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Path probability prediction is essential to describe the dynamic birth and death of propagation paths, and build the accurate channel model for air-to-ground (A2G) communications. The occurrence probability of each path is complex and time-variant due to fast changeable altitudes of UAVs and scattering environments. Considering the A2G channels under urban scenarios, this paper presents three novel stochastic probability models for the line-of-sight (LoS) path, ground specular (GS) path, and building-scattering (BS) path, respectively. By analyzing the geometric stochastic information of three-dimensional (3D) scattering environments, the proposed models are derived with respect to the width, height, and distribution of buildings. The effect of Fresnel zone and altitudes of transceivers are also taken into account. Simulation results show that the proposed LoS path probability model has good performance at different frequencies and altitudes, and is also consistent with existing models at the low or high altitude. Moreover, the proposed LoS and NLoS path probability models show good agreement with the ray-tracing (RT) simulation method., 10 pages, 9 figures, the journal submitted to is IEEE Internet of Things Journal

Benefiting from maneuverability, flexibility, and low-cost deployment, aerial base stations (ABSs) have emerged as a promising solution to meet the coverage demand when terrestrial BSs are overloaded and unavailable. Nevertheless, the high mobility of ABSs and the complicated interference incurred by the addition of ABSs inevitably cause the spatial-temporal discontinuity in air-to-ground (A2G) coverage, which renders the network unable to provide users with on-demand coverage. On this account, this article discusses how to enhance the A2G coverage by exploiting the ever more enhanced computation capability of network edge nodes. In particular, we propose a coverage-oriented computing control architecture for adaptive coverage structure generation and resource orchestration based on the designed optimal deployment scheme for ABSs. This architecture can flexibly adjust the coverage structure and available resources to ensure the spatial continuity in A2G coverage. Furthermore, we design an efficient aerial-computing-based resource management scheme for ABSs to enable temporal continuity in A2G coverage by exploiting artificial intelligence approaches.

For indoor localization system, the integration of various fingerprints could intuitively improve localization accuracy since different fingerprints complement each other. In spite of the potential benefits, however, the limitation of applying various fingerprints in improving localization accuracy still remains unknown. Moreover, how to design efficient indoor localization methods through fully exploiting the features of different fingerprints is to be explored as well. In this work, we investigate the location error of a fingerprint-based indoor system with the application of hybrid fingerprints. It manifests that the location error is dependent on correlation coefficient of different types of fingerprints. For instance, the exploiting correlation between different types of fingerprints is shown to effectively reduce the location error, which gradually decreases with the number of adopted fingerprints. On this basis, we propose a hybrid received signal strength (RSS) and channel state information (CSI) localization algorithm (HRC), which is designed based on deep learning. The HRC fully exploits quick construction of fingerprint database with coarse-grained RSS and rich multipath information of fine-grained CSI. The RSS and CSI with high correlation are selected to construct fingerprint database, aiming to improve localization accuracy. Moreover, a deep auto-encoder is used to reduce the computation complexity and the deep neural network is trained for location estimation. Experimental results, which are obtained by designed indoor localization system, validate that the location error of HRC can be reduced by 77.3% and 20.3%, compared with the existing localization methods and HRC without RSS/CSI selection by correlation coefficient, respectively.

Capable of supporting the on-demand high-rate connection towards the core network, wireless millimeter wave (mmWave) backhaul becomes an appealing solution with the growing deployment of small cell base stations (BSs). However, the application of wireless backhaul would impose great limitations on the admission of BSs and the access rate, resulting in a tight coupling between backhaul and radio access network (RAN). In this light, we comprehensively investigate the performance of small cell networks (SCN) in terms of network spatial throughput (ST), supposing that backhaul is conveyed from gateways to BSs through mmWave links. Our results show that the coupling effect makes network ST in the RAN greatly dependent on gateway density $\lambda _{\mathrm{G}}$ and suffer exponential degradation with over-provision of backhaul. It manifests that, if excessive backhaul capacity is provided by deploying more gateways, the exacerbating BS-generated interference deteriorates spatial reuse gain, thereby degrading network ST. Thus, leveraging the coupling between backhaul and RAN, we further conduct a joint optimization of BS and gateway densities to improve network ST. Remarkably, the optimization could mitigate the dependence of network ST on $\lambda _{\mathrm{G}}$ and brings a 2-fold increase in network ST, which is inversely proportional to mmWave beamforming beamwidth.

