We investigate the problem of minimizing the transmit power while providing long-term average data rate guarantees for two mobile users in a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted wireless communication system. We formulate the studied problem as a sequential optimization problem in which one has to make a decision in each time slot based on current channel states and past data rates. To achieve long-term rate guarantees, we propose the opportunistic over-provision algorithm for choosing an achievable rate vector for users, finding a beamforming vector for the base station and selecting phase shifts as well as energy splitting ratios for the STAR-RIS in each time slot. Specifically, when channel gains are large enough, the opportunistic over-provision algorithm could select an achievable rate vector that is larger than the vector that consists of specified long-term average rates. Simulation results indicate that the proposed algorithm could significantly outperform a number of alternative schemes.

We investigate the problem of optimal transmit beamforming for maximizing the covert data rate subject to an upper bound for transmit power in a wireless communication system that contains an internal eavesdropper. To find an optimal beamforming vector, we formulate a non-convex optimization problem that takes into consideration the states of the transmitter-receiver channel and the transmitter-eavesdropper channel. We propose a novel algorithm that utilizes linear algebra and sequential convex optimization to efficiently obtain an adequate transmit beamforming vector. Simulation results show that the proposed approach could outperform two alternative schemes.

We propose fast beamforming algorithms for minimizing the power consumption of wireless communications while assuring unicast and multicast data rates in NOMA wireless networks. Specifically, we put forward the greedy backward substitution algorithm for unicast beamforming design and the Gram-Schmidt rotation algorithm for multicast beamforming design. The proposed approach adopts zero-forcing techniques, orthogonalization and rotation but does not have to solve complicated convex optimization problems. In addition, we derive novel analytical results for the studied problem. Simulation results indicate that the proposed approach outperforms a number of alternative schemes in the literature in terms of beamforming power consumption and computational complexity.

In this paper, we put forward a distributed, Transformer-assisted deep reinforcement learning scheme for latency-sensitive, mobility-aware and queue-aware task offloading in mobile edge computing systems. The proposed scheme adopts an attention-based transformer and deep reinforcement learning for minimizing the average cost and the task processing latency. Since the proposed scheme is distributed, there is no single point of failure in the system. Simulation results show that the proposed scheme could significantly outperform a number of baseline schemes in the literature.

In this paper, we propose a backbone-aware user association algorithm for heterogeneous hierarchical federated learning. We consider the scenario in which mobile devices have different computation and communication capabilities, while edge servers have different model uploading delays to the cloud server. To find an optimal user association, we formulate a combinatorial optimization problem that takes into consideration mobile-to-edge delays and edge-to-cloud delays. To reduce the computational complexity, we put forward the backbone-aware greedy algorithm. In addition, we prove that it is not always optimal for a mobile device to connect to the edge server with the minimum mobile-to-edge delay. Furthermore, we propose using dynamic bandwidth allocation after assigning users to edge servers to further reduce the latency. We also use simulation results to show the advantages of the proposed approach.

We put forward a novel minimum mean square error (MMSE) threshold-based power control scheme for wireless federated learning in digital communication systems. The proposed approach uses pulse-amplitude modulation to attain digital over-the-air aggregation of local machine learning models. To reduce the communication cost, we design a novel threshold-based power control strategy that minimizes the mean squared error of parameter estimation and satisfies a constraint on the average power. Simulation results show that the proposed approach is superior to one-bit broadband digital aggregation (OBDA) in terms of the testing accuracy of machine learning and the power consumption of wireless communications. Furthermore, in comparison with broadband analog aggregation (BAA), the proposed approach reduces the power consumption of wireless communications without sacrificing the testing accuracy.

