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Power control is becoming increasingly essential for the fifth-generation (5G) and beyond systems. An example use-case, among others, is the unmanned-aerial-vehicle (UAV) communications where the nearly line-of-sight (LoS) radio channels may result in very low signal-to-interference-plus-noise ratios (SINRs). The authors in (Chiang et al., 2007) proposed to efficiently and reliably solve this kind of non-convex problem via a series of geometrical programmings (GPs) using condensation approximation. However, it is only applicable for a small-scale network with several communication pairs and practically infeasible with more (e.g., tens of) nodes to be jointly optimized. We therefore in this paper aim to provide new insights into this problem. By properly introducing auxiliary variables, the problem is transformed to an equivalent form which is simpler and more intuitive for condensation. A novel condensation method with linear complexity is also proposed based on the form. The enhancements make the GP-based power control feasible for both small- and especially large-scale networks that are common in 5G and beyond. The algorithm is verified via simulations. A preliminary case study of uplink UAV communications also shows the potential of the algorithm. [ABSTRACT FROM AUTHOR]

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]

This paper studies how to apply game theory to realize a well-function device-to-device enabled vehicular (D2D-V) communication system, where the uplink channel allocated to the cellular user (CU) is reused by multiple D2D-V users. Considering a non-cooperative setting where the CU and D2D-V users are selfish and profit-driven, a novel Stackelberg game framework is proposed to model the single-leader-multiple-follower hierarchical competition, where the CU and D2D-V users act as the leader and the followers, respectively. For the current D2D-V networks, the interference in the dense vehicle scene often leads to extremely poor communication quality. Moreover, a vehicle's mobility leads to an uncertain channel state and further affects the stability of communication. To achieve effective communication, robust Stackelberg game-based resource allocation is developed, and a price-penalty mechanism is further proposed. Unlike previous Stackelberg games, the robust game is highlighted by handling the channel uncertainty which is embedded in the interference probability constraints. Besides, a game equilibrium (GE) is considered to be the solution and its existence and uniqueness are investigated. Also, a distributed robust power control and nonuniform price bargaining algorithm is proposed to approach the GE. Numerical simulations are performed to evaluate the algorithm performances, and the results indicate that the proposed algorithm is effective in high mobility vehicular networks under uncertain channel environments. [ABSTRACT FROM AUTHOR]

The orbital angular momentum (OAM)-based communication systems may face severe transmission problems when the transmit and receive uniform circular array pairs are blocked. In this paper, a promising technique named intelligent reflecting surface (IRS) is proposed to help alleviate blockages and provide alternative line-of-sight links. To maximize the achievable rate of the IRS-assisted OAM communication systems, we optimize its transmit power allocation along with the IRS's reflecting phase shifts, and propose an alternative optimization-based algorithm to solve the resulting optimization problem with coupled variables and non-convex structure. Specifically, the proposed algorithm obtains a closed-form solution to the transmit power allocation by applying the majorization-minimization and ℓ1-ball projection approaches, and obtain the locally optimal solution to the IRS's reflecting phase shifts by applying the weighted minimum mean square error-based fixed point iteration approach. Simulation results demonstrate the superiority of our proposed algorithm over existing baseline algorithms and also show its robust stability to the oblique angle errors. [ABSTRACT FROM AUTHOR]

Multi-hop device-to-device (D2D) communications play an important role in expanding D2D coverage. In this paper, we study a relay-based and network-coding-assisted (in particular, XOR coding) multi-hop D2D communication system. In the system, toward jointly considering the impact of interference and network traffic conditions on the quality of D2D communications, various channel fading models and traffic models are investigated, and the packet loss probability of D2D links is meticulously computed using these models. With the packet loss probability of D2D links, the general closed-form expressions of end-to-end packet loss probability (E2EPLP) of the system with the presence (or absence) of XOR coding are subsequently derived. Our experiments reveal that both the number of relays and the mechanism of XOR coding can affect the system performance. Specifically, the increase in the number of relays will lower the overall system performance (e.g., an increase in the E2EPLP and end-to-end completion time, and a decrease of the end-to-end rate may follow as a result). On the other hand, although the presence of XOR coding unfortunately raises the system E2EPLP, it can effectively improve the end-to-end completion time and end-to-end rate. It is our belief that the analytical approach proposed in this paper and the results found in our work can be useful to peer studies in the context of applying network coding in multi-hop D2D networks. [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]