The access point (AP) deployment is a fundamental task for constructing an accurate localization system. Existing literature mainly deals with the AP placement problem using optimal geometry analysis since the target-AP geometry will affect the localization performance. However, some non-ideal phenomena in practical scenario, e.g., the existence of obstacles, array orientation and path loss, will degrade the accuracy of angle-of-arrival (AoA) estimation as well as the localization accuracy. In this article, we reformulate the AP planning incorporating these factors. We decompose the problem into two subproblems, namely AP selection problem and error minimization problem. The AP selection problem selects the minimum number of APs to satisfy a desired localization accuracy, aided by a refined orientation updating procedure. We design a centralized and a distributed error minimization algorithm to further decrease the localization error. The centralized algorithm shows superiority in time efficiency. Nevertheless, the case with large number of APs may lead to excessive computational cost. Accordingly, we further devise the distributed algorithm which is adaptive to large-scale deployment. Numerical studies in indoor environments with barriers are conducted to verify our proposed approach.

This letter models the high-altitude air-to-ground (A2G) channel for aerial base stations (BSs), which are carried by fixed-wing unmanned aerial vehicles (UAVs). Different from ground BS, fixed-wing UAVs have to periodically hover to provide coverage to ground users (GUs), which may result in the instability of A2G wireless links. On the one hand, pathloss is modeled as a function of distance from UAV to GUs, which includes hover radius and flight altitude, and the elevation angle of GUs. On the other hand, the model of multipath and shadow (MS) fading is dependent on the elevation angle as well. Specifically, we show that generalized Gamma distribution is suitable to model MS fading under large elevation angle, while log-normal fading is suitable otherwise. Moreover, we further investigate the spatial-temporal correlation of the A2G channel, which is shown to depend on the movement distance/direction of GU and the distance from GU to the hover center.

Non-orthogonal multiple access (NOMA)-enabled coordinated multipoint (CoMP) transmission has great potential in balancing spectrum utilization and interference mitigation in dense cellular networks. However, NOMA reforms the spectrum sharing policy of CoMP transmission due to introducing additional interference among coordinated base stations (BSs), which deteriorates the CoMP transmission rate. In this paper, we investigate the joint optimization of BS clustering and power control for NOMA-enabled CoMP transmission in dense cellular networks to maximize system sum-rate. We first characterize the impact of interference among coordinated BSs on CoMP transmission rate and find that the BS clustering is dependent on NOMA user grouping and restricted by the NOMA decoding condition. We then derive a tight lower bound on the system sum-rate and exploit it to design a joint BS clustering and power control scheme. Specifically, a power control algorithm is designed by a penalty convex-concave procedure to satisfy the NOMA decoding condition and users’ rate requirements. A BS clustering algorithm based on successive convex approximation is designed to iteratively update the BS clustering and NOMA user grouping to increase system sum-rate. Finally, two algorithms are alternately performed until all users successfully access and the system sum-rate converges. Simulation results show that the proposed scheme can efficiently alleviate interference among coordinated BSs to improve system sum-rate and spectrum efficiency of NOMA-enabled CoMP transmission even under high user density.