In this paper, we propose a novel approach of designing the velocity function and the trajectory for a UAV to efficiently achieve location-dependent visual coverage. Specifically, the UAV dynamically adjusts its altitude to photograph terrestrial polygons with different image resolution requirements. Unlike prior work that assumes the UAV speed is constant, the proposed approach allows the UAV to change its speed. To minimize the task completion time, we put forward a novel approach that is composed of three algorithmic components. The first component uses an aggressive method for selecting the UAV photographing altitudes, designs the UAV velocity functions, and derives the UAV flying times for all pairs of regions. Based on the UAV flying times rather than the distances, the second component utilizes an auxiliary traveling salesman problem to determine the visited order of terrestrial regions. For each terrestrial region, the third component generates candidate coverage paths and picks up the coverage path based on the UAV flying time. We also derive analytical results on the UAV trajectory length. Large-scale simulation results indicate that the proposed approach outperforms a two-dimensional trajectory planning algorithm and a greedy algorithm for planning a three-dimensional trajectory in terms of the UAV task completion time.

In this paper, we put forward a novel local loss-assisted client selection approach for communication-efficient federated learning. Unlike many prior works that require all clients to upload compressed local gradients or model parameters in each round, the proposed approach intelligently selects a small subset of clients to send local model parameters to the server in each round. Specifically, in each round, clients send their latest local model losses to the server and the server picks up the clients with the smallest losses to upload the best set of local model parameters. Although different clients are selected in different rounds, the number of selected clients in a round is fixed and therefore the communication cost of a round is time-invariant. We use collaborative image classification as the application for evaluating the proposed approach of federated learning. Simulation results reveal that the proposed local loss-assisted client selection approach could significantly reduce the communication cost of federated learning at the cost of slightly reducing the accuracy of image classification.

Federated learning (FL) is a promising distributed learning technique particularly suitable for wireless learning scenarios since it can accomplish a learning task without raw data transportation so as to preserve data privacy and lower network resource consumption. However, current works on FL over wireless networks do not profoundly study the fundamental performance of FL over wireless networks that suffers from communication outage due to channel impairment and network interference. To accurately exploit the performance of FL over wireless networks, this paper proposes a novel intermittent FL model over a cellular-connected Unmanned Aerial Vehicle (UAV) network, which characterizes communication outage from UAV (clients) to their server and data heterogeneity among the datasets at UAVs. We propose an analytically tractable framework to derive the uplink outage probability and use it to devise a simulation-based approach so as to evaluate the performance of the proposed intermittent FL model. Our findings reveal how the intermittent FL model is impacted by uplink communication outage and UAV deployment. Extensive numerical simulations are provided to show the consistency between the simulated and analytical performances of the proposed intermittent FL model.

In this paper, we propose a novel machine learning-based signal detection scheme for multi-user wireless multiple-input multiple-output (MIMO) networks with random traffic. We focus on the challenging case in which the number of active users that transmit data to the base station in a time slot is a random variable from the viewpoint of the base station. Instead of using multiple machine learning models and exhaustive search, we propose using a novel deep machine learning model that adopts an extended constellation diagram and a loss function based on the nonuniform probability mass function for transmitted symbols. Simulation results reveal that the proposed machine learning-based signal detection scheme outperforms the zero-forcing detector and the minimum mean square error detector in wireless MIMO networks when the number of active users is random.

In this paper, we propose a reinforcement learning approach of collision avoidance and investigate optimal trajectory planning for unmanned aerial vehicle (UAV) communication networks. Specifically, each UAV takes charge of delivering objects in the forward path and collecting data from heterogeneous ground IoT devices in the backward path. We adopt reinforcement learning for assisting UAVs to learn collision avoidance without knowing the trajectories of other UAVs in advance. In addition, for each UAV, we use optimization theory to find out a shortest backward path that assures data collection from all associated IoT devices. To obtain an optimal visiting order for IoT devices, we formulate and solve a no-return traveling salesman problem. Given a visiting order, we formulate and solve a sequence of convex optimization problems to obtain line segments of an optimal backward path for heterogeneous ground IoT devices. We use analytical results and simulation results to justify the usage of the proposed approach. Simulation results show that the proposed approach is superior to a number of alternative approaches.

