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Being an effective non-orthogonal multiple access (NOMA) technique, sparse code multiple access (SCMA) is promising for future wireless communication. Compared with orthogonal techniques, SCMA enjoys higher overloading tolerance and lower complexity because of its sparsity. In this paper, based on deterministic message passing algorithm (DMPA), algorithmic simplifications such as domain changing and probability approximation are applied for SCMA decoding. Early termination, adaptive decoding, and initial noise reduction are also employed for faster convergence and better performance. Numerical results show that the proposed optimizations benefit both decoding complexity and speed. Furthermore, efficient hardware architectures based on folding and retiming are proposed. VLSI implementation is also given in this paper. Comparison with the state-of-the-art have shown the proposed decoder's advantages in both latency and throughput (multi-Gbps). [ABSTRACT FROM AUTHOR]

In this paper, we study the resource allocation problem for downlink multicarrier non-orthogonal multiple access systems with short-packet communications (MC-NOMA-SPC). In contrast to long-packet communications in conventional wireless systems, SPC suffers from a transmission rate degradation and an unavoidable decoding error rate. Thus conventional resource allocation based on the Shannon capacity assuming infinite blocklength is no longer optimal. In this paper, we propose to use the effective-throughput as the performance metric to evaluate the tradeoff between the transmission rate and the decoding error rate. Building on that, we jointly optimize the subcarrier assignment, transmission power allocation, and transmission rate adaptation of each user to maximize the total weighted effective-throughput subject to transmission reliability and various practical constraints. Since the formulated problem belongs to a non-convex mixed integer nonlinear programming (MINLP) problem, we develop an efficient algorithm based on the dynamic programming (DP) recursion framework to obtain its optimal solutions. We further propose a low-complexity algorithm based on the principles of block coordinate descent (BCD) and concave-convex procedure (CCCP). Finally, the simulation results show that the proposed low-complexity algorithm can achieve similar performance as that of the optimal solution and outperform the other baseline schemes significantly. [ABSTRACT FROM AUTHOR]

In this paper, an intelligent reflecting surface (IRS) is deployed to assist the terahertz (THz) communications. The sum-rate of user equipments (UEs) is maximized while guaranteeing the rate requirement of each UE. A block coordinate searching (BCS) algorithm is proposed to jointly optimize the IRS’s coordinates, phase shifts, THz sub-bands allocation and power control. Specifically, the relaxation with penalties based (RPB) algorithm is developed to obtain feasible coordinates of the IRS and guarantee the monotonicity of objective value. In addition, the IRS phase shifts are formulated as closed-form expressions with introduced pricing factors. Simulation results show that the proposed scheme can significantly enhance system performance. [ABSTRACT FROM AUTHOR]

In this paper, we consider a computation offloading problem in a three-tier network consisting of multiple mobile users (MUs), multiple edge clouds, and a central cloud. On this basis, we formulate a system Energy Time Cost ($ETC$) minimization problem by jointly optimizing the resource allocation, mobile-edge matching decision, and offloading decision. Due to the difficulty of directly solving the formulated problem, we decompose it and propose two offloading algorithms, namely Gale-Shapley based Minimum/Sequential Offloading Algorithm (GS-MOA/GS-SOA), to optimize offloading decisions by minimizing the system $ETC$ , in which the mobile-edge matching strategies based on the proposed Gale-Shapley based Mobile-Edge mAtching aLgorithm (GS-MEAL) and resource allocations are optimized iteratively. The simulations show the effectiveness of our proposed algorithm. [ABSTRACT FROM AUTHOR]

Terahertz (THz) band communication is envisioned as a key technology to satisfy the increasing demand for ultrabroadband wireless systems, thanks to its ultrabroad bandwidth. Tailored for the unique properties of pulse-based communications in the THz band, two timing acquisition algorithms are proposed and analyzed thoroughly in this paper. First, a low-sampling-rate (LSR) synchronization algorithm is proposed, by extending the theory of sampling signals with finite rate of innovation in the communication context and exploiting the properties of the annihilating filter. The simulation results show that the timing accuracy at an order of ten picoseconds is achievable. In particular, the LSR algorithm has high performance with uniform sampling at $1/20$ of the Nyquist rate when the signal-to-noise ratio (SNR) is high (i.e., greater than 18 dB). Complementary to this, a maximum likelihood (ML) approach for timing acquisition is developed, which searches for the timing offsets by adopting a two-step acquisition procedure based on the ML criterion. The simulation results show that the ML-based algorithm is well suitable in the low SNR case with a half-reduced search space. For further evaluation, the error performance and the resulting bit-error-rate sensitivity to the timing errors in the LSR and the ML algorithms are both analytically and numerically studied. This paper provides very different and promising angles to efficiently and reliably solve the timing acquisition problem for pulse-based THz band wireless systems. [ABSTRACT FROM PUBLISHER]

This paper considers optimal joint time, frequency, and power allocation for energy-efficient device-to-device (D2D) communications in an overlay cellular network, where multiple D2D transmitters communicate with their corresponding D2D receivers while utilizing the energy harvested from a cellular base station (BS). Due to channel estimation error and feedback delays, the channel state information (CSI) available at the transmitters is usually imperfect, which can significantly deteriorate the energy efficiency (EE) of the considered system. To mitigate the impact of imperfect CSI, we formulate a non-convex robust optimization problem for the maximization of the EE (in bits/Joule) of the system while ensuring quality of service (QoS) requirements on the minimum data rata and the minimum amount of harvested energy for cellular and D2D users, respectively. We reveal that the optimal energy-efficient D2D transmission should exhaust all the harvested energy of each D2D transmitter. Exploiting this fact, we propose an efficient iterative algorithm for obtaining the optimal solution. To strike a balance between performance and computational complexity, we further propose a low-complexity suboptimal algorithm. Simulation results show that compared with several baseline schemes, both the proposed optimal and suboptimal algorithms can significantly improve the EE of wireless-powered D2D communications under imperfect CSI. Moreover, the proposed suboptimal algorithm is close-to-optimal when the number of cellular users are small or the channel estimation error is large. [ABSTRACT FROM AUTHOR]