Exploiting the time-varying feature of wireless channels, retransmission could enable reliable data transmission. However, the growing deployment of small cell base stations (BSs) would induce significant temporal interference correlation. In consequence, once the current transmission fails, the subsequent ones are likely to fail as well. In this light, we investigate the retransmission performance in dense networks with temporally correlated interference in terms of network spatial throughput (ST). Our results reveal that the impact of temporal interference correlation on the retransmission performance critically depends on the BS density. Specifically, temporal interference correlation would cause a greater network ST attenuation when the BS density is closer to the critical density, under which network ST is maximized. Moreover, the impact of temporal interference correlation is shown to be cumulative as the number of retransmission attempts increases. Furthermore, towards efficient retransmission, we adopt and optimize a P-Activation strategy (PAS). It is shown that the optimized PAS is able to effectively mitigate the overwhelming strength and temporal correlation of interference. As a result, the retransmission performance in improving network ST is significantly enhanced in dense networks, while the variation of network ST with BS density exhibits sigmoid trend instead of the previous near-bell shape.

In mobile edge computing (MEC), the computation offloading of massive users could cause the task uploading congestion to deteriorate the users’ offloading delay. The non-orthogonal multiple access (NOMA) enabled MEC is envisioned to address this issue by allowing multiple users to simultaneously upload their tasks on one subchannel. However, the differentiated uploading delay of users may make task uploading completion inconsistent with NOMA decoding order, which complicates the co-channel interference and restricts NOMA to reducing the uploading delay. In this paper, we characterize the interaction between the differentiated uploading delay and co-channel interference for a pair of NOMA users. Furthermore, we propose a computation offloading scheme to reduce the users’ average offloading delay by jointly optimizing offloading decision and resource allocation. Specifically, the proposed scheme first obtains the optimal power allocation based on the characterized interaction and the closed-form solution of computation resource allocation by convex programming. Then, the NOMA user pairing and offloading decision are iteratively determined by semidefinite relaxation and convex-concave procedure. Simulation results show that the proposed scheme effectively mitigates co-channel interference under differentiated uploading delay of users and outperforms in reducing the users’ average offloading delay and increasing the number of users to offload tasks.

Owing to the multiple antennas equipped at modern Wi-Fi infrastructures, the angle-of-arrival (AoA) based indoor localization systems have successfully achieved the accuracy of tens of centimeters. However, the high accuracy is acquired at the cost of employing the additional resources in the domains of frequency, space or time, which requires complex processing and hinders the practical application. In this paper, we present the design and implementation of RcLoc, which takes full advantages of the flexible array orientations and receiver positions, based on limited resources. Particularly, RcLoc devises a receiver configuration scheme for guiding the system deployment. Optimized array orientation could effectively improve the AoA estimation accuracy and well-designed receiver positions contribute to the Cramer-Rao lower bound of localization error. In the stage of system realization, we further devise an array calibration method to calibrate the actual array and develop an improved AoA estimation algorithm, which make RcLoc robust to the array arrangement. We prototype RcLoc on commodity Wi-Fi devices without manual intervention. Comprehensive experiments in a multipath-rich indoor environment show that RcLoc achieves a median localization accuracy of 0.4 m, which provides useful insights for receiver deployment.

This paper aims at shedding light on the impact of unsaturated traffic on the performance of uplink non-orthogonal multiple access (NOMA) transmissions. Nevertheless, the unsaturated traffic gives rise to the discontinuous interference and the inherent interaction of queues, which in turn highly complicates the performance evaluation. By utilizing tools from queuing theory, we first explicitly characterize the stable throughput region, which represents the region of traffic arrival rates on the condition that the queuing delay converges in distribution to a bounded random variable. In light of this, the critical condition under which NOMA can extend the stable throughput region of orthogonal multiple access (OMA) is derived. Then, we propose an algorithmic solution to evaluate the average delay incurred from both queuing and transmission. It is interestingly found that the superiority of NOMA over OMA in terms of average delay heavily hinges on the temporal traffic dynamics of each user. In particular, NOMA enjoys a clear advantage when the traffic arrival rate of the user with stronger channel condition considerably exceeds the traffic arrival rate of the user with weaker channel condition. The derived results can provide helpful guidance to fully leverage the comparative advantages of NOMA under various traffic conditions.