In the era of the fifth generation (5G) communication networks, massive multiple-input multiple-output (MIMO) systems demand even lower computation complexity and power consumption while catching up with good detection performance. In this paper, a low-complexity nonlinear detection algorithm is proposed for massive MIMO systems, which is based on partial tree search and successive interference cancellation (SIC). The proposed scheme allows us to expedite the detection process by coping with the transmit symbols group by group. As compared to vertical-Bell laboratories layered space time (V-BLAST), the major breakthrough of computation reduction lies in the fact that the partial tree search can assist the detection process to avoid the inversion of the detection matrix required in each recursion of the SIC process. Both computational complexity analysis and simulation results show that our proposed algorithm not only significantly reduces computational complexity, but also has better bit error rate (BER) performance. [ABSTRACT FROM AUTHOR]

Multibeam satellite communication plays an important role in future integrated satellite-terrestrial communication systems. However, the large area covered by the satellite beams makes itself vulnerable to be wiretapped, especially for the cases that eavesdroppers are located near the legitimate receivers. In this paper, cooperative jamming from both satellite and full-duplex receivers are investigated where we aim to minimize the power consumed by a multibeam satellite system with secrecy outage probability constraints. To tackle this problem, an algorithm of joint beamforming and power allocation via alternating optimization (JBFPA-AO) is proposed. Specifically, a feasible solution is first obtained by transforming the original non-convex problem into a semi-definitive programming (SDP) problem with relaxed constraints. And then, alternating optimization is adopted to find better solutions iteratively. Convergence of the proposed JBFPA-AO is proved, and simulation results show its effectiveness to reduce the power utilized by the satellite. [ABSTRACT FROM AUTHOR]

The approach of factor-graphs (FGs) is applied in the context of power control and user pairing in Device-to-Device (D2D) communications as an effective underlay concept in wireless cellular networks. D2D communications can increase the spectral efficiency of wireless cellular networks by establishing a direct link between devices with limited help from the evolved node base stations (eNBs). A well-designed user pairing and power allocation scheme with low complexity can remarkably improve the system's performance. In this paper, a simple and distributed FG based approach is utilized for power control and user pairing implementation in an underlay cellular network with D2D communications. A max-min criterion is proposed to maximize the minimum rate of all active users in the network, including the cellular and multiple D2D co-channel links in the uplink direction. An associated message-passing (MP) algorithm is presented to distributedly solve the resultant NP-hard maximization problem, with a guaranteed convergence compared to game theoretic and Q-learning based methods. The complexity and convergence of the proposed method is analyzed and numerical results confirm that the proposed scheme outperforms alternative algorithms in terms of complexity, while keeping the sum-rate of users nearly the same as centralized counterpart methods. [ABSTRACT FROM AUTHOR]

Sparse Code Multiple Access (SCMA) has been proposed for the newest generation of communication systems, considering overloaded non-orthogonal multiple access. To deal with the overload, SCMA exploits the sparsity feature with the aid of receivers based on the messsage-passing algorithm (MPA). In this paper, we review a low-complexity MPA receiver based on edge selection and gaussian approximation (ESGA). We improve this receiver by proposing a threshold-based criterion for the edge selection (TB-ESGA). With the threshold, the number of interfering neighbors considered in the calculation of each message of the MPA becomes a random variable. We derive the probability of selecting a given number of edges in our strategy and also an expression for the average complexity order, as a function of the threshold. We also show that the proposed threshold results in a better trade-off between complexity and error-rate performance. [ABSTRACT FROM AUTHOR]

In this paper, we focus on a UAV-assisted cooperative communication system based on simultaneous wireless information and power transfer (SWIPT), where the UAV serves as a relay and its transmission capability is partly powered by radio signal from the source via the time-sharing mechanism. We study the end-to-end cooperative throughput maximization problem by optimizing the UAV's decision profile, power profile and trajectory for both amplify-and-forward (AF) and decode-and-forward (DF) protocols. The problem is decomposed into three optimization subproblems for decision profile, power profile and trajectory, and solved through alternating optimization, by which each of the subproblems is solved with the other two fixed. A binarization algorithm is further proposed to make the decision profile feasible. We show that the proposed solution outperforms not only two SWIPT-based strategies, but also a similar solution from an existing work without consideration for SWIPT. In addition, results indicate that the proposed algorithm performs efficiently in both optimality and convergence. [ABSTRACT FROM AUTHOR]