In this paper, we study the distributed precoding problem for simultaneous wireless information and power transfer (SWIPT) in decode-and-forward (DF)-based multiple-input–multiple-output (MIMO) relay networks. The system model considered here consists of a source, a relay, an information decoding (ID) receiver, and an energy harvesting (EH) receiver, all mounted with multiple antennas. Since full channel state information (CSI) is usually unavailable, a practical scenario, where only local CSI is required, is considered in this paper. Such a practical scenario naturally constitutes a noncooperative game, where the source and the relay can be regarded as two rational game players. With properly designed utilities, it can be further shown that the existence and uniqueness of the pure-strategy Nash equilibrium (NE) of the proposed game can both be guaranteed under some mild conditions. Therefore, a distributed iterative precoding algorithm can be developed based on the best-response dynamic to obtain the unique NE solution for the proposed game. Moreover, a proximal-point-based regularization approach is also pursued to ensure the convergence of the proposed algorithm without requiring special restrictions on the channel ranks. Numerical simulations are also provided to demonstrate the proposed algorithm. Results show that our algorithm can converge quickly to a satisfactory solution with guaranteed convergence. [ABSTRACT FROM PUBLISHER]

Deep neural networks (DNNs) have been widely applied for classification tasks in wireless communications such as link scheduling, user association and base station (BS) muting. When training a DNN using datasets with highly-skewed class distribution where most data belong to a few majority classes, learning performance for minority classes will degrade since the imbalanced data forces the training process to be biased towards the majority classes. In many wireless problems, multiple decisions for classification are made jointly, say deciding which BSs in a network should be muted to avoid interference. In this paper, we employ DNN to learn an optimal predictive interference coordination policy, and strive to avoid biased learning in such a multi-label multi-class classification problem. The major contribution is proposing a training method to encourage fairness among classes by minimizing the maximal cost of decisions among classes, which is converted into a problem to optimize the weighting factors on the training cost of each class. We provide a gradient descent algorithm to optimize the weighting factors and the model parameters of the DNN by alternative updates. Simulation results show that the training method can improve the learning performance for the minority class and can achieve higher network utility than existing training methods. [ABSTRACT FROM AUTHOR]

In this paper, we study the beam synthesis and management methods for the phased antenna array-based wireless communications systems, e.g., 5 G mmWave communications. First, we propose a novel on-off beam synthesis method that can synthesize any given on-off beam pattern. The proposed method maximizes the beam power towards a given set of on-directions while suppressing the power towards all off-directions. Second, by using a set of beams synthesized by the proposed on-off beam synthesis method, we design a non-interactive hierarchical beam management algorithm that can greatly reduce the radio resource usage, feedback overhead, and the latency of the beam discovery, compared to the exhaustive beam search algorithms. Since the proposed beam management algorithm is non-interactive, it can be applied to the initial access as well as the beam tracking, and is especially useful for supporting a massive number of devices. Finally, we show the validness of the proposed beam synthesis and management methods by conducting experiments on a phased antenna array experimental testbed. [ABSTRACT FROM AUTHOR]

Wireless network communication has developed rapidly in recent years, especially in the field of Internet of vehicles (IoV). However, due to the limitations of traditional network architecture, resource scheduling in wireless network environment is still facing great challenges. We focus on the urgent need of users for bandwidth resources in the IoV scenario under virtual network environment. This paper proposes a bandwidth aware multi domain virtual network embedding (BA-VNE) algorithm. The algorithm is mainly aimed at the problem that users need a lot of bandwidth in wireless communication mode, and solves the problem of bandwidth resource allocation from the perspective of virtual network embedding (VNE). In order to improve the performance of the algorithm, we introduce particle swarm optimization (PSO) algorithm to optimize the performance of the algorithm. In order to verify the effectiveness of the algorithm, we have carried out simulation experiments from link bandwidth, mapping cost and virtual network request (VNR) acceptance rate. The final results show that the proposed algorithm is better than other representative algorithms in the above indicators. [ABSTRACT FROM AUTHOR]

The multi-UAV network is promising to extend conventional networks by providing broader coverage, and better reliability. Nevertheless, the broadcast nature of wireless signals, and the broader coverage expose multi-UAV communications to the threats of passive eavesdroppers. Recent studies mainly focus on securing a single legitimate link, or communications between a UAV, and multiple ground users in one/two-UAV-aided networks, while the physical layer secrecy analysis for hierarchical multi-UAV networks is underexplored. In this paper, we investigate a general two-tier UAV network consisting of multiple UAV transmitters (UTs), and multiple UAV receivers (URs) in the presence of multiple UAV eavesdroppers (UEs). To protect all legitimate UT-UR links against UEs at the physical layer, we design a two-stage framework consisting of a UT-UR association stage, and a cooperative transmission stage. Specifically, we formulate the secure transmission problem into a many-to-one matching game followed by an overlapping coalition formation (OCF) game, taking into account the limited capabilities, and the throughput requirements of URs, as well as the transmission power constraints of UTs. A matching algorithm, and an OCF algorithm are proposed to solve these two sequential games whose convergences, and stabilities are guaranteed theoretically. Simulation results show the superiority of our algorithms, and the effectiveness of our two-stage game framework in the terms of secrecy performance. [ABSTRACT FROM AUTHOR]

Next-generation wireless communications are expected to provide reliable high-rate connectivity. The recent concept of fog-enabled architecture comes to rescue by invoking a substantial amount of data storage close to end-user devices for meeting these challenges. Thus, caching popular contents at heterogeneous devices, e.g., fog nodes (FNs) or fog access points (FAPs), constitutes a promising technique of reducing both the traffic and the energy consumption of the backhaul links. Therefore, in this paper, we propose an energy-efficient caching and node association algorithm for cache-aided fog networks. First, we solve the problem of energy-efficient content caching and delivery in the FNs/FAPs in conjunction with a fixed node association strategy, where the FNs communicate either with other FNs by node-to-node (N2N) communications or with the FAPs in their proximity. In both caching scenarios, we investigate the relationship between the caching probability of the file and the energy-efficient content delivery by formulating the associated energy efficiency (EE) optimization problem under caching memory constraints. Then, we derive a joint modulation mode allocation strategy and caching policy for each content caching node and conceive a joint node association and caching algorithm for maximizing the EE. Finally, we quantify both the overall EE and throughput for demonstrating that the proposed caching and transmission strategy achieves significant performance improvements. [ABSTRACT FROM AUTHOR]