This paper takes a Halbach permanent magnet linear motor (PMLM) without a core as the research subject; based on the traditional structure of a single-layer PM array, an improved PMLM with an optimized double-layer Halbach PM array is proposed to reduce the harmonic components of the air gap magnetic flux density (AGMFD) for direct-drive applications in the feeding system of the machine tool which need high force capability. The 3-D models of PMLMs with single-layer and double-layer Halbach PM arrays are established by using finite element (FE) software; the air gap magnetic field, the back electromotive force (EMF), and the electromagnetic thrust of the two types of motors are analyzed. The simulated results show that the PMLM with an optimized double-layer Halbach PM array can achieve better performances of the motor and reduce the cost of the PMLM. Finally, the experimental results verify that the proposed double-layer Halbach PMLM is effective and practical.

The 5G cellular network employs non-orthogonal multiple access (NOMA) to enhance network connectivity and capacity, and device-to-device (D2D) communications to improve spectrum efficiency. However, the underlay D2D communications may destroy the execution condition for the successive interference cancellation (SIC) decoding of NOMA cellular networks by introducing the extra interference, which degrades the cellular transmission reliability. Thus, we develop the interlay mode as a special D2D mode for NOMA system, which enables the power domain multiplexing of the D2D pair and cellular users to eliminate the strong interference between them by the SIC decoding. When D2D pair conducts the selection between the interlay mode and underlay mode, the SIC decoding constraint should be satisfied at both D2D receiver and NOMA base station. In order to maximize the system sum rate while meeting the SIC decoding constraint, we propose a joint D2D mode selection and resource allocation scheme with interlay mode, which can be formulated as a combinatorial optimization problem. To tackle the combinatorial nature of mode selection and spectrum assignment, we first prove that the original problem can be reformulated as a maximum weight clique problem, and then propose a graph-based algorithm by applying branch-and-bound method to obtain its optimal solution. Finally, simulation results are provided to demonstrate that the interlay mode along with the proposed algorithms can coordinate D2D communications and NOMA cellular network to significantly improve the system sum rate and the D2D access rate.

Unmanned aerial vehicles (UAVs) have been extensively applied as aerial access points to assist the terrestrial wireless network. Despite the inherent potential, nevertheless, it still remains to explore whether the gain of UAV access points (UAPs) could be fully harvested, which is critically dependent on the factors including the flight altitude and deployment density of UAPs. In this light, we investigate the performance of a downlink UAV integrated terrestrial cellular network (UTCN) and analytically study the influence of varying UAP altitude and density on the spatial throughput (ST) of UTCN. In particular, we obtain the UAP altitude upper bound, below which more line-of-sight (LOS) connections could be provided to improve network ST. Otherwise, cross-layer interference over the LOS paths becomes dominant, which results in significant degradation of network ST. More importantly, we reveal the limitation of the application of UAPs by showing that there exists a critical UAP density, beyond which network ST would encounter a rapid decrease. To fully exploit the potential of UAPs, we further tailor a probabilistic interference avoidance scheme and study the optimization of the UAP activated probability. Remarkably, network ST could be substantially improved using the optimized activated probability, i.e., network ST could increase with the growing UAP density and converge to a positive constant instead of zero in the dense UAP regime. Therefore, the results of this paper could provide insight on the deployment and optimization of UTCN.

For angle-of-arrival (AoA)-based localization methods, the AoA estimation accuracy would notably influence the localization performance. In this letter, we evaluate the estimation accuracy of different AoAs in a multipath environment and propose a weighted AoA-based localization approach. Specifically, the AoA estimation accuracy is quantified by deriving a closed form expression of asymptotic variance of the AoA estimation error. However, in practice, the accessible information for receivers is insufficient to retrieve the variance. Accordingly, we still find an approximate but effective substitution to evaluate the AoA estimation accuracy on basis of the limited information. Aided by the above analysis, we design the weighted localization method, where each estimated AoA is weighted with corresponding factor related to the AoA estimation accuracy. Simulations show that our proposed localization method decreases the median localization error by 20% compared with the unweighted method.