In this paper, we propose the design of a uniquely factorable hexagonal constellation for a noncoherent wireless communication system with a single transmitter antenna and multiple receiver antennas [single-input multiple-output (SIMO)]. The hexagonal constellation has a densely packed 2-D “honeycomb” structure and is more energy efficient than the commonly used cross quadrature amplitude modulation (QAM) constellations. By using the hexagonal lattice formed from the Eisenstein integers and the recently developed concept of the uniquely factorable constellation (UFC) for the QAM constellation, an algorithm is developed to effectively and efficiently construct constant norm hexagonal UFCs of various sizes. In addition, an optimal energy scale is found to maximize the coding gain for the constant norm training hexagonal UFC schemes subject to a transmission bit rate constraint. Computer simulations show that the hexagonal unitary UFC proposed in this paper has the best error performance in comparison to the current literature results for the noncoherent SIMO system. [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]

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]

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]

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]

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]

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]

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 aims at solving two classes of energy efficiency (EE) maximization problems in multiple channels wireless communication systems. First, the EE maximization problem with sum power constraint is solved based on the geometric water-filling approach; and second, the approach is extended into the EE maximization problem with additional least throughput requirement constraint. Our proposed algorithms make use of the water-filling structure of the optimal solution and provide exact and computation efficient solution to the energy-efficient power allocation problems. The proposed algorithms also have excellent scalability, which is applicable for large-scale wireless communication systems. Optimality of the proposed algorithms is strictly proved, and the proposed algorithms only require low-degree polynomial computational complexity. Numerical results are presented to demonstrate the efficiency of the proposed algorithms. To the best of our knowledge, no prior algorithms in the existing literature could provide such solutions to the EE maximization problems under the merit of exactness and the efficiency. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the cross-layer resource optimization for wireless relay networks with dynamic node-selfishness information. The degree of intrinsic selfishness (DeIS) and the degree of extrinsic selfishness (DeES) of the relay node (RN) are defined as the effects of its own residual energy resource and the incentive in an incentive mechanism, respectively. A model of two-timescale node-selfishness dynamics is constructed to describe the time-varying characteristics of the RN's DeIS and DeES. Based on the two-timescale node-selfishness dynamics of the RNs, a two-timescale resource-optimization dynamical algorithm (TRODA) is employed to control the transmit powers of both RNs and end users (EU), as well as the flow rates of the data packets injected by all EUs. Meanwhile, the stability and the tracking error of the TRODA under the two-timescale node-selfishness dynamics are analyzed by Lyapunov theory. Our simulation results demonstrate that the two-timescale optimization algorithm is stable and has trivial tracking error. [ABSTRACT FROM AUTHOR]

The ability of mobile devices to be connected to more than one radio node at the same time enables mobile devices to transmit and receive traffic to and from multiple paths. This ability helps to increase the average mobile device data rate and to improve the network reliability. Load balancing among multiple paths become a key factor to avoid network congestion, nevertheless it requires efficient techniques to split traffic without adding more delay or generating too much packet reordering for delay-sensitive traffic. In this paper, we address two key issues in the context of uplink wireless mobile networks: 1) how to accurately split traffic among multiple paths and 2) how to minimize the end-to-end delay without increasing packet reordering. We propose delay-aware load balancing algorithm (DALBA), a novel strategy that splits traffic at the granularity of the packet. DALBA aims to minimize the splitting error (SE) and the end-to-end delay difference by effectively using all of the available paths. We analyze DALBA's performance through extensive simulations using H.264 video traffic. Numerical results demonstrate that DALBA outperforms previous algorithms in terms of SE, end-to-end delay and peak signal-to-noise ratio while keeping packet reordering to a suitable low value. [ABSTRACT FROM AUTHOR]

Scheduling algorithm design is a critical and challenging issue in multiuser wireless networks with dynamic flows. The well-known Queue-length-based MaxWeight (QMW) scheduling algorithm can achieve throughput-optimality if there only exist persistent flows that are long-lived with infinite traffic arrival. In this paper, we propose a head-of-line access delay (HAD)-based scheduling algorithm and show that it is throughput-optimal when the flows are dynamic, i.e., they are short-lived with finite data to transmit. HAD is an online algorithm and does not require prior knowledge of the statistics of the arrival traffic and channel information. We also develop the Markov analytic model to study system performance and reveal important properties of the proposed HAD scheduling algorithm. To reduce the complexity of the analysis, we further study two approximation methods corresponding to different arrival traffic intensity. Performance evaluation shows that the HAD scheduling algorithm can outperform the classic QMW and stabilize the system at the presence of flow-level dynamics. Compared to the other existing algorithms, HAD is practical to implement with a better delay performance. [ABSTRACT FROM AUTHOR]