This paper studies the information-energy ($\mathrm{I}$ - $\mathrm{E}$) region for simultaneous wireless information and power transfer (SWIPT) networks in mobility scenarios, where a moving transmitter simultaneously transmits information and power to a fixed receiver, and the receiver adopts power splitting (PS) receiving architecture. In order to characterize the tradeoff between the received information and harvested energy, the $\mathrm{I}$ - $\mathrm{E}$ region is defined, and corresponding optimization problems are formulated to explore the system $\mathrm{I}$ - $\mathrm{E}$ regions by jointly optimizing the transmit power at the transmitter and the PS ratio at the receiver with three popular energy harvesting (EH) models, i.e., traditional linear EH model, the logistic nonlinear EH model and the piecewise nonlinear EH model. Particularly, to efficiently solve the nonconvex optimization problem with the logistic nonlinear EH model, a successive convex approximate (SCA)-based algorithm is proposed, which is able to characterize the lower bound of the $\mathrm{I}$ - $\mathrm{E}$ with low complexity. To solve the optimization problems with the linear and piecewise EH models, as they are convex, some closed and semi-closed solutions are derived by using Lagrange dual method and KKT conditions. Numerical results show that compared with the $\mathrm{I}$ - $\mathrm{E}$ regions under the linear and piecewise EH models, that under the logistic nonlinear EH model is smaller due to the limitations of practical EH circuit features. Moreover, with a fixed moving speed, when the transmit power is relatively large, the logistic nonlinear EH model can be replaced with the piecewise one due to the relatively small gap between their achieved $\mathrm{I}$ - $\mathrm{E}$ regions. Additionally, for a fixed moving track length, the higher moving speed yields the smaller $\mathrm{I}$ - $\mathrm{E}$ region with all three EH models. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a promising paradigm to connect massive number of devices in future wireless communications while satisfying various quality of service (QoS) requirements. In this paper, we consider a QoS-constrained IoT system operating with finite blocklength (FBL) codes to support low latency communications. Two data arrival models are considered, namely, constant-rate arrival and ON-OFF discrete-time Markov arrival. The throughput performance is studied for both arrival models under statistical queuing constraints and deadline limits. For the scenario with instantaneous channel state information (CSI), we derive the QoS-constrained throughput expressions for both arrival models. Subsequently, an instantaneous-CSI-driven optimal power control algorithm is proposed to maximize the throughput, while guaranteeing a certain reliability target. In addition, we consider a scenario with only average CSI being available at the transmitter and propose to apply hybrid automatic repeat request (HARQ) schemes to improve the FBL performance. The decoding error probability and the outage probability are first characterized, following which the distribution of transmission period is derived. Furthermore, the throughput expressions are provided for both types of arrivals. Via numerical analysis, the impact of error probability, fixed transmission rate, coding blocklength, and QoS constraints on the throughput is studied. [ABSTRACT FROM AUTHOR]

Opportunistic beamforming (OBF) is an attractive research topic in wireless communications, which can provide multi-user diversity with a low complexity. The conventional OBF technique requires a large number of waiting users, and cannot simultaneously support multiple users. In order to solve the mentioned problem, this paper proposes a downlink multi-user multi-weight opportunistic beamforming non-orthogonal multiple access (MW-OBF-NOMA) system. An objective function is formulated to maximize the sum spectrum efficiency (SE) of the proposed system, however, it is a non-convex issue and intractable to derive an optimal solution. To solve this non-convex issue, we divide it into two sub-optimal issues and solve them one-by-one. Based on the solutions of the two sub-optimal issues, a joint iterative resource allocation algorithm is presented. Moreover, we discuss the influences of signal-to-noise ratio (SNR) estimation, and deduce a closed-form expression of bit error ratio (BER). Numerical results show that our proposed system can simultaneously serve multiple users without extra resources, and achieve an improved sum SE with a low complexity. [ABSTRACT FROM AUTHOR]

The new radio (NR) system is recently proposed to deploy alongside the long term evolution (LTE) system to improve the wireless communication capacity. The Vandermonde-subspace frequency division multiplexing (VFDM) is an efficient technique for such a two tier network, whereby the NR transmitter can reuse the frequency spectrum of LTE system meanwhile guaranteeing interference free to LTE. In this paper, we first investigate the robust rate-maximization problem in the VFDM system. In the condition of imperfect channel state information (CSI), the precoder is optimized to satisfy both non-interference to LTE system and rate-maximization in the NR system. To tackle the challenge in solving such a non-convex problem, an iterative algorithm is proposed based on the block coordinate descent (BCD) and penalty dual decomposition (PDD) methods. Finally, simulation results demonstrate the superiority of the proposed algorithm in terms of robustness and achievable rate over the conventional SVD-based VFDM technique. [ABSTRACT FROM AUTHOR]

Wireless vehicle-to-vehicle (V2V) and vehicle-toinfrastructure (V2I) communication holds great promise for significantly reducing the human and financial costs of vehicle collisions. A common characteristic of this communication is the broadcast of a device's core state information at regular intervals (e.g., vehicle speed and location or traffic signal state and timing). Unless controlled, the aggregate of these broadcasts will congest the channel under dense traffic scenarios, reducing the effectiveness of collision avoidance applications that use transmitted information. Active congestion control using distributed techniques is a topic of great interest for establishing the scalability of this technology. This paper defines a new adaptive congestion control algorithm that can be applied to the message rate of devices in this vehicular environment. While other published approaches rely on binary control, the LInear MEssage Rate Integrated Control (LIMERIC) algorithm takes advantage of full-precision control inputs that are available on the wireless channel. The result is provable convergence to fair and efficient channel utilization in the deterministic environment, under simple criteria for setting adaptive parameters. This “perfect” convergence avoids the limit cycle behavior that is inherent to binary control. We also discuss several practical aspects associated with implementing LIMERIC, including guidelines for the choice of system parameters to obtain desired utilization outcomes, a gain saturation technique that maintains robust convergence under all conditions, convergence with asynchronous updates, and using channel load to determine the aggregate message rate that is observable at a receiver. This paper also extends the convergence analysis for two important cases, i.e., measurement noise in the input signal and delay in the update process. This paper illustrates key analytical results using MATLAB numerical results and employs standard NS-2 simulations to demonstrate the performance of LIMERIC in several high-density scenarios. [ABSTRACT FROM PUBLISHER]

In this paper, we model a vehicular multihop network as an evolving graph and formulate the problem of optimal data dissemination over the network in terms of minimum number of transmissions. We show that the problem belongs to the NP complexity class and provide an easy to implement polynomial-time \2 \tau H(\Delta)-approximation algorithm, where \tau is the number of different subgraphs that comprise the evolving graph, and H(\Delta) is the harmonic number of the degree of the evolving graph. By means of applying our heuristic over a vehicular scenario generated by a microscopic road-traffic simulator, we provide some insight into the data dissemination issue. In addition, the proposed algorithm is employed to benchmark a state-of-the-art communication protocol. We hope this paper will inspire more efficient heuristics and data dissemination solutions. [ABSTRACT FROM PUBLISHER]