This paper aims to propose a three-dimensional (3D) point process that can be employed to generally deploy unmanned aerial vehicles (UAVs) in a large-scale cellular network and tractably analyze the fundamental network-wide performances of the network. This 3D point process is devised based on a 2D marked Poisson point process in which each point and its random mark uniquely correspond to the projection and the altitude of each point in the 3D point process, respectively. We elaborate on some important statistical properties of the proposed 3D point process and use them to tractably analyze the coverage performances of a UAV-enabled cellular network wherein all the UAVs equipped with multiple antennas are served as aerial base stations. The downlink coverage of the UAV-enabled cellular network is found and its closed-form results for some special cases are explicitly derived as well. Furthermore, the fundamental limits achieved by cell-free massive antenna array are characterized when coordinating all the UAVs to jointly perform non-coherent downlink transmission. These findings are validated by numerical simulation.

In this paper, we study the problem of optimal beamforming for non-orthogonal multiple-access (NOMA) wireless relay networks. For a two-hop wireless relay network that consists of a NOMA broadcasting channel and a Gaussian interference channel, we propose a novel algorithm that efficiently obtains beamforming vectors for maximizing the end-to-end sum rate. We first classifies NOMA wireless relay networks into two classes based on the channel coefficients. For a wireless relay network that belongs to the first class, we use the maximal-ratio transmission (MRT) technique to obtain an optimal set of beamforming vectors. On the other hand, for a wireless relay network that belongs to the second class, we transform the non-convex optimal beamforming problem into a semidefinite programming (SDP) problem and then solve it. Simulation results show that the proposed approach significantly outperforms a number of alternative schemes such as the random-phase beamforming scheme, the maximal-ratio transmission scheme, and the zero-forcing beamforming scheme.

In this paper, we propose using a decentralized planning-assisted approach of deep reinforcement learning for collision and obstacle avoidance in UAV networks. We focus on a UAV network where there are multiple UAVs and multiple static obstacles. To avoid hitting obstacles without severely deviating from the ideal UAV trajectories, we propose merging adjacent obstacles based on convex hulls and design a novel trajectory planning algorithm. For UAVs to efficiently avoid collisions in a distributed manner, we propose using a decentralized multi-agent deep reinforcement learning approach based on policy gradients. In addition, we propose using a priority-based algorithm for avoiding collisions without reducing the speeds of UAVs too much. Simulation results show that the proposed decentralized planning-assisted deep reinforcement learning approach outperforms a number of baseline approaches in terms of the probability that all UAVs successfully reach their goals within the deadline.

Attaining reliable communications traditionally relies on a closed-loop methodology but inevitably incurs a good amount of networking latency thanks to complicated feedback mechanism and signaling storm. Such a closed-loop methodology thus shackles the current cellular network with a tradeoff between high reliability and low latency. To completely avoid the latency induced by closed-loop communication, this article aims to study how to jointly employ open-loop communication and multi-cell association in a heterogeneous network (HetNet) so as to achieve ultra-reliable and low-latency communications. We first introduce how mobile users in a HetNet adopt the proposed proactive multi-cell association (PMCA) scheme to form their virtual cell that consists of multiple access points (APs) and then analyze the communication reliability and latency performances. We show that the communication reliability can be significantly improved by the PMCA scheme and maximized by optimizing the densities of the users and the APs. The analyses of the uplink and downlink delays are also accomplished, which show that extremely low latency can be fulfilled in the virtual cell of a single user if the PMCA scheme is adopted and the radio resources of each AP are appropriately allocated.