Very recently, regulatory bodies worldwide have approved dynamic access of unlicensed networks to the TV white space (TVWS) spectrum. Hence, in the near future, multiple heterogeneous and independently operated unlicensed networks will coexist within the same geographical area over shared TVWS. Although heterogeneity and coexistence are not unique to TVWS scenarios, their distinctive characteristics pose new and challenging issues. In this paper, the problem of the coexistence interference among multiple heterogeneous and independently operated secondary networks (SNs) in the absence of secondary cooperation is addressed. Specifically, the optimal coexistence strategy, which adaptively and autonomously selects the channel maximizing the expected throughput in the presence of coexistence interference, is designed. More in detail, at first, an analytical framework is developed to model the channel selection process for an arbitrary SN as a decision process. Then, the problem of the optimal channel selection, i.e., the channel maximizing the expected throughput, is proved to be computationally prohibitive (NP-hard). Finally, under the reasonable assumption of identically distributed interference on the available channels, the optimal channel selection problem is proved not to be NP-hard, and a computationally efficient (polynomial-time) algorithm for finding the optimal strategy is designed. Numerical simulations validate the theoretical analysis. [ABSTRACT FROM PUBLISHER]

In this paper, we focus on adaptive time-variant channel estimation for vehicle-to-vehicle (V2V) communications in intelligent transportation systems (ITS) using the IEEE 802.11p physical layer. The IEEE 802.11p pilot pattern is identical to that in the well-known IEEE 802.11a/g (WiFi) standard, which was initially designed for indoor environments with little or no mobility. However, in a V2V drive-by situation, the channel impulse response rapidly changes due to the high relative velocity between transmitter and receiver, as well as the changes in the scattering environment. Hence, for such V2V channels, advanced decision directed channel estimation methods are needed to reach a frame error rate (FER) smaller than \10^-1. Even more importantly, the channels are nonstationary, which implies that the Doppler power spectral density (DSD) and the power delay profile (PDP) change on a timescale comparable with the frame length, which complicates the estimator design. In this paper, we develop an adaptation method for the channel estimation filter that is suitable for the following: 1) the short frame length in IEEE 802.11p; 2) the given pilot structure; and 3) the requirement of only a single received short frame for filter adaptation. We define a set of hypotheses on the support of the DSD and a second set of hypotheses on the support of the PDP. Each hypothesis is represented by a specific subspace spanned by orthogonal basis vectors. For basis vector calculation, we develop a numerically stable algorithm utilizing generalized discrete prolate spheroidal sequences. The adaptation algorithm chooses a hypothesis from both sets such that a probabilistic bound on the channel estimation error is minimized. We implement the hypothesis test by means of a novel subspace selection algorithm that allows utilizing correlated observations of a time- and frequency-selective (2-D) fading process. We validate the adaptive channel estimation scheme in an IEEE 802.11p compliant link level simulation for a relative velocity range from 0 to 111 m/s \approx 400 km/h \approx 249 mi/h. Adaptive filtering enables an up to fourfold reduction in the number of required iterations to reach an FER below \10^-1 for an Eb/N0 = \12 \ \dB. [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]

This paper tackles a power minimization problem of interleave-division multiple-access (IDMA) systems over a fading multiple-access channel. The problem is minimizing the total power received by the receiver while keeping the bit error rates (BERs) of all users below a predefined value. The original formulation of the problem has highly nonlinear and implicitly defined functions, which render most existing optimization methods incapable. A new formulation is proposed in this paper, whose solution can effectively be obtained by a branch-and-bound (B&B) technique. An algorithm is devised based on B&B, and its effectiveness is also demonstrated by numerical experiments of systems with a moderate numbers of users. [ABSTRACT FROM AUTHOR]

This paper presents a method of determining electromotive force and battery internal resistance as time functions, which are depicted as functions of state of charge (SOC) because Soc = f(t). The model is based on battery discharge and charge characteristics under different constant currents that are tested by a laboratory experiment. This paper further presents the method of determining the battery soc according to a battery modeling result. The influence of temperature on battery performance is analyzed according to laboratory-tested data, and the theoretical background for calculating the SOC is obtained. The algorithm of battery SOC indication is depicted in detail. The algorithm of the battery SOC "online" indication considering the influence of temperature can be easily used in practice by a microprocessor. An NiMH battery is used in this paper to depict the modeling method. In fact, the method can also be used for different types of contemporary batteries, as well as Li-ion batteries, if the required test data are available. [ABSTRACT FROM AUTHOR]