This paper proposes a scheme for constructing an Internet Protocol (IP) version 6 over low-power wireless personal area networks (6LoWPAN) wireless sensor network based on a cluster tree. In the cluster generation algorithm, a cluster tree is always an isolated node with the maximum number of neighbor isolated nodes that launches the cluster generation process; therefore, the total number of cluster heads is minimized. In the cluster-tree construction algorithm, the cluster-tree architecture is proposed, and the cluster-tree construction algorithm minimizes the total number of nodes included in a cluster tree; therefore, the routing cost is reduced. In the cluster-tree repair algorithm, when a cluster head or a cluster associate node fails or moves, a new cluster head or cluster associate node is elected to maintain the cluster-tree topology. This paper compares the performance parameters of Least Cluster Change algorithm with Lowest-ID, Root-based Lowest-ID cluster constructing mechanism, and the proposed scheme, including the cluster-tree topology stability, the routing memory cost, and the routing update cost. The analytical data show that the performance of the proposed scheme is better. [ABSTRACT FROM PUBLISHER]

Location estimation and tracking for mobile stations have attracted a significant amount of attention in recent years. Different types of signal sources are considered available to provide measurement inputs for location estimation and tracking in heterogeneous wireless networks. Various techniques have been studied and combined for location tracking, e.g., the least square methods for location estimation associated with the Kalman filters for location tracking. In this paper, the hybrid unified Kalman tracking (HUKT) technique is proposed to provide an integrated algorithm for precise location tracking based on both time of arrival (TOA) and time difference of arrival (TDOA) measurements. A new variable is incorporated as an additional state within the Kalman filtering formulation to consider the nonlinear behavior in the measurement update process. The relationship between this new variable and the desired location estimate is applied in the state update process of the Kalman filter. Three different designs of hybrid factor are proposed to adaptively adjust the weighting value between the TOA and TDOA measurements. Moreover, similar concepts are also utilized in the design of unified Kalman tracking schemes for pure TOA and TDOA measurement inputs in this paper. Compared with existing schemes, numerical results illustrate that the proposed HUKT algorithm can achieve enhanced accuracy for mobile location tracking, particularly under environments with an insufficient number of measurements in one of the signal paths. [ABSTRACT FROM PUBLISHER]

In this paper, an initial attempt to exploit the benefits of machine learning in solving relay selection for dualhop networks has been made. From data-driven perspective, we convert relay selection to multiclass-classification problem and propose a decision-tree-based selection scheme. Input features are generated by binary quantization to the equivalent channel state information of each relay, so that the dependence of relay selection on maximization algorithm is removed. To optimize quantization parameter, we derive and maximize two splitting criteria—information gain and Gini index. Experimental results show that decision-tree-based scheme achieves comparable performance to optimal selection in terms of average end-to-end rate while reducing computational complexity and feedback amount. [ABSTRACT FROM AUTHOR]

Sparse code multiple access (SCMA) is one type of non-orthogonal multiple access techniques for the fifth-generation wireless communication networks. Existing multiuser detection algorithms for SCMA uplink systems are based on the assumption that signals are in synchronization. However, asynchrony cannot be neglected due to the propagation delays in practical environments. In this paper, we present three detection algorithms for uplink asynchronous SCMA systems, namely belief propagation message passing algorithm, belief propagation with equal gain combining message passing algorithm, and partial phase-coherent algorithms. Simulation results show that our proposed asynchronous detection algorithms are able to offer descent bit error rate performances, with only a small performances loss compared to the synchronous case. Moreover, our proposed algorithms converge well in the uplink asynchronous SCMA systems. [ABSTRACT FROM AUTHOR]

This paper proposes a zone-based approach referred to as zone-based GrEEn (ZBGrEEn) algorithm, for energy-efficient spectrum sharing (SS) networks. The SS network is considered to encompass device-to-device (D2D) transmissions between a single primary transmitter (PT) and multiple primary receivers (PRs), placed in distinctive zones around the PT. Power allocation is performed optimally so that the target rate of each application demanded by the PRs are adequately met. When the PT fails to meet the target rates over ${\rm{PT}} \to {\rm{PR}}$ D2D links, it transmits cooperatively through an appropriate secondary transmitter (ST) in the SS network, which acts as an amplify-and-forward (AF) relay. The ST's basis for selection is the ${\rm{PT}} \to {\rm{ST}}$ channel state and power level at the ST. Use of game theory for solving the energy efficiency (EE) maximization problem has also been quantified. ZBGrEEn introduces zone factor for determining the transmission time for each zone in the SS network. This time determines the energy consumption at the user terminals, thereby impacting its battery lifetime. The efficiency of the proposed scheme has been validated through simulation results, with the enhancement in battery lifetime of up to 46.28% achievable for the PT. Game theory is also observed to be rewarding in battery lifetime augmentation, especially for ultradense networks (UDNs). Performance of other user terminals in the network can be similarly evaluated. [ABSTRACT FROM AUTHOR]

The portfolio of models offered by car manufacturing groups often includes many variants (i.e., different car models and their versions). With such diversity in car models, variant management becomes a formidable task. Thus, there is an effort to keep the variants as close as possible. This simple requirement forms a big challenge in the area of communication protocols. When several vehicle variants use the same signal, it is often required to simultaneously schedule such a signal in all vehicle variants. Furthermore, new vehicle variants are designed incrementally in such a way as to maintain backward compatibility with the older vehicles. Backward compatibility of time-triggered schedules reduces expenses relating to testing and fine-tuning of the components that interact with physical environment (e.g., electromagnetic compatibility issues). As this requirement provides for using the same platform, it simplifies signal traceability and diagnostics, across different vehicle variants, besides simplifying the reuse of components and tools. This paper proposes an efficient and robust heuristic algorithm, which creates the schedules for internal communication of new vehicle variants. The algorithm provides for variant management by ensuring compatibility among the new variants, besides preserving backward compatibility with the preceding vehicle variants. The proposed method can save about 20% of the bandwidth with respect to the schedule common to all variants. Based on the results of the proposed algorithm, the impact of maintaining compatibility among new variants and of preserving backward compatibility with the preceding variants on the scheduling procedure is examined and discussed. Thanks to the execution time of the algorithm, which is less than one second, the network parameters like the frame length and cycle duration are explored to find their best choice concerning the schedule feasibility. Finally, the algorithm is tested on benchmark sets. [ABSTRACT FROM AUTHOR]