This paper aims to propose a three-dimensional (3D) point process that can be employed to generally deploy unmanned aerial vehicles (UAVs) in a large-scale 3D cellular network and to tractably analyze the fundamental network-wide performances of the network. The proposed 3D point process is devised based on a 2D marked Poisson point process in which each point and its random mark uniquely correspond to the projection and the altitude of each point in the 3D point process, respectively. We study some of the important statistical properties of the proposed 3D point process and shed light on some crucial insights into them that facilitate the analyses of a UAV-enabled cellular network wherein all UAVs equipped with multiple antennas are deployed by the proposed 3D point process to serve as aerial base stations. The salient features of the proposed 3D point process lie in its suitability in practical 3D channel modeling and tractability in analysis. The downlink coverages of the UAV-enabled cellular network are found and their closed-form results for some special cases are also derived. Most importantly, their fundamental limits achieved by cell-free massive antenna array are characterized when coordinating all the UAVs to jointly perform non-coherent downlink transmission. These key findings and observations are numerically validated in this paper.

In this paper, we propose a novel approach of optimal power allocation and signal phase selection for NOMA wireless relay networks. Specifically, to optimize the network throughput, the base station and the stronger relay optimally allocate their transmission power, while the weaker relay selects an optimal signal phase. To maximize the end-to-end sum rate, we formulate a non-convex optimization problem and derive novel analytical results. In addition, we propose the four-candidate greedy algorithm for optimal power allocation and signal phase selection in NOMA wireless relay networks. Simulation results show that the proposed greedy algorithm could significantly improve the system performance.

In this paper, we study optimal power control for sequential NOMA in wireless relay networks. When a sequential NOMA scheme is used, in each time slot, the base station uses NOMA in the first phase, while the stronger relay uses NOMA in the second phase. To solve the problem of optimal power control for sequential NOMA, we design the geometric eight-point algorithm based on geometric properties of the non-convex optimization problem. Specifically, the proposed geometric eight-point algorithm adopts parabolas and the geometric classification algorithm to obtain a locally optimal power allocation vector with low computational complexity. Simulation results indicate that the proposed sequential NOMA scheme could increase the spectral efficiency and reduce the relay power consumption for wireless relay networks.

In this paper, we propose a geometric approach for optimal power control and relay selection in NOMA wireless relay networks. First, for each pair of relays, to derive an optimal vector of transmission power that maximizes the network throughput, we formulate a non-convex optimization problem. To obtain a closed-form solution for the non-convex optimization problem, we adopt ellipses and hyperbolas for classification of transmission power vectors. In addition, the proposed geometric approach could be used to select an optimal pair of relays and an optimal vector of transmission power in polynomial time. Furthermore, we observe that allocating all transmission power of the BS to send data to the stronger relay is not necessarily optimal in a NOMA wireless relay network. Simulation results show that the proposed geometric approach could significantly increase the end-to-end sum rate and reduce the power consumption of wireless communications for NOMA wireless relay networks with low computational complexity.

In this paper, we proposed a novel approach for proactive edge caching in wireless small cell networks. Specifically, we propose using a recurrent neural network for predicting the content popularity with low computational complexity. The mean estimation error of the adopted recurrent neural network could be very close to that of the optimal linear prediction filter utilizing all past history. Based on the predicted content popularity, we formulate and solve a minimum cost flow problem in order to optimally place content files at edge caches. Since the computational complexity of the adopted recurrent neural network is relatively low and the minimum cost flow problem can be solved in polynomial time, the proposed approach is feasible in practice. Simulation results show that the proposed approach outperforms a greedy approach and can significantly reduce the bandwidth consumption of the backhaul network.

This paper aims to study how to achieve high transmission security in the physical (PHY) layer of a multi-tier heterogeneous network (HetNet) through a simple device-to- device (D2D) enabling scheme. For the HetNet, we propose a simple D2D-enabling scheme with low complexity for users to opportunistically enable their D2D mode and become either D2D or cellular users by exploiting the diversity of all user association signals from all base stations (BSs). To evaluate whether the proposed D2D enabling scheme improves the PHY-layer security of the HetNet, the secrecy outage probability of the HetNet is defined and analyzed from two different perspectives of BSs and users. We define the BS- centric and user-centric security outage events and derive the explicit lower bound on their probability when the proposed D2D enabling scheme is adopted. Our analytical and numerical results not only show that the proposed D2D-enabling scheme can achieve high PHY-layer security but also reveal how densely the BSs should be deployed in the HetNet in order to achieve the high PHY-layer security from the perspectives of BSs and users.

