Search

The opportunistic beamforming system (OBS) is currently receiving much attention in the field of downlink beamforming due to its simple random beamforming, low feedback complexity, and same throughput scaling obtained with perfect channel-state information using dirty paper coding at the transmitter. In this paper, we focus on its closed-form throughput evaluation over Rayleigh fading channels, based on the asymptotic theory of extreme order statistics. First, the throughput of a single-beam OBS is investigated, and an analytical solution tighter than the previously reported one is derived. Then, the asymptotic throughput bounds on a multibeam OBS are presented, and also, our analytical expression is shown to be very tight with the simulation results even with fewer users. After that, we argue that the reported conclusion that the single-beam OBS is much preferable to the multibeam OBS in the high-signal-to-noise-ratio (SNR) regime is inaccurate, but that, instead, it is satisfied only when the number of users is very small, due to its limited multiuser diversity gain. Finally, we show that four transmit beams is the most preferable in the multibeam OBS with a large number of users and moderate SNR, which arrives at the tradeoff between increasing spatial multiplexing gain and disappearing multiuser diversity gain. Index Terms--Multiple-input single-output (MISO), multiuser diversity, opportunistic beamforming system (OBS), spatial multiplexing, throughput.

Wireless powered backscatter communications (WPBC) is capable of implementing ultra-low-power communication, thus promising in the Internet of Things (IoT) networks. In practice, however, it is challenging to apply WPBC in large-scale IoT networks because of its short communication range. To address this challenge, this paper exploits an unmanned ground vehicle (UGV) to assist WPBC in large-scale IoT networks. In particular, we investigate the joint design of network planning and dynamic resource allocation of the access point (AP), tag reader, and UGV to minimize the total energy consumption. Also, the AP can operate in either half-duplex (HD) or full-duplex (FD) multiplexing mode. Under HD mode, the optimal cell radius is derived and the optimal power allocation and transmit/receive beamforming are obtained in closed form. Under FD mode, the optimal resource allocation, as well as two suboptimal ones with low computational complexity, is developed. Simulation results disclose that dynamic power allocation at the tag reader rather than at the AP dominates the network energy efficiency while the AP operating in FD mode outperforms that in HD mode concerning energy efficiency. [ABSTRACT FROM AUTHOR]

A fast blind adaptive beamforming algorithm is proposed in code-division multiple-access wireless systems based on an array pattern synthesis approach. By incorporating an interference suppression mechanism in the iterative procedure, the proposed algorithm outperforms conventional blind algorithms. Two practical adaptive beamforming algorithms are further presented, which provide fast convergence speed with low complexity. The simulation results demonstrate the effectiveness of the proposed adaptive beamforming algorithms in various signal environments. Index Terms--Adaptive beamforming, blind algorithm, fast convergence, low complexity.

This work studies multigroup multicasting transmission in cloud radio access networks (C-RANs) with simultaneous wireless information and power transfer where densely packed remote radio heads (RRHs) cooperatively provide information and energy services for information users (IUs) and energy users (EUs) respectively. To maximize the weighted sum rate (WSR) of information services while satisfying the energy harvesting levels at EUs an optimization of joint beamforming design for the fronthaul and access links is formulated which is however neither smooth nor convex and is indeed NP-hard. To tackle this difficulty the smooth and successive convex approximations are used to transform the original problem into a sequence of convex problems and two first-order algorithms are developed to find the initial feasible point and the nearly optimal solution respectively. Moreover an accelerated algorithm is designed to improve the convergence speed by exploiting both Nesterov and heavy-ball momentums. Numerical results demonstrate that the proposed first-order algorithms achieve almost the same WSR as that of traditional second-order approaches yet with much lower computational complexity and the proposed scheme outperforms state-of-the-art competing schemes in terms of WSR. [ABSTRACT FROM AUTHOR]

This work investigates the energy-efficient resource allocation for layered-division multiplexing (LDM) based non-orthogonal multicast and unicast transmission in cell-free massive multiple-input multiple-output (MIMO) systems, where each user equipment (UE) performs wireless information and power transfer simultaneously. To begin with, the achievable data rates for multicast and unicast services are derived in closed form, as well as the received radio frequency (RF) power at each UE. Based on the analytical results, a nonsmooth and nonconvex optimization problem for energy efficiency (EE) maximization is formulated, which is however a challenging fractional programming problem with complex constraints. To suit the massive access setting, a first-order algorithm is developed to find both initial feasible point and the nearly optimal solution. Moreover, an accelerated algorithm is designed to improve the convergence speed. Numerical results demonstrate that the proposed first-order algorithms can achieve almost the same EE as that of second-order approaches yet with much lower computational complexity, which provides insight into the superiority of the proposed algorithms for massive access in cell-free massive MIMO systems. [ABSTRACT FROM AUTHOR]