Due to emerging mobile applications and an Internet of Things, data traffic carried by wireless networks has increased dramatically. As a result, maximizing the utilization of the limited network resource is a top priority. To date, there exists a large body of work on the stochastic network utility maximization (NUM) problem. On the one hand, the stochastic NUM problem and its associated solutions have been mainly investigated under the light-tailed (LT) data condition. On the other hand, empirical results show that data delivered by wireless networks is not only large in volume but also highly variable, which can be characterized by heavy-tailed (HT) distributions. Such HT traffic exhibits high burstiness and strong temporal correlations, which are fundamentally different from the behavior of LT traffic. In this paper, first it is shown that the classic stochastic gradient algorithms (SGAs), as effective solutions for stochastic NUM problems, fail to prevent HT traffic flows from aggressively competing with LT flows for limited resources. This leads to unbounded queueing delay for LT flows, which results in network instability. To counter such a challenge, the time-average stochastic gradient routing algorithm is proposed, which prevents competition among HT and LT traffic flows so that utility optimality and network stability can be achieved simultaneously. Moreover, because HT data have unbounded moments, such as mean and variance, it is not feasible to apply the conventional convergence analysis, which assumes the moment boundedness of traffic arrivals. To address this problem, the ordinary differential equation method is adopted to prove that the proposed algorithm still converges even with highly variable HT traffic. Comprehensive simulation results are shown to verify the derived theoretical results. [ABSTRACT FROM AUTHOR]

Caching in wireless device-to-device (D2D) networks can bring contents closer to users and reduce backhaul traffic. However, the major obstacle to realize cache-enabled D2D communications is the selfish nature of users. In this paper, we propose an incentive mechanism for pricing the contributions of D2D transmitters in cache-enabled D2D-underlaid cellular networks modeled by stochastic geometry. A Stackelberg game is employed to resolve the conflict of interests between the operator and D2D transmitters. The main objective of this game is to reach the Stackelberg equilibrium, in which both the operator and D2D transmitters achieve the maximum profit by designing the optimal incentive price and the optimal caching strategy. We show the existence of the equilibrium and propose a low-complexity algorithm to obtain the near-optimal caching strategy and incentive price. Numerical results verify the existence of the SE and the advantages of the proposed scheme in terms of the utilities for the operator and D2D transmitters over existing caching schemes with fixed incentive. [ABSTRACT FROM AUTHOR]

The cross-layer utility maximization problem, which is subject to stability constraints for a multicommodity wireless network where all links share the same number of orthogonal channels, is considered in this paper. We assume a time-slotted network, where the channel gains randomly change from one slot to another. The optimal cross-layer network control policy can be decomposed into the folloing three subproblems: 1) flow control; 2) next-hop routing and in -node scheduling; and 3) power and rate control, which is also known as resource allocation (RA). These subproblems span the layers from the physical layer to the transport layer. In every time slot, a network controller decides the amount of each commodity data admitted to the network layer, schedules different commodities over the network's links, and controls the power and rate allocated to every link in every channel. To fully exploit the available multichannel diversity, we consider the general case, where multiple links can be activated in the same channel during the same time slot, and the interference is controlled solely through power and rate control. Unfortunately, the RA subproblem is not yet amendable to a convex formulation, and in fact, it is NP-hard. The main contribution of this paper is to develop efficient RA algorithms for multicommodity multichannel wireless networks by applying complementary geometric programming and homotopy methods to analyze the quantitative impact of gains that can be achieved at the network layer in terms of end-to-end rates and network congestion by incorporating different RA algorithms. Although the global optimality of the solution cannot be guaranteed, the numerical results show that the proposed algorithms perform close to the (exponentially complex) optimal solution methods. Moreover, they efficiently exploit the available multichannel diversity, which provides significant gains at the network layer in terms of end-to-end rates and network congestion. In addition, the assessment of the improvement in performance due to the use of multiuser detectors at the receivers is provided. [ABSTRACT FROM PUBLISHER]

With the rapid emergence of applications in Mobile Opportunistic Networks (MONs), understanding and characterizing their properties becomes extremely important. A fundamental model for MONs is time-varying graph, which currently remains poorly understood since many well-recognized properties of static graphs have no obvious counterpart in dynamic ones. In MONs, the dynamical links change opportunistically and usually the system energy is very limited, which results in unique and unknown properties about the network connectivity and reachability. In this paper, taking the communication energy into account, we introduce the concept of Energy-aware Temporal Reachability Graphs (ETRG), which characterizes the connectivity of MONs with the consideration of communication consumed energy, and consequently reveals the communication capabilities of MONs with the given of real-world system parameters of data size, tolerable delay, and energy budget. We come up with efficient algorithm to calculate ETRG from the corresponding time-varying graphs. By applying ETRG to several mobile networks recorded by real-life human and vehicular mobility traces, we characterize their network connectivity properties in terms of average reachability, communication asymmetry, and stability. Moreover, utilizing ETRG that places upper bounds of the communications capabilities, we reveal the fundamental relations and tradeoffs among large-scale variability of the system metrics of energy budget, tolerable delay, and data size on the system performance. [ABSTRACT FROM AUTHOR]

As an important complement and enhancement technique for cellular communication systems, the device-to-device (D2D) technique has exhibited a number of benefits in terms of, for instance, offering an improved spectral efficiency, enabling a reduced transmit power and providing an enhanced channel capacity, etc. However, a severe interference may be imposed on the conventional cellular users by the activated D2D links, thus, substantially eroding the performance of the former. In this paper, we propose an interference limited area (ILA) based D2D-management scheme along with an appropriately designed power control algorithm for mitigating the above-mentioned interference. We first derive the closed-form expressions of the average coverage probability for both the cellular and D2D links under the proposed ILA scheme, followed by proposing a resource-allocation algorithm subject to a power control mechanism. Numerical results show that the proposed ILA scheme is capable of outperforming the conventional scheme in terms of both the coverage probability (i.e., for both cellular and D2D links) and the sum data rate, particularly in the scenarios with a high D2D density and a high signal-to-interference-plus-noise ratio. [ABSTRACT FROM AUTHOR]

Short packet communications (SPC) will play an important role in future Internet-of-Things networks. Conventional pilot-assisted transmission (PAT) needs significant overhead to obtain accurate channel state information (CSI) for further symbol detection and bit recovery, thereby reducing the spectral efficiency of the transmission. In this paper, a machine learning framework called Label-Assisted Transmission is proposed, in which the received signals are grouped into clusters through clustering algorithms and known labels are transmitted for cluster-symbol/bits mapping. This novel framework supports bit recovery directly without requiring the bit-symbol mapping information. When such mapping information is available, modulation constrained (MC) clustering algorithms are proposed, which exploit the unique characteristics of digital communication signals. For frequency-flat channels, this novel design needs only one known label regardless of the modulation size and the missing labels can be reconstructed using a proposed label reconstruction scheme. For frequency-selective channels with $L$ -tap time domain channel responses, only $L$ known labels are needed to reconstruct the missing labels if orthogonal frequency division multiplexing technology is adopted. The proposed clustering receiver works well even when the number of clusters is much larger than the number of received samples. The performance of the proposed framework is analyzed empirically through extensive simulations, which verify that the proposed scheme approaches the maximum likelihood detector with perfect CSI. [ABSTRACT FROM AUTHOR]

