma (MG) distribution. By adopting the MG distribution, various composite fading channel models can be approximated with mathematically tractable and highly accurate properties. In other words, signal- to-noise ratio statistics which contain complicated functions for each diversity reception scheme are formulated as a weighted sum of gamma distributions. With an aid of properties of the gamma distribution, we can derive closed-form expressions of an ergodic capacity for important diversity reception schemes such as maximal ratio combining and selection combining. We observe that our analysis can be expressed with the general number of receiver branches over various composite fading conditions. Simulation results verify that the derived ergodic capacity expressions match well with the empirical results.

In this paper, we study bit allocation and pairing methods based on zero forcing beamforming for downlink multiuser distributed antenna systems with limited feedback. Before assigning the feedback bit for each distributed antenna (DA) port, we need to solve the pairing issue which determines the set of DA ports to support a user. To this end, we first analyze an upper bound of a mean rate loss between perfect channel state information systems and limited feedback systems. Since minimizing the obtained bound is a joint optimization problem with respect to the pairing and the bit allocation, it is difficult to identify a solution analytically. Instead, we propose a two-step algorithm which derives the pairing based on the bound of the rate loss, and then obtain the feedback bit allocation method independently. From simulation results, we confirm that the proposed algorithms offer about 135% performance gains over a conventional equal bit allocation scheme for 5 DA ports systems.

In this paper, we study a feedback bit allocation algorithm with signal-to-leakage plus noise ratio maximizing beamforming (FA-SMB) for distributed antenna systems (DAS) presented in work [1]. We first investigate a scaling law of feedback bits for both the FA-SMB scheme and equal bit allocation. Through this analysis, we confirm that the required number of feedback bits to satisfy the maximum allowable rate gap between DAS with perfect channel state information (CSI) and limited feedback linearly increases with signal-to-noise ratio. Also, it is verified that the FA-SMB scheme saves the feedback bits by up to 30% over the equal bit allocation with the same rate gap at SNR = 40 dB for three-user DAS. Moreover, we show that the FA-SMB scheme substantially reduces the computational complexity compared to exhaustive search. Finally, we provide simulation results to demonstrate the efficacy of the FA-SMB scheme.

In this paper, we study bit allocation and pairing methods based on distributed zero forcing beamforming for downlink multi-user distributed antenna (DA) systems with limited feedback. Before assigning the feedback bit for each DA port, we need to solve the pairing issue that determines the set of DA ports to support a user. To this end, we first analyze an upper bound of a mean rate loss between perfect channel state information systems and limited feedback systems. Since minimizing the obtained bound is a joint optimization problem with respect to the pairing and the bit allocation, it is difficult to identify a solution analytically. Instead, we propose a two-step algorithm that derives the pairing based on the bound of the rate loss and then obtain the non-iterative bit allocation method independently. To further improve the performance, an enhanced feedback bit allocation algorithm is also proposed by applying an iterative optimization technique. In addition, we investigate a scaling law of limited feedback systems to maintain a constant rate loss as signal-to-noise ratio increases. From simulation results, we confirm that the proposed algorithms offer about 135% performance gains over a conventional scheme for five DA port systems and verify that our analysis is well matched with the numerical results.

In this work we investigate a series of phenomena that can result from the flat pricing in the current wireless Internet service. First, we investigate the charging model of current wireless Internet service, especially the WiBro (wireless broadband) service, via which we present inherent problems that result from the flat pricing scheme. After that, we present an analytic model for the measurement of the negative consequences that arise from the flat pricing. To that purpose, we introduce the concept of utility as a measure of user's satisfaction about the quality of the wireless Internet service, from which we derive a measure of user's dissatisfaction about diminished achievable bandwidth due to overload of the wireless resource. Finally, we quantify the proportion of users who are dissatisfied by degraded QoE (quality of experience).

In this paper, We extended our previous work of QR-RLS based MIMO(Multiple input Multiple output) channel estimation to Mobile Wimax 802.16m system. Mobile wimax system provides high data rate, also fulfills user's requirement like VOD(Video on demand)at very high vehicle speed and also provides better cell coverage area. Channel estimation is crucial part to achieve this goals especially in fast fading environment. Generally, Mobile Wimax systems uses Preamble and Pilots for channel estimation purpose. In the proposed method both preamble and pilots are jointly used for robust channel estimation. At First, QR-RLS Estimator uses Preamble for coarse channel estimation at start of every frame. Once the coarse channel is estimated, then pilots (scattered throughout time-frequency grid) are jointly used with the coarse channel component to derive the channel fading rate. This fading rate is then used to finely estimate the channel at pilot as well as data subcarrier. Thus robust estimation results without adding any overhead. Jointly estimated channel is then used with QR-LRL based data detection, where hard decision values are calculated. Simulation results are shown under various slow-fast channel fading conditions. Results are compared with pilot based channel estimation with LS(least square) interpolation, which shows that joint coarse-Fine estimation gives better performance.

We propose a new approach for the usage-based charging scheme in IP networks. Especially, we propose a method of determining usage charges to the QoS-free traffic for the VPN services based on the relative usage of the network resources in broadband IP networks. First, we present drawbacks of current flat-rate charging scheme for IP leased-line services which is analogous to the future IP virtual private networks. Next, we propose principles for assigning tariff to the traffic based on the relative usage of bandwidth, which determines charges to the user. Finally, via numerical experiments, we will illustrate the validity and implication of the proposed method.

A new approach to evaluating the degradation of cell delay and loss performance in ATM networks is described. The main concept is to assess a weighted penalty to the expected performance from the view point of the arriving cell. We propose a new method to impose a penalty to the system such that the greater the difference between the target performance and the observed performance, the higher the penalty given to the system. The performance measures under consideration are the delay variance and loss probability of the arriving cells, which can be derived from the information of buffer status. We also present the result of the numerical experiments and discuss the implication.

An analytical model for the gracious cell discard (GCD) control of a finite capacity queue in ATM output multiplexer is proposed. We put a gracious cell throttle (GCT) in front of a queue (main queue: MQ). The GCT is composed of a temporary queue (TQ) and a nonlinear filter (NLF). In every time slot, a burst of cells composed of HP (high priority) and LP (low priority) arrives. Cells are classified and HP cells are not governed by the NLF and go directly to TQ, whereas LP cells are governed by the NLF. When the length of MQ is greater than zero, the NLF discards a part of LP cells based on a nonlinear filter function. The remaining LP cells are admitted to MQ along with all the HP cells within the available space of MQ. As a cell rejection policy for the overflow in MQ, we propose a partial rejection with prioritized selection (PR-PS). PR-PS operates as follows: when the number of cells in TQ exceeds the vacant space in MQ, the HP cells are accepted first within the limit of the remaining space in MQ. If there remains any vacant space in MQ after the HP cells are accepted, the LP cells can enter within the limit of the updated remaining space in MQ. Finally, we evaluate performance of the QoS (quality of service) measures related to the cell loss and delay.