This paper introduces a space-time decision feedback equalizer whose coefficients are recursively renewed on the basis of the modified affine projection algorithm (APA). Despite its effectiveness against a rank-deficiency problem, the complexity of the conventional APA utilizing the sample matrix inversion is considerable. Furthermore, the shift invariance property could not be exploited in the case of a space-time decision-directed equalizer (STDE) application. To overcome such challenges, this paper introduces an APA employing a hyperplane projection scheme to reduce computational complexity without any degradation in its convergence characteristic and tracking capability. The performance of the proposed equalization methodology is verified by showing its superior bit error rate behaviors, fast convergence property, and reduced computational complexity by performing adaptive STDE under the single-input-multiple-output wireless multipath fading channel. [ABSTRACT FROM AUTHOR]

In wireless location, nonline-of-sight (NLOS) errors are a major error source. To improve positional performance, these errors should be identified and mitigated before measurements are used in position computations. A novel NLOS mitigation algorithm is proposed in this paper. Employing system redundancy, the algorithm derives from the distribution function of hyperbolic intersections, which is an intermediate mobile location. Cost functions based on the intermediate position are then created for each time-of-arrival measurement. NLOS errors are detected and mitigated via hypothesis tests performed on the cost functions. Simulation tests are presented in this paper, and the results verify the effectiveness of this NLOS-error-mitigation method. This paper also demonstrates that algorithm performance depends closely on receiver noise. Reducing receiver noise or providing the intermediate mobile location via external aiding can improve performance. [ABSTRACT FROM AUTHOR]

Reverse link capacity of a direct-sequence code-division multiple-access (DS-CDMA) system in a multicell environment has been studied recently, and significant capacity improvements due to the use of beamforming have been observed. However, system performance with beamforming will be affected by several impairments, such as direction of arrival estimation errors, array perturbations, mutual coupling, and signal spatial spreads. In this paper, reverse link performance of CDMA systems with beamforming under these impairments (imperfect beamforming) is investigated. A simplified beamforming model is developed to evaluate the system performance in terms of user capacity, bit-error rates (BER), and outage probabilities. Both signal-to-interference-ratio-based power control and strength-based power control are considered in this paper. The capacity and BER degradations due to different impairments are shown, and outage probabilities under different power control schemes are examined. [ABSTRACT FROM AUTHOR]

This paper investigates the features of a cellular geometry in code-division multiple-access (CDMA) systems with soft handoff and distinguishes controlling area of a cell from coverage area of a cell. Some important characteristics of the cellular configuration in soft handoff systems are used to propose a new design of efficient call admission control (CAC) in CDMA systems. Then, the paper constructs a continuous-time Markov chain (CTMC) model for CAC in CDMA with a soft handoff queue, obtains closed-form solutions, and thus develops loss formulas as performance indices such as the new blocking probability and the handoff dropping probability. In order to determine handoff traffic arrival rate, a fixed-point strategy is developed. Algorithms are also provided to stably compute loss probabilities and to determine the optimal number of guard channels. A new soft handoff scheme—eliminating pseudo handoff calls (EPHC)—is proposed to improve channel utilization efficiency based on mobility information. As an application of the loss formulas, the proposed modeling techniques are used to evaluate and compare the performance of conventional and proposed EPHC soft handoff schemes. Numerical results show the effectiveness of the proposed Markov chain models and the benefits of the new soft handoff scheme. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of power control for sum-rate maximization on multiple interfering links [transmitter–receiver pairs (Tx–RX)] under a sum power constraint. We consider a single-frequency network, where all pairs are operating in the same frequency band, thereby creating interference with each other. We study the power-allocation problem for sum-rate maximization with and without quality-of-service (QoS) requirements on individual links. When the objective is only sum-rate maximization without QoS guarantees, we develop an analytic solution to decide optimal power allocation for a two-TX–RX-pair problem. We also develop a low-complexity iterative algorithm for a three-TX–RX-pair problem. For a generic N>\3 TX–RX pair problem, we develop two low-complexity suboptimal power-allocation algorithms. The first algorithm is based on the idea of making clusters of two or three TX–RX pairs and then leveraging the power-allocation results obtained for the two- and three-TX–RX-pair problems. The second algorithm is developed by using a high signal-to-interference-plus-noise ratio (SINR) approximation, and this algorithm can be also implemented in a distributed manner by individual TXs. We then consider the same problem but with additional QoS guarantees for individual links. We again develop an analytic solution for the two-TX–RX-pair problem and a distributed algorithm for N>\2 TX–RX pairs. [ABSTRACT FROM PUBLISHER]