Signal processing on antenna arrays has recently received extensive attention in the area of angle-of-arrival (AoA)-based indoor localization. Although sufficient array elements can improve the resolution in the AoA estimation, the array orientation has not been well exploited in research into the localization performance. In this paper, we investigate the effect of array orientations on the performance of AoA-based indoor localization systems. Appropriate array orientation can efficiently reduce the uncertainty in AoA estimation, thereby improving the localization accuracy. Accordingly, we present OpArray, an accurate indoor localization system based on flexible array deployment. First, OpArray designs an array deployment scheme, which establishes the foundation for accurate AoA estimates. The deployment scheme can be easily implemented through array rotations so as to optimize array orientations at receivers. Second, OpArray incorporates two refined phase preprocessing algorithms to mitigate the impact of negative factors, which exist in the practical implementation. In addition, aided by an improved AoA estimation algorithm, OpArray can localize a target on commercial off-the-shelf Wi-Fi platforms. Our experiments in a multipath-rich indoor environment show that OpArray achieves a median localization error of 0.5 m and the 80th percentile error is 1.0 m, which outperforms the state-of-the-art localization systems.

Network densification is proved to be the most efficient method to expand network capacity over the past few decades. Nevertheless, can network densification always boost network performance? To shed light on this, we explore the pros and cons of network densification in $k$ -dimension downlink small cell networks by analyzing transmission success probability (SP) and network spatial throughput (ST). Remarkably, different from long-distance propagation in sparse networks, short-distance propagation of distinctive features dominates as well under dense deployment of small cells. On this account, we apply a multislope bounded pathloss model (MBPM) to characterize signal attenuation within long and short propagation distance. Under MBPM, it is surprisingly found that network densification would enhance SP and ST only when small cell base station density $\lambda \left(\mathrm{SBS}/\mathrm{m}^{k}\right)$ is smaller than some critical densities. Beyond the critical densities, both SP and ST are proved to exponentially diminish with $\lambda$ and even approach zero when $\lambda$ is sufficiently large. In other words, ultradense deployment of small cells indeed deteriorates network performance. Besides, the numerically derived critical densities are helpful for the practical deployment of small cell networks.

In this paper, we investigate the limitation of multi-user multiple-input multiple-output (MIMO) in ultra-dense networks (UDNs) and investigate how to overcome the limitation by designing efficient interference management strategies. Specifically, it is shown that the area spectral efficiency (ASE), an indicator to network capacity, would approach zero with over-deployed base stations (BSs) even when multi-user MIMO is applied. Worse still, it manifests that the multi-user gain of MIMO cannot be harvested in UDN due to the overwhelming interference. In particular, the maximal ASE is shown to be degraded by increasing the number of served users in each cell. To alleviate the bottleneck brought by interference, we have designed and optimized two simple but efficient BS activation policies. It is shown that the application of the optimized BS activation policy could improve network capacity scaling law and boost the potential of multi-user MIMO in UDN. Remarkably, the ASE is shown to increase with BS density $\lambda $ even when $\lambda $ and user density are sufficiently large. Moreover, the maximal ASE in the $\lambda \rightarrow \infty $ regime is shown to increase with the number of served users in each cell, which contradicts with the all-BS-on case.

Sparse code multiple access (SCMA) is emerging as a promising multiple access technology for the fifth-generation wireless networks. By leveraging user-specific SCMA codebooks, SCMA possesses the potential to enable non-orthogonal resource access, and thus leading to the overloading gain. Benefiting from this, SCMA can accommodate more users and achieve a higher throughput than orthogonal frequency division multiple access. In this correspondence, we propose a tractable analytical framework using tools from stochastic geometry to investigate the performance of SCMA wireless networks. In particular, we obtain explicit closed-form expressions for the success probability and the area spectral efficiency (ASE). Based on the derived results, we prove that the ASE is a quasi-concave function of the transmitter density. Moreover, we shed light on the impact of SCMA codeword dimension $K$ and the number of non-zero entries in SCMA codeword $N$ on the network performance. Specifically, we demonstrate that the optimal transmitter density and the maximum ASE scale super-linearly with rate $K^{N}$ . Simulation results validate the accuracy of the presented analysis and exhibit the superiority of SCMA.