In this paper, we propose an opportunistic matrix precoding algorithm for non-separable wireless MIMO-NOMA networks. It is known that NOMA is beneficial when the difference between two channel gains is very large and therefore the two channels are separable. We study general wireless MIMO-NOMA networks in which the two channels might be non-separable. Based on channel state information, we formulate and solve a non-convex optimization problem in order to maximize the sum rate and provide guaranteed rate to the weaker user. We divide the non-convex optimization problem into two parts and propose algorithms for solving them. Our simulation results show that the proposed algorithm could significantly increase the sum rate for non-separable wireless MIMO- NOMA networks in comparison with a number of alternative approaches.

In this paper, we propose novel algorithms of beam-forming training for millimeter wave wireless communication systems. Instead of searching the whole codebook organized as a binary tree, we propose using Bayesian tree search algorithms to reduce the average delay of beamforming training. In addition, we design algorithms that derive an optimal threshold for the proposed opportunistic one-threshold tree search algorithm and an optimal pair of thresholds for the proposed opportunistic two-threshold tree search algorithm. Furthermore, we propose using quantization to efficiently calculate the likelihood ratio in the proposed opportunistic tree search algorithms. Our simulation results show that the proposed algorithms could significantly reduce the average delay of beamforming training.

In this paper, we propose the green stopping signal-centric predictive polling algorithm for machine type communications. Based on Markov optimal stopping theory, the green stopping signal-centric predictive polling algorithm exploits channel state information to reduce the energy consumption of wireless communications without knowing the mean channel gain. To reduce the computational complexity, the proposed algorithm is built upon a closed-form optimal stopping rule. Furthermore, we derive analytical results that characterize the optimal stopping time when the proposed green stopping signal-centric predictive polling algorithm is used. Simulation results show that the proposed approach could significantly reduce the average energy consumption at machines and the average signal prediction error at the base station in comparison with alternative approaches.

In this paper, we investigate the service capacity of a software-defined network with network function virtualization and service chaining. To obtain and achieve the service capacity, we formulate a mixed integer linear programming problem to jointly optimize the placement of virtual machines and the steering of network flows. In addition, we propose the greedy linear programming relaxation algorithm for efficiently obtaining solutions in three phases based on auxiliary linear programming problems and augmented service paths. We justify the usage of the proposed approach by analytical results and simulation results.

In this paper, we propose an optimization framework for selecting an optimal downlink beamforming vector and an optimal uplink transmission power level in full-duplex small cell wireless networks. We study the case in which the full-duplex base station is equipped with multiple antennas and each half-duplex UE is equipped with a single antenna. To benefit from recent progresses in all-digital self- interference cancellation, we formulate an optimization problem to achieve optimal beamforming and power control. Since the studied optimization problem is not convex, we first choose the optimal direction for the beamforming vector. Next, we adopt difference of concave programming to obtain the optimal magnitude of the beamforming vector and the optimal uplink transmission power level. Simulation results show that the proposed approach could significantly improve the performance of small cell wireless networks, especially when the residual self-interference is small.

In this paper, we propose using non-orthogonal multiple access to increase the throughput of wireless multicast. To achieve optimal user assignment and discrete power control, we formulate a combinatorial optimization problem. In particular, we concentrate on the general case in which an user cluster consists of an arbitrary number of users. To enhance scalability, we propose novel and efficient algorithms for finding out an optimal pair of user assignment and transmission power vector for NOMA multicast. Simulation results show that the proposed approach could significantly improve the multicast throughput.

In this paper, we investigate the service capacity of SDN-based cellular networks with service chaining and network function virtualization. Due to service chaining requirements, the service capacity is different from the well-known maximum-flow-minimum-cut capacity. To maximize the service capacity, we take a novel approach of traffic engineering. In particular, we formulate and solve a linear optimization problem to obtain optimal service paths for flows and an optimal bandwidth allocation for virtual machines. We also include numerical results that justify the usage of the proposed approach.