Due to signal fading and user mobility in wireless networks, quality-of-service provisioning for wireless services has become more challenging. As a promising technique, cooperative communications make use of the broadcasting nature of the wireless medium to facilitate data transmission and, thereby, reduce energy consumption. However, in many studies on wireless cooperative diversity, it is often assumed that the number of relays or the relay distribution intensity is known a priori. In this paper, we relax this assumption and propose an algorithm to estimate the relay intensity for a backoff-based cooperative scheme, where the relays are distributed as a homogeneous Poisson point process. It is proved that the algorithm can converge to an optimal solution with the minimum estimation error. Based on the estimated relay intensity, we further investigate a distributed energy saving strategy, which selectively turns off some relays to reduce energy consumption while maintaining the required transmission success probability. The performance of the proposed cooperative scheme is analytically evaluated with respect to the collision probability. The numerical and simulation results demonstrate the high accuracy and efficiency of the intensity estimation algorithm and validate the theoretical analysis. Moreover, the proposed cooperative scheme exhibits significant energy savings and satisfactory transmission performance, which offers a good match to accommodate green communications in wireless networks. [ABSTRACT FROM PUBLISHER]

This paper investigates the uplink transmission in multicell multiuser multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems. The system model considers imperfect channel estimation, pilot contamination (PC), and multicarrier and multipath channels. Analytical expressions are first presented on the mean square error (MSE) of two classical channel estimation algorithms [i.e., least squares (LS) and minimum mean square error (MMSE)] in the presence of PC. Then, a simple H-infinity (H-inf) channel estimation approach is proposed to have good suppression to PC. This approach exploits the space-alternating generalized expectation–maximization (SAGE) iterative process to decompose the multicell multiuser MIMO (MU-MIMO) problem into a series of single-cell single-user single-input single-output (SISO) problems, which reduces the complexity significantly. According to the analytic results given herein, increasing the number of pilot subcarriers cannot mitigate PC, and a clue for suppressing PC is obtained. It is shown from the results that the H-inf has better suppression capability to PC than classical estimation algorithms. Its performance is close to that of the optimal MMSE as the length of channel impulse response (CIR) is increased. By using the SAGE process, the performance of the H-inf does not degrade when the number of antennas is large at the base station (BS). [ABSTRACT FROM PUBLISHER]

In cooperative communication networks, owing to the nodes' arbitrary geographical locations and individual oscillators, the system is fundamentally asynchronous. Such a timing mismatch may cause rank deficiency of the conventional space–time codes and, thus, performance degradation. One efficient way to overcome such an issue is the delay-tolerant space–time codes (DT-STCs). The existing DT-STCs are designed assuming that the transmitter has no knowledge about the channels. In this paper, we show how the performance of DT-STCs can be improved by utilizing some feedback information. A general framework for designing DT-STC with limited feedback is first proposed, allowing for flexible system parameters such as the number of transmit/receive antennas, modulated symbols, and the length of codewords. Then, a new design method is proposed by combining Lloyd's algorithm and the stochastic gradient-descent algorithm to obtain optimal codebook of STCs, particularly for systems with linear minimum-mean-square-error receiver. Finally, simulation results confirm the performance of the newly designed DT-STCs with limited feedback. [ABSTRACT FROM PUBLISHER]

In this paper, a novel low-complexity tree-search (TS) algorithm, which is referred to as a hierarchical tree search (HTS), is proposed for soft-output multiple-input–multiple-output (MIMO) detection to mitigate the performance loss caused by an early termination. The proposed HTS algorithm is realized by the following two components: the hierarchical set partitioning to find all hypotheses with reasonable quality as fast as possible and to fairly improve them and the new cost metric to determine the search order by considering the quality of the hypotheses found so far. Through simulation, it is shown that the performance–complexity tradeoff of the proposed HTS algorithm surpasses those of the existing algorithms in various MIMO configurations. [ABSTRACT FROM PUBLISHER]

In wireless sensor-actor networks, sensors probe their surroundings and forward their data to actor nodes. Actors collaboratively respond to achieve predefined application mission. Since actors have to coordinate their operation, it is necessary to maintain a strongly connected network topology at all times. Moreover, the length of the inter-actor communication paths may be constrained to meet latency requirements. However, a failure of an actor may cause the network to partition into disjoint blocks and would, thus, violate such a connectivity goal. One of the effective recovery methodologies is to autonomously reposition a subset of the actor nodes to restore connectivity. Contemporary recovery schemes either impose high node relocation overhead or extend some of the inter-actor data paths. This paper overcomes these shortcomings and presents a Least-Disruptive topology Repair (LeDiR) algorithm. LeDiR relies on the local view of a node about the network to devise a recovery plan that relocates the least number of nodes and ensures that no path between any pair of nodes is extended. LeDiR is a localized and distributed algorithm that leverages existing route discovery activities in the network and imposes no additional prefailure communication overhead. The performance of LeDiR is analyzed mathematically and validated via extensive simulation experiments. [ABSTRACT FROM PUBLISHER]