Besides advanced telecommunications techniques, the most prominent evolution of wireless networks is the densification of network deployment. In particular, the increasing access points/users density and reduced cell size significantly enhance spatial reuse, thereby improving network capacity. Nevertheless, does network ultra-densification and over-deployment always boost the performance of wireless networks? Since the distance from transmitters to receivers is greatly reduced in dense networks, signal is more likely to be propagated from long- to short-range region. Without considering short-range propagation features, conventional understanding of the impact of network densification becomes doubtful. With this regard, it is imperative to reconsider the pros and cons brought by network densification. In this article, we first discuss the short-range propagation features in densely deployed network and verify through experimental results the validity of the proposed short-range propagation model. Considering short-range propagation, we further explore the fundamental impact of network densification on network capacity, aided by which a concrete interpretation of ultra-densification is presented from the network capacity perspective. Meanwhile, as short-range propagation makes interference more complicated and difficult to handle, we discuss possible approaches to further enhance network capacity in ultra-dense wireless networks. Moreover, key challenges are presented to suggest future directions., submitted to IEEE Commun. Mag

Capable of enhancing spatial multiplexing and greatly expanding network capacity, network densification has undoubtedly become a general trend in future wireless networks. However, in addition to the expected advantages, the overuse of spatial resources would result in unpredictable and overwhelming interference as well due to the limited spectrum resources and highly disorganized network infrastructures. Hence, a critical issue arises: are conventional IM techniques still applicable and effective to tackle the interference in UDN? To shed light on the problem, in this article we first investigate the characteristics of interference in UDN and then provide an overview of the existing dominant IM techniques. It is found that, due to the complicated interference, most IM techniques designed for sparse networks would lose their merits when applied in UDN. To reap their potential benefits, we design an IM entity for UDN, aided by which an adaptive on-off power control method is proposed. Results reveal that network capacity could be significantly improved using the proposed method, especially in UDN. In addition, open issues and challenges for IM co-design with stateof- the-art communications technologies are highlighted to provide insight on the potential direction in future wireless networks.

In this letter, we have investigated the frequency diversity gain of a sparse code multiple access (SCMA) system to improve spectrum utilization by exploiting the codebook correlation in the SCMA system. To this end, a backtracking codebook matching (BCM) algorithm is proposed, whereby there are more successfully scheduled users, which are allocated the low-correlated codebooks. Simulation results exhibit the superiority of the BCM algorithm and the impact of codebook correlation on scheduling.

End users’ quality of experience (QoE) is one of the most crucial requirements to be considered in device-to-device communication. Users’ QoE is affected by the ratio of the amount of received data to the amount of shared data. The ratio should be approximate to 1. To achieve this, we propose an evolutionary algorithm-based cooperative content caching and communication (EACCC) scheme. This scheme works by finding the maximum 1-factor of a directed weighted graph, where each edge’s weight corresponds to the mean opinion score of the connection between two users. Finding the maximum 1-factor of graph-based communication is a problem that is difficult to solve optimally, but an evolutionary algorithm can quickly converge to a high-quality solution. Each user provides an opinion score of incoming and outgoing connection, and the maximum 1-factor corresponds to the scheme that maximizes overall QoE. The proposed EACCC scheme achieves optimal connections among users in a vast and complex network, due to strong mutual communication. The simulation results verify that our proposed scheme outperforms traditional QoE optimization schemes. Moreover, our scheme can be implemented easily in a realistic scenario.