With the advent of the Internet of things, massive machine-type communications (mMTC) have become one of the most important requirements for next generation communication systems. In the mMTC scenarios, grant-free nonorthogonal multiple access on the transmission side and compressive sensing-based multi-user detection (CS-MUD) on the reception side are a promising solution because many users sporadically transmit small data packets at low rates. In this paper, we propose a novel CS-MUD algorithm for the active user and data detection in the mMTC systems. The proposed scheme consists of the maximum a posteriori probability (MAP) based active user detector (MAP-AUD) and the MAP-based data detector (MAP-DD). By exchanging the extrinsic information between MAP-AUD and MAP-DD, the proposed algorithm improves the active user detection performance and the reliability of the data detection. In addition, we extend the proposed algorithm to exploit group sparsity. By jointly processing the multiple received data with common activity, the proposed algorithm dramatically enhances the active user detection performance. We show by numerical experiments that the proposed algorithm achieves a substantial performance gain over existing algorithms. [ABSTRACT FROM AUTHOR]

In the air–ground propagation channel, multipath propagation severely affects the performance of the communication, navigation, and surveillance systems used in civil aviation. To gain a deeper understanding of the air–ground channel, we are interested in the locations of objects causing multipath propagation. Therefore, in this paper we propose and experimentally validate an algorithm to localize reflectors causing multipath components based on channel sounding data collected using omnidirectional antennas. The algorithm relies on superresolution parameter estimation and tracking as well as on a localization algorithm. Using both simulations and channel sounding data collected from flight trials, we demonstrate the ability to reliably localize the reflectors causing multipath components. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are anticipated to be widely deployed in future wireless communications, due to their advantages of high mobility and easy deployment. However, the broadcast nature of air-to-ground line-of-sight wireless channels brings a new challenge to the information security of UAV-ground communication. This paper tackles such a challenge in the physical layer by exploiting the mobility of UAV via its trajectory design. We consider a UAV-ground communication system with multiple potential eavesdroppers on the ground, where the information on the locations of the eavesdroppers is imperfect. We formulate an optimization problem, which maximizes the average worst case secrecy rate of the system by jointly designing the robust trajectory and transmit power of the UAV over a given flight duration. The nonconvexity of the optimization problem and the imperfect location information of the eavesdroppers make the problem difficult to be solved optimally. We propose an iterative suboptimal algorithm to solve this problem efficiently by applying the block coordinate descent method, $\mathcal {S}$ -procedure, and successive convex optimization method. Simulation results show that the proposed algorithm can improve the average worst case secrecy rate significantly, as compared to two other benchmark algorithms without robust design. [ABSTRACT FROM AUTHOR]

This paper considers a heavy loaded full-duplex (FD) small cell consisting of one FD base station and multiple half-duplex users where the number of subchannels is less than the number of uplink and downlink users. With the objective of maximizing the total sum-rate, a joint power allocation, and user pairing problem is formulated. To solve this problem, a novel decomposition method is proposed, which decouples the power allocation and user pairing problem into two subproblems. It is proven that the optimal solution of subproblems is the optimal solution of the original problem. Then, optimal algorithms are developed for the two subproblems. Numerical results show that the proposed algorithm achieves better performance compared with the baselines of the greedy algorithm and random pairing algorithm. [ABSTRACT FROM AUTHOR]

The employment of social similarity (SS) aware device-to-device (D2D) user equipment (UE)-to-network (UE-NW) relay helps to improve system capacity and UEs’ accessability to the evolved Node B (eNB), where a crucial aspect is the design of the relay helper UE (HUE) selection process. However, few existing studies jointly consider the diverse impacts from various design criteria on the performance of HUE selection. In this paper, we formulate the SS-aware D2D UE-NW relay selection (RS) issue as a multiobjective binary integer linear programming (MOBILP) problem under the constraints of several criteria. Then, we propose a new two-stage D2D UE-NW selection scheme. Specifically, a joint algorithm exploiting the intuitionistic fuzzy analytic hierarchy process and the entropy weight generation method is designed for stage one, which provides both subjective and objective preferences for assisting decision making. In stage two, with the constructed decision weights, the MOBILP can be transformed to a simpler binary integer linear programming problem and thus be solved distributively through an efficient message passing method. Based on realistic social data traces, our simulation results validate the benefits of the proposed two-stage scheme, which ensures high system performances in terms of throughput, fairness and energy efficiency. [ABSTRACT FROM AUTHOR]

Ultradense heterogeneous networks (HetNets) are emerging as an inevitable approach to tackle the capacity crunch in cellular networks. However, imbalanced load among small and macrocells and poor resource utilization as a consequence in HetNets remains a long-standing problem. This paper addresses this problem by presenting a solution for maximization of coverage and capacity while minimizing load imbalance among macro and small cells. Most recent studies on the topic focus on either optimization of coverage, capacity or load, or a combination of two of these three intertwined objectives. We formulate the optimization problem as a function of two hard parameters namely antenna tilt and transmit power, and a soft parameter, cell individual offset, that affect the coverage, capacity, and load directly. The resulting solution is a combination of the otherwise conflicting coverage and capacity optimization (CCO) and load balancing (LB) self-organizing network (SON) functions. In the presented joint CCO-LB solution, a conflict free operation of CCO and LB is ensured by designing a novel load aware user association methodology and resolving the effects of shadowing on coverage probability using stochastic approximation. The problem is proven to be nonconvex and is solved using genetic algorithm, sequential quadratic programming, and pattern search algorithms. The proposed CCO-LB solution is compared against two recently proposed CCO and CCO-LB solutions in the literature. Results show that the proposed solution can yield significant gain in terms of throughput, spectral efficiency, and load distribution. [ABSTRACT FROM AUTHOR]

The hidden terminal problem degrades the uplink system throughput of wireless local area network seriously. In this paper, we propose the optimum allocation (OA) scheme of the location-based virtual sector (VS) method, which solves the hidden terminal problem without massive overheads like request-to-send and clear-to-send (RTS/CTS) packets. The VS method forms sectors virtually to make short-distance mobile terminal (MT) groups using MTs’ location information. In the group, all MTs can sense the transmission of all other MTs. In the proposed method, rough time slot control is implemented for intersector control, which does not need a highly functional centralized controller. The proposal OA scheme of the VS method improves the fairness among MTs by optimizing active periods of VS according to the number of MTs in each VS. By computer simulations, we show that the proposed method can increase the system throughput from the RTS/CTS and improve fairness among MTs even in the condition that positioning error is 1/10 of cell radius. [ABSTRACT FROM AUTHOR]