Femto base stations (FBSs) are cost-effective choices for wireless service providers (WSPs) to provide better indoor coverage and capacity in cellular networks. With the introduction of femtocells, spectrum allocation among macro base stations (MBSs) and numerous FBSs becomes challenging. WSPs will allocate resources among hierarchical infrastructures to mitigate mutual interference and maximize WSPs' revenue from service provisioning. In this paper, we propose a channel allocation and service provision framework to help WSPs allocate resources and increase revenue. The spectrum allocation and revenue maximization problem is formulated as a convex problem, and a centralized method is proposed to efficiently solve it. We also propose a distributed algorithm called LD (based on Lagrangian decomposition) to achieve a near-optimal solution with low computational and communication complexity, which can be used when the system is on a large scale. The simulation results show that our scheme can significantly improve the system's revenue. [ABSTRACT FROM PUBLISHER]

Cooperative communication (CC) offers an efficient and low-cost way to achieve spatial diversity by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of CC. Most existing relay selection strategies focus on optimizing the outage probability or energy consumption. To fill in the vacancy of research on throughput improvement via CC, we study the relay selection problem with the objective of optimizing the throughput in this paper. For unicast, it is a P problem, and an optimal relay selection algorithm is provided with a correctness proof. For broadcast, we show the challenge of relay selection by proving it nonprobabilistic hard (NP-hard). A greedy heuristic algorithm is proposed to effectively choose a set of relay nodes that maximize the broadcast throughput. Simulation results show that the proposed algorithms can achieve high throughput under various network settings. [ABSTRACT FROM PUBLISHER]

In this paper, we examine the queuing performance of terminals in a multichannel centralized S-ALOHA system with a finite terminal population and finite queue size in each terminal, employing a uniform backoff (UB) algorithm with retry limit for collision resolution. The performance evaluations focus on uplink traffic from web browsing, which is modeled as a Markov-modulated Bernoulli process with correlated arrivals. We analyze the system performance in terms of system throughput, mean queue length, mean delay, the probability that a packet is dropped by retry limit, the probability that a packet is blocked by a full queue, and the mean and variance of packet retransmission time, in relation to the source correlation, number of channels, window size, and retry limit. For comparison, we also consider bufferless terminals with correlated arrivals. In addition, we evaluate by simulations the performance of terminals with finite queue size when they have perfect knowledge of the backlog size. Results from our study allow the parameters of the UB algorithm to properly be chosen to meet the access-level quality-of-service requirements. [ABSTRACT FROM PUBLISHER]

Subcarrier allocation, power allocation, and partner selection (PS) algorithms are examined for amplify-and-forward (AF) cooperative multicarrier systems. Consider a network that consists of multiple cooperative pairs that employ a two-phase cooperation scheme, where the users first transmit their own messages on each of their subcarriers in phase I and either retransmit their own messages or relay the messages of their partners in phase II. In general, subcarrier allocation and partner selection problems are known to be intractable for systems with large number of users and subcarriers. In this paper, we propose a suboptimal but mathematically tractable approach to address these issues and show its effectiveness compared with existing algorithms. This approach is done by considering the power allocation over a relaxed problem formulation, where each subcarrier is allowed to retransmit or relay for all other subcarriers in phase II over virtually orthogonal channels. The power allocation in this case is a convex optimization problem and, thus, can efficiently be solved. Based on the results of the relaxed power allocation problem, an efficient subcarrier allocation algorithm for the original problem can then be derived by allowing subcarriers to forward the data that are allocated the most power. The results also yield a necessary and sufficient condition on when cooperation is helpful among two users. A partner selection algorithm is then devised based on the criterion given in the condition. With the proposed subcarrier allocation scheme, an iterative procedure is then adopted for resource allocation in the two phases. The complexity of the proposed algorithm is discussed, and its efficacy is demonstrated through numerical simulations. [ABSTRACT FROM PUBLISHER]