Non-orthogonal multiple access (NOMA) is a promising solution for secure transmission under massive access. However, in addition to the uncertain channel state information (CSI) of the eavesdroppers due to their passive nature, the CSI of the legitimate users may also be imperfect at the base station due to the limited feedback. Under both channel uncertainties, the optimal power allocation and transmission rate design for a secure NOMA scheme is currently not known due to the difficulty of handling the probabilistic constraints. This article fills this gap by proposing novel transformation of the probabilistic constraints and variable decoupling so that the security guaranteed sum-rate maximization problem can be solved by alternatively executing branch-and-bound method and difference of convex programming. To scale the solution to a truly massive access scenario, a first-order algorithm with very low complexity is further proposed. Simulation results show that the proposed first-order algorithm achieves identical performance to the conventional method but saves at least two orders of magnitude in computation time. Moreover, the resultant transmission scheme significantly improves the security guaranteed sum-rate compared to the orthogonal multiple access transmission and NOMA ignoring CSI uncertainty. [ABSTRACT FROM AUTHOR]

In this letter, an adaptively-regularized iterative reweighted least squares algorithm with sparsity bound learning is designed to efficiently recover sparse signals from measurements. In particular, at each iteration the support of estimated signal is exploited to construct a sparsity-promoting matrix and, then, formulate an adaptive regularization. Since this algorithm could learn sparsity information at each iteration, it ensures a sparser and sparser solution, and the mean squared error analysis corroborates its convergence. Experimental results demonstrate that the proposed algorithm outperforms other typical ones in terms of sparsity level, compressive ratio, and detection probability. [ABSTRACT FROM AUTHOR]

While machine-type communication (MTC) devices generate considerable amounts of data, they often cannot process the data due to limited energy and computational power. To empower MTC with intelligence, edge machine learning has been proposed. However, power allocation in this paradigm requires maximizing the learning performance instead of the communication throughput, for which the celebrated water-filling and max-min fairness algorithms become inefficient. To this end, this paper proposes learning centric power allocation (LCPA), which provides a new perspective on radio resource allocation in learning driven scenarios. By employing 1) an empirical classification error model that is supported by learning theory and 2) an uncertainty sampling method that accounts for different distributions at users, LCPA is formulated as a nonconvex nonsmooth optimization problem, and is solved using a majorization minimization (MM) framework. To get deeper insights into LCPA, asymptotic analysis shows that the transmit powers are inversely proportional to the channel gains, and scale exponentially with the learning parameters. This is in contrast to traditional power allocations where quality of wireless channels is the only consideration. Last but not least, a large-scale optimization algorithm termed mirror-prox LCPA is further proposed to enable LCPA in large-scale settings. Extensive numerical results demonstrate that the proposed LCPA algorithms outperform traditional power allocation algorithms, and the large-scale optimization algorithm reduces the computation time by orders of magnitude compared with MM-based LCPA but still achieves competing learning performance. [ABSTRACT FROM AUTHOR]

In this paper, a novel 3D cellular model consisting of aerial base stations (aBSs) and aerial user equipments (aUEs) is proposed, by integrating the coordinated multi-point (CoMP) transmission technique with the theory of stochastic geometry. For this new 3D architecture, a tractable model for aBSs’ deployment based on the binomial-Delaunay tetrahedralization is developed, which ensures seamless coverage for a given space. In addition, a versatile and practical frequency allocation scheme is designed to eliminate the inter-cell interference effectively. Based on this model, performance metrics including the achievable data rate and coverage probability are derived for two types of aUEs: i) the general aUE (i.e., an aUE having distinct distances from its serving aBSs) and ii) the worst-case aUE (i.e., an aUE having equal distances from its serving aBSs). Simulation and numerical results demonstrate that the proposed approach emphatically outperforms the conventional binomial-Voronoi tessellation without CoMP. Insightfully, it provides a similar performance to the binomial-Voronoi tessellation which utilizes the conventional CoMP scheme; yet, introducing a considerably