Traffic offloading via device-to-device communications is expected to play a major role to meet the exponential data traffic growth in wireless networks. In this paper, we focus on the problem of user capacity maximization in ultra-dense heterogeneous networks with device-to-device cooperation, where a large number of users in a given geographical area request common data content, such as video on demand streaming, with strict quality of service guarantees. We aim at finding the best strategy for delivering the content either over long range connectivity from the access points or short range connectivity from peer mobile terminals while meeting a target rate per served user. We formulate the multiuser resource management problem as an optimization problem including traffic offloading and channel allocation considering a very high user density reaching up to 1.75 users per square meter. We extend the system model to include mobile terminals acting as content owners by having the content cached locally, in order to further enhance the network capacity and coverage. We then propose an iterative resource management solution, which first solves the optimization subproblem of traffic offloading with orthogonal channels, and then optimally allocates channels to the transmitters with minimized interference due to channel reuse. We also propose efficient suboptimal hierarchical tree-based algorithms that operate in real time with dynamic and fast solutions for ultra-dense networks. For performance evaluation, we consider a realistic scenario consisting of a stadium topology with thousands of mobile terminals active simultaneously. We generate results as a function of a wide range of system parameters, and demonstrate that the proposed algorithms achieve near-optimal performance with notably low time complexity. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study of underwater visible light communications (UVLC), from channel characterization, performance analysis, and effective transmission and reception methods. To this end, we first simulate the fading-free impulse response (FFIR) of UVLC channels using Monte Carlo numerical procedure to take into account the absorption and scattering effects; and then to characterize turbulence effects, we multiply the aforementioned FFIR by a fading coefficient modeled as a lognormal random variable (RV) for weak oceanic turbulence. Based on this general channel model, we analytically study the bit error rate (BER) performance of UVLC systems with binary pulse position modulation (BPPM). In the next step, to mitigate turbulence effects, we employ multiple transmitters and/or receivers, i.e., we apply spatial diversity technique over UVLC links. Closed-form expressions for the system BER are provided, when an equal gain combiner is employed at the receiver side, thanks to the Gauss–Hermite quadrature formula as well as approximation to the sum of lognormal RVs. We further apply saddle-point approximation, an accurate photon-counting method, to evaluate the system BER in the presence of shot noise. Both laser-based collimated and light emitting diode based diffusive links are investigated. Additionally, in order to reduce the intersymbol interference (ISI), introduced by the multiple-scattering effect of UVLC channels on the propagating photons, we also obtain the optimal multiple-symbol detection (MSD) algorithm, as well as the suboptimal generalized MSD (GMSD) algorithm. Our numerical analysis indicate good matches between the analytical and photon-counting results implying the negligibility of signal-dependent shot noise, and also between the analytical results and numerical simulations confirming the accuracy of our derived closed-form expressions for the system BER. Besides, our results show that spatial diversity significantly mitigates fading impairments while (G)MSD considerably alleviates ISI deterioration. [ABSTRACT FROM AUTHOR]

This paper considers the transmission of scheduling information in orthogonal frequency-division multiple-access (OFDMA)-based cellular communication systems such as the Third-Generation Partnership Project (3GPP) long-term evolution (LTE). These systems provide efficient usage of radio resources by allowing users to be dynamically scheduled in both frequency and time. This requires considerable amounts of scheduling information to be sent to the users. This paper compares two basic transmission strategies: transmitting a separate scheduling message to each user versus broadcasting a joint scheduling message to all users. Different scheduling granularities are considered, as are different scheduling algorithms. The schemes are evaluated in the context of the LTE downlink using multiuser system simulations, assuming a full-buffer situation. The results show that separate transmission of the scheduling information requires a slightly lower overhead than joint broadcasting when proportional fair scheduling is employed and the users are spread out over the cell area. The results also indicate that the scheduling granularity standardized for LTE provides a good tradeoff between scheduling granularity and overhead. [ABSTRACT FROM PUBLISHER]

The opportunities and flexibility in relay networks and orthogonal frequency-division multiple access (OFDMA) make the combination a suitable candidate network and air-interface technology for providing reliable and ubiquitous high-data-rate coverage in next-generation cellular networks. Advanced and intelligent radio resource management (RRM) schemes are known to be crucial toward harnessing these opportunities in future OFDMA-based relay-enhanced cellular networks. However, it is not very clear how to address the new RRM challenges (such as enabling distributed algorithms, intra-cell/inter-cell routing, intense and dynamic co-channel interference (CCI), and feedback overhead) in such complex environments comprising a plethora of relay stations (RSs) of different functionalities and characteristics. Employment of conventional RRM schemes in such networks will highly be inefficient if not infeasible. The next-generation networks are required to meet the expectations of all wireless users, irrespective of their locations. High-data-rate connectivity, mobility, and reliability, among other features, are examples of these expectations. Therefore, fairness is a critical performance aspect that has to be taken into account in the design of prospective RRM schemes. This paper reviews some of the prominent challenges involved in migrating from the conventional cellular architecture to the relay-based type and discusses how intelligent RRM schemes can exploit the opportunities in relay-enhanced OFDMA-based cellular networks. We identify the role of multiantenna systems and explore the current approaches in literature to extend the conventional schedulers to next-generation relay networks. This paper also highlights the fairness aspect in such networks in the light of the recent literature, provides some example fairness metrics, and compares the performances of some representative algorithms. [ABSTRACT FROM AUTHOR]

The IEEE 802.16j standard has been developed to provide performance enhancement to the existing IEEE 802.16e network by incorporating the multihop relay (MR) technology. However, frequent handoffs and low spectrum-utilization issues that were not encountered in IEEE 802.16e may be incurred in IEEE 802.16j. The relay station (RS) grouping is one optional mechanism in the IEEE 802.16j MR standard to overcome these problems. The concept of RS grouping is to group neighboring RSs together to form an RS group, which can be regarded as a logical RS with larger coverage. In this paper, we investigate RS grouping performance enhancement in terms of throughput and handoff frequency. This paper designs an RS grouping algorithm to minimize handoffs by utilizing a greedy grouping policy: RS pairs with higher handoff rates will have higher priority for selection. The simulation results show that the handoff frequency of the considered MR network can significantly be reduced, and suitable RS grouping patterns can be derived using our grouping algorithm. In addition, we propose two centralized scheduling policies, i.e., the throughput-first (TF) policy to maximize the system throughput and the delay-first (DF) policy to minimize the average packet delay. By integrating our RS grouping algorithm and centralized scheduling algorithms, the simulation results indicate that, for the case of fixed users, groupings with smaller group sizes can result in better throughput performance. However, when user mobility is considered, the throughput value increases as the group size increases. Furthermore, we also show that the DF policy can both minimize the average packet delay and provide the fairness property among users with different traffic loads. [ABSTRACT FROM AUTHOR]