This paper proposes the full interference cancellation (FIC) algorithm to cancel the interrelay interference (IRI) in the two-path cooperative system. Arising from simultaneous data transmission from the source and relay nodes, IRI may significantly decrease performance if it is not carefully handled. Compared with the existing partial interference cancellation scheme, the FIC approach is more robust yet is not as complex. Numerical results are also given to verify the proposed scheme. [ABSTRACT FROM PUBLISHER]

Recent studies have shown that roadside wireless local area network (WLAN) access points (APs) can provide network access from vehicles. However, the unplanned deployment of WLAN APs is the main hurdle in the provision of continuous network services. Because of this unplanned deployment, a mobile node moving at vehicular speed experiences periods of connectivity and disruption as it exploits roadside WLAN APs. Although several works have studied the performance of WLAN APs in providing network services at vehicular speed, very little has been reported on their mathematical interpretation. This paper proposes a hidden Markov model (HMM) to represent a typical roadside-to-vehicle communication. The proposed model is based on the experimental findings and is used to quantify the probabilistic measure of disruption. To the best of the authors' knowledge, this is the first effort that quantifies the aggregate amount of disruption in 802.11-based roadside-to-vehicle communications. [ABSTRACT FROM PUBLISHER]

Collaborative spectrum sensing (CSS) between secondary users (SUs) in cognitive networks exhibits an inherent tradeoff between minimizing the probability of missing the detection of the primary user (PU) and maintaining a reasonable false alarm probability (e.g., for maintaining good spectrum utilization). In this paper, we study the impact of this tradeoff on the network structure and the cooperative incentives of the SUs that seek to cooperate to improve their detection performance. We model the CSS problem as a nontransferable coalitional game, and we propose distributed algorithms for coalition formation (CF). First, we construct a distributed CF algorithm that allows the SUs to self-organize into disjoint coalitions while accounting for the CSS tradeoff. Then, the CF algorithm is complemented with a coalitional voting game to enable distributed CF with detection probability (CF-PD) guarantees when required by the PU. The CF-PD algorithm allows the SUs to form minimal winning coalitions (MWCs), i.e., coalitions that achieve the target detection probability with minimal costs. For both algorithms, we study and prove various properties pertaining to network structure, adaptation to mobility, and stability. Simulation results show that CF reduces the average probability of miss per SU up to 88.45%, relative to the noncooperative case, while maintaining a desired false alarm. For CF-PD, the results show that up to 87.25% of the SUs achieve the required detection probability through MWCs. [ABSTRACT FROM PUBLISHER]

The enhanced distributed channel access (EDCA) mechanism of the IEEE 802.11e standard provides quality-of-service (QoS) support through service differentiation by using different medium-access-control (MAC) parameters for different stations. The configuration of these parameters, however, is still an open research challenge, as the standard provides only a set of fixed recommended values that do not take into account the current wireless local area network (WLAN) conditions and, therefore, lead to suboptimal performance. In this paper, we propose a novel algorithm for EDCA that, given the throughput and delay requirements of the stations that are present in the WLAN, computes the optimal configuration of the EDCA parameters. We first present a throughput and delay analysis that provides the mathematical foundation upon which our algorithm is based. This analysis is validated through simulations of different traffic sources (both data and real time) and EDCA configurations. We then propose a mechanism to derive the optimal configuration of the EDCA parameters, given a set of performance criteria for throughput and delay. We assess the effectiveness of the configuration provided by our algorithm by comparing it against 1) the recommended values by the standard, 2) the results from an exhaustive search over the parameter space, and 3) previous configuration proposals, which are both standard and nonstandard compliant. Results show that our configuration outperforms all other approaches. [ABSTRACT FROM AUTHOR]

In this paper, we model the various users in a wireless network (e.g., cognitive radio network) as a collection of selfish autonomous agents that strategically interact to acquire dynamically available spectrum opportunities. Our main focus is on developing solutions for wireless users to successfully compete with each other for the limited and time-varying spectrum opportunities, given experienced dynamics in the wireless network. To analyze the interactions among users given the environment disturbance, we propose a stochastic game framework for modeling how the competition among users for spectrum opportunities evolves over time. At each stage of the stochastic game, a central spectrum moderator (CSM) auctions the available resources, and the users strategically bid for the required resources. The joint bid actions affect the resource allocation and, hence, the rewards and future strategies of all users. Based on the observed resource allocations and corresponding rewards, we propose a best-response learning algorithm that can be deployed by wireless users to improve their bidding policy at each stage. The simulation results show that by deploying the proposed best-response learning algorithm, the wireless users can significantly improve their own bidding strategies and, hence, their performance in terms of both the application quality and the incurred cost for the used resources. [ABSTRACT FROM AUTHOR]