In this paper, we propose a signal-centric medium access control scheme that simultaneously exploits spatial and temporal correlations among sensing results for machine-to-machine communications. To model sensing results with spatial and temporal correlations, we propose using a vector autoregressive process. To minimize the overall prediction error, we propose using the space- time predictive polling algorithm. In addition, we derive the a-priori prediction error that is essential for making an optimal polling decision. Simulation results show that the proposed space-time predictive polling algorithm could significantly outperform algorithms that use only temporal correlation.

In this paper, we propose using software-defined networking technologies for optimal traffic engineering in cellular networks with service chaining. We study the case in which virtual machines are used to support network services and each flow requires multiple network services. To minimize the maximum load of virtual machines and guarantee that the sum rate of admitted flows is large enough, we formulate an optimization problem that is inherently different from the minimum cost flow problem. We transform the optimization problem to a linear programming problem, which can be solved in polynomial time. We also use numerical results to justify the usage of the proposed approach.

In this paper, we propose signal-centric predictive medium access control algorithms for machine-to-machine communications. In particular, the proposed signal-centric predictive algorithms exploit temporal correlations among sensing results to reduce the total prediction error at the base station. We propose polling algorithms for optimal performance and distributed algorithms for scalability. In addition, we derive analytical results that characterize the polling schedule when the proposed signal-centric predictive polling algorithm is used. Furthermore, we propose algorithms that strike a good balance between prediction error and fairness. Simulation results show that the proposed algorithms significantly reduce the overall prediction error.

In this paper, we propose two opportunistic polling algorithms for capacity-region-aware medium access control (MAC) in wireless networks with partial channel state information. Based on the probability density function of the unknown channel gain, the proposed algorithms opportunistically exploit the capacity region of a multiple access channel in network information theory and benefit from successive interference cancellation techniques. Simulation results show that both algorithms could increase the network throughput. Furthermore, the active opportunistic polling algorithm could increase the network throughput by more than 30%.

In this paper, we propose signal-centric polling-based medium access control algorithms for machine-to-machine communications networks in which the total number of machines is much larger than the total number of channels. To minimize the total prediction error of signals collected at the base station in each time slot, we propose the signal-centric predictive polling algorithm. To mitigate the starvation problem, we propose the weighted signal-centric predictive polling algorithm that strikes a good balance between prediction error and fairness. Analytical and simulation results are included to justify the usage of the proposed algorithms.

In this paper, we propose using group partition and dynamic rate adaptation for scalable throughput optimization of capacity-region-aware device-to-device communications. We adopt network information theory that allows a receiving device to simultaneously decode multiple packets from multiple transmitting devices, as long as the vector of transmitting rates is inside the capacity region. Based on graph theory, devices are first partitioned into subgroups. To optimize the throughput of a subgroup, instead of directly solving an integer-linear programming problem, we propose using a fast iterative algorithm to select active devices and using aggression levels for rate adaptation based on channel state information. Simulation results show that the proposed algorithm is scalable and could significantly outperform the greedy algorithm by more than 50%.

We propose a novel bargaining-game-based access network selection scheme for call requests in heterogeneous networks (HetNet). The bargaining game takes into consideration a number of factors including the mobility pattern of the mobile device, the load of the candidate access network, and the preference of the candidate access network to the call request. The studied bargaining game has a unique solution since the set of feasible payoff vectors is convex. Simulation results show that the proposed scheme could significantly outperform the TOPSIS scheme and the evolutionary game approach in terms of the handoff occurrence ratio and the call dropping probability.

In this paper, we propose a group partition approach for scalable throughput optimization of capacity-region-aware Device-to-Device communications. Instead of the conventional collision model, we adopt network information theory that allows a receiving device to simultaneously decode multiple packets from multiple transmitting devices, as long as the vector of transmitting rates is inside the capacity region. We propose a novel approach to partition devices into subgroups based on coloring a conflict graph and then solve an optimization problem for each subgroup. In particular, the formation of the conflict graph explicitly takes the capacity region into consideration. Simulation results show that the proposed approach could significantly outperform the greedy algorithm.