We study power-allocation problems in wireless systems through a network utility maximization framework. In this framework, the type of utility function represents the characteristics of each service. Hence, to deal with multiclass services in the system, it is important to accommodate various types of utility functions. In this paper, we consider four types of utility functions that can represent most of the services in wireless networks. We first develop a simple algorithm that solves a general optimization problem with those four types of utility function. We show that, even though the proposed algorithm might not provide the optimal solution, it provides an asymptotically optimal solution, which could be a good approximation to the optimal solution. The proposed algorithm can be applied to various resource-allocation problems in wireless systems. Among them, in this paper, we show that the proposed algorithm can be applied to both uplink and downlink power-allocation problems. [ABSTRACT FROM AUTHOR]

Contour line estimation in a monitored physical phenomenon using wireless sensor networks (WSNs) with cross-layer considerations is investigated in this paper. Herein, we present a statistical approach to locate contour points for contour line construction in the presence of irregular sensor deployment with the assistant of Delaunay triangular meshes. The proposed contour-estimation algorithm has certain error-correction capability, and it is robust to the data degradation coming from sensing and/or communication noise. To ensure the success of the proposed algorithm, we further analyze three sources of signal distortion: sensing noise, data quantization error, and data communication noise. We find that choosing a proper data quantization level can balance the communication cost and the desired signal quality. Meanwhile, the impact of channel impairment can be mitigated by the appropriate data-fusion mechanism. In this paper, an adaptive data-fusion mechanism is proposed to avoid excessive packet retransmissions. The correctness of the proposed approach is verified through simulation, and the effects of different system parameters on the overall system performance are also given to show the robustness of the proposed algorithm. This paper also offers some guidelines on the design of the WSN system. [ABSTRACT FROM AUTHOR]

Maximum-likelihood (ML) detection for higher order multiple-input-multiple-output (MIMO) systems faces a major challenge in computational complexity. This limits the practicality of these systems from an implementation point of view, particularly for mobile battery-operated devices. In this paper, we propose a modified approach for MIMO detection, which takes advantage of the quadratic-amplitude modulation (QAM) constellation structure to accelerate the detection procedure. This approach achieves low-power operation by extending the minimum number of paths and reducing the number of required computations for each path extension, which results in an order-of-magnitude reduction in computations in comparison with existing algorithms. This paper also describes the very-large-scale integration (VLSI) design of the low-power path metric computation unit. The approach is applied to a 4 x 4, 64-QAM MIMO detector system. Results show negligible performance degradation compared with conventional algorithms while reducing the complexity by more than 50%. [ABSTRACT FROM AUTHOR]

Orthogonal frequency-division multiplexing (OFDM) can be used to support high-data-rate transmissions over time-dispersive fading channels. Many OFDM-based communication systems use packet-based transmission, where channel estimation is needed for the detection of information-carrying symbols. The finite impulse response (FIR) channel model is simple and effective for some simulations of channel estimation for the OFDM-based communication systems over the time-dispersive channels; yet, it is only an approximate channel model that cannot be used in the case of accurate channel estimation. Unfortunately, many researchers have overlooked this issue and have been devising channel-estimation algorithms squarely based on the FIR channel model. While the channel estimation results from these algorithms can be impressive for the FIR channels, the algorithms can hardly be applied in real-world applications. This paper explains in detail the reason the FIR channel model is only an approximate channel model for the OFDM-based communication systems, trying to discourage the inappropriate usage of this model, which can lead to fruitless efforts. This paper also presents a realistic channel model for OFDM-based communication systems, which can be used to realistically access the channel parameter-estimation algorithms. [ABSTRACT FROM AUTHOR]

This paper introduces a novel analytical model for analyzing the performance of handoff algorithms based on absolute and relative signal strength measurements. The performance is analyzed in multicell and multisector environments where cochannel interference (CCI) is present. The model presented in this paper accounts for the distinct features of intersector handoff compared with intercell handoff. Numerical results show that the performance metrics are affected by the CCI level. The occurrence condition of unnecessary handoffs in multisector systems is also presented. [ABSTRACT FROM AUTHOR]

This paper considers a scheduling problem for multi-user transmissions over the downlink of a time-slotted cellular data network. For such a network, opportunistic scheduling exploits the time-varying radio channel and improves network performance. This paper presents a new scheduling solution that maximizes the aggregate user performance subject to certain minimum and maximum performance constraints. By constraining the performance experienced by individual users, who share a common radio downlink, to some upper bounds, it is possible to provide the system operator with a better control of radio resource allocations and service differentiation among different classes of users. The proposed solution, which is referred to as Throughput Constrained Opportunistic Scheduling (TCOS), offers better service differentiation among different classes of users than the existing opportunistic scheduling algorithms with only minimum performance constraints. For ideal memoryless fading channels, TCOS provides performance comparable with an existing scheme with minimum and maximum performance constraints. For realistic fading channels with memory, TCOS significantly improves system performance due to its ability to trade off feasibility with throughput. Extensive simulation results demonstrate the improved system performance and effective service differentiation of the proposed solution under realistic channel conditions. [ABSTRACT FROM AUTHOR]

This paper presents an analysis framework for estimating spatio-temporal distortion associated with data-gathering algorithms in sensor networks. We assume that the underlying data field being sensed is spatially and temporally correlated and that the sink node performs real-time reconstruction of the field. Unlike existing work, the proposed analysis framework assumes realistic transmission schemes and network/routing parameters, such as congestion and limited transmission range. Our simulations show that, even for a simple collision-free transmission scheme and regular deployment patterns, the distortion analyses yield starkly different results, compared with the analyses performed in the absence of these assumptions. The analytical method presented in this paper could easily be extended to consider the real-time data-reconstruction problem under different transmission schemes, node distributions, and reconstruction methods. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a novel approach for computing the number, location, and transmission powers of wireless base stations in a 2-D urban setup. The new approach utilizes 2-D convolution to extract the supply-demand correlation. Available efficient methods for computing the convolution are then used to substantially reduce the complexity of the solution. The proposed approach enables network designers to choose arbitrary antenna propagation and radio demand patterns using a simple color-coding mechanism. Simulations of the proposed algorithm show its efficiency and flexibility in solving the placement problem. In this paper, we consider only the coverage planning. Consequently, the technique in this paper is useful in networks that operate with time-division multiple-access technology. However, the work can be extended to consider simultaneous coverage and frequency planning. [ABSTRACT FROM AUTHOR]