To cater for the exploding growth of video traffic, small cell base stations (SBSs) and deviceto- device-enabled caching and delivery have been regarded as promising techniques for future wireless networks. In this article, we design a proximity communications enhanced multilayer caching and delivery architecture. Then merits possessed by the proposed architecture are highlighted, and challenges and open issues are comprehensively presented. Specifically, we shed light on the trade-offs between key performance indicators (e.g., hit ratio, latency, and coverage) and operation costs (e.g., device storage space, wireless bandwidth, and device battery life), and then clarify fundamental coupling between content caching and delivery. To further verify the effectiveness of the cooperation among SBSs and user equipments, we propose a distributed content caching and delivery strategy, jointly considering popularity distribution, diverse storage capability, and user mobility. Simulation results demonstrate that the proposed strategy can significantly lower the content retrieval latency and reduce the traffic flowing to core networks. Furthermore, design details of the experimental testbed are presented, and the validity of our developed strategy is verified.

Coordinated multipoint (CoMP) is an efficient technique to increase cell-edge coverage probability and throughput in cloud radio access network (C-RAN). In this paper, we integrate device-to-device (D2D) communications with CoMP by applying a distance based mode selection rule for downlink users in C-RAN, exploiting the proximity of D2D communications to improve system spectral efficiency. Using stochastic geometry, we first derive the signal-to-interference ratio distribution at a typical downlink user and a typical D2D receiver when two types of CoMP schemes, namely, zero-forcing beamforming (ZFBF) and noncoherent joint transmission (NC-JT), are applied in C-RAN. In addition, we analytically compare ZFBF and NC-JT using rate coverage probability. Meanwhile, we analyze the effect of D2D communications on enhancing the area spectral efficiency (ASE) of CoMP enabled C-RAN system. Numerical results show that increasing the co-operative cluster size would potentially degrade the system ASE when the network is heavily loaded. Furthermore, it is observed that enabling D2D mode in C-RAN can effectively offload the traffic of C-RAN and provide significant ASE gains if mode selection threshold is properly designed. Lastly, it is analytically demonstrated that spectrum resources can be better exploited by D2D users if they coexist with ZFBF enabled radio units (RUs) rather than NC-JT enabled RUs.

As a major supplement to LTE-Advanced, D2D communications underlaying cellular networks have proven efficient in offloading network infrastructures and improving network performance. The scheduling mechanism plays a key role in providing better user experience in D2D communications. However, controlled by operators, D2D communications pose specific problems that do not exist in available wireless networks. Therefore, mature scheduling mechanisms devised for cellular networks or ad hoc networks are not directly applicable to D2D communications. In this article, we first review recent research on scheduling mechanisms for D2D communications, and discuss the design considerations and implementation challenges. Then we propose an on-demand scheduling mechanism, DO-Fast, which can provide QoS differentiation capabilities. Next, we provide performance evaluations based on simulations and an experimental testbed. Finally, we conclude this article and point out possible directions for future research.

In this paper, we characterize the transmission capacity region (TCR) in D2D integrated cellular networks when two prevalent interference management techniques, power control and Successive Interference Cancellation (SIC) are utilized. The TCR is defined as the enclosure of all feasible sets of active transmitter intensities in cellular and D2D systems. Closed-form approximate expressions of TCR are derived for two spectrum sharing modes, i.e., reuse mode and dedicated mode. The analysis provides insights into the impact of network parameters, interference management methods, as well as bandwidth allocation policy on the TCR. Moreover, we compare the reuse mode and dedicated mode in terms of TCR. Specifically, with power control, given the same target rate for cellular users and D2D users, the TCR of the dedicated mode is shown to be entirely enclosed by that of the reuse mode when $2^{\alpha\over 2}\leq\theta+2$ , where $\alpha$ and $\theta$ are, respectively, the path loss exponent and decoding threshold. However, with SIC utilized, numerical results show that when $\theta>1$ , better performance can always be achieved by the reuse mode in terms of TCR. The results can serve as a guideline for the design of efficient interference management techniques and spectrum regulation in D2D integrated cellular networks.