We propose adaptive capacity-region-aware algorithms for medium access control in wireless networks. In particular, the proposed algorithms are aware of the information-theoretic capacity region of a multiple access channel. According to the proposed algorithms, a node dynamically adjusts its channel access strategy based on the previous strategies of other nodes and the channel feedback. A strategy is composed of a transmission threshold and an aggression level. We propose symmetric learning algorithms for maximizing throughput. In addition, we propose asymmetric learning algorithms to strike a good balance between throughput and fairness. Furthermore, we propose novel methods to properly choose a finite number of available data transmission rates. We use both analytical results and simulation results to justify the usage of the proposed algorithms.

In this paper, we propose a novel approach for the analysis of large-scale mobile telecommunications social networks. To search for alpha users that are essential for social marketing, we propose using both graph theory and linear algebra. In particular, we first look for all trusses in a social graph based on graph theory and then find out alpha users in the trusses based on linear algebra. While there is no proof that the adjacency matrix of a graph always has strictly positive eigenvectors, we prove that the adjacency matrix of a truss always has positive eigenvectors. The proposed approach is scalable and can be used to analyze massive networks.

In this paper, we propose a novel energy-and-memory efficient location update scheme for wireless M2M communications. According to information theory, in comparison with periodically registering to the Machine-Type Communications (MTC) server, it is more efficient for a MTC device to perform location updates only when new nodes are added into the corresponding parsing tree. Based on the theory of random walks over trees, we propose optimally pruning the parsing tree to minimize the expected value of the sum of the memory cost and the energy cost for a MTC device. We use both analytical results and simulation results to justify the usage of the proposed scheme.

Signaling overhead reduction has been a key approach to realizing energy-efficient wireless cooperative communication systems. Motivated by the fact that consecutive temporal samples of real-world wireless channels are typically correlated, this paper proposes to exploit such time-domain correlation for relay training overhead reduction in cooperative networks. Specifically, we consider a cooperative transmit beamforming system, in which relay terminals employing the decode-and-forward protocol collaboratively transmit the source message according to the pre maximal-ratio-combining (pre-MRC) principle. During the training phase, the relays send training signals to aid channel estimation at the destination. Based on the acquired record of the channel state information (CSI), the destination then employs a linear minimum-mean-square-errors (LMMSE) channel predictor to update the CSI; in this way, training overhead dedicated by relays can then be reduced. We derive a closed-form expression for the receive SNR at the destination when the pre-MRC beamforming factors are computed in accordance with the predicted CSI. Our analytic results can be used for characterizing the performance degradation of channel prediction as the duration of the prediction phase is enlarged. The proposed analytic studies are corroborated by numerical simulations.

In this paper, we propose the tree/stack splitting with remainder algorithm for distributed medium access control in a wireless network with multipacket reception. In order to reduce the length of a cycle and increase the network throughput, when the splitting with remainder algorithm is used, some nodes that attempt to transmit packets at the beginning of a cycle might have to postpone their packet retransmissions until the beginning of the next cycle. We demonstrate that the splitting with remainder algorithm outperforms the erasure algorithm and the probe algorithm. For the splitting with remainder algorithm, we analytically and accurately derive the network throughput and the average packet delay. We show that our analytical results are consistent with packet-based simulation results.

In this paper, we propose an analytical approach for performance evaluation of the classic tree/stack splitting algorithm in an interference-dominating wireless access network with random traffic and finite nodes. In an interference-dominating wireless access network, a receiver could simultaneously receive multiple packets from a variety of transmitters, as long as the signal-to-interference-plus-noise ratio exceeds a predetermined threshold. We use discrete-time Markov chains and regenerative processes to derive the throughput curve, the packet blocking probability, the average system size, and the average packet delay. We show that the exact performance of the splitting algorithm depends on the total number of nodes in the network. We verify our numerical results by rigorous mathematical proof and computer simulations.

In this paper, we propose and evaluate algorithms for solving the worst-case-coverage deployment problem in ad-hoc wireless sensor net works. The worst-case-coverage deployment problem is to deploy additional sensors in the wireless sensor field to optimize the worst-case coverage. We derive a duality theorem that reveals the close relation between the maximum breach path and the minimum Delaunay cut. The duality theorem is similar to the well-known max-flow-mm-cut theorem in the field of network optimization. The major difference lies in the fact that the object function we study in this paper is nonlinear rather than linear. Based on the duality theorem, we propose an efficient dual algorithm to solve the worst- case-coverage deployment problem. In addition, we propose a genetic algorithm for deploying a number of additional sensors simultaneously. We use analytical proofs and simulation results to justify the usage of the proposed approaches

In this paper, we propose the predictive multicast polling scheme for medium access control in wireless networks with multipacket reception capability. We concentrate on the case in which the packet arrival process is general and the maximum queue size is finite but larger than one. We derive both analytical results and simulation results. We use the theory of discrete-time Markov chain to analyze the evolution of the system state. In addition, we propose to use Markov reward processes to calculate the exact value of the network throughput. Furthermore, we obtain the average system size, the packet blocking probability, and the average packet delay. We show that our numerical results are consistent with simulation results. We also use simulation results to justify the usage of the proposed approach. Our study shows that the system performance can be significantly improved with a few additional buffers in the queues.

In this paper, we propose concurrently searching for correlated mobile users in mobile communications networks. Previous work either focuses on locating a single mobile user or assumes that the locations of mobile users are statistically independent. We first propose a mobility model in which the movements of mobile users are statistically correlated. Next, we use the theory of Markov chain to derive the joint probability density function of the locations of mobile users. In addition, we propose a novel approach to discover the correlations among the locations of mobile users without explicitly calculating the joint probability density function. Our simulation results indicate that exploring the correlations among the locations of mobile users could significantly reduce the average paging delay and increase the maximum stable throughput.

In this paper, we propose to concurrently search for a number of mobile users in a wireless cellular network based on the probabilistic information about the locations of mobile users. The concurrent search approach guarantees that all k mobile users will be located within k time slots. It is shown that even in the worst case when mobile users appear equally in all the cells of the network, the concurrent search approach is able to reduce the average paging cost by 25%. More importantly, this is achieved without an increase in the worst case paging delay or in the worst case paging cost. Depending on the total number of mobile users to be located, total number of cells in the network, and the probabilistic information about the locations of mobile users, the reduction of the average paging cost due to the usage of the concurrent search approach ranges from 25% to 88%. The case in which perfect probabilistic information is unavailable is also studied.

In this paper, we study the impact of heterogeneous receivers on the throughput of multicast flow control and propose a new multicast flow control algorithm to optimally partition group members into multiple subgroups. Our main contributions are as follows. First, we cast the multicast flow control problem in the Internet as the list partition problem and then prove that the list partition problem is equivalent to the optimal paging problem in cellular networks. The result is not only interesting in itself but also essential to derive the first known analytical bounds for the throughput of multicast flow control. Furthermore, we propose an algorithm to solve not only the list partition problem but also the optimal paging problem and the problem of bulk data transfer using multiple multicast groups. The complexity of our algorithm is one order less than the best known algorithm designed only for the problem of bulk data transfer using multiple multicast groups in the literature. While earlier work uses simulations to justify the usage of multiple subgroups to deliver information to a large amount of receivers in heterogeneous networks, we provide the first analytical support.

We present some results regarding the paging cost gains and the cost-delay tradeoff that can be achieved by using sequential paging to locate mobile users in cellular networks. We describe tight bounds on the average paging cost and the paging delay and quantify the intuition that greater gains are achieved when the mobile user's location probabilities are concentrated in a small portion of the location area. We also examine the impact of errors in the location estimates on the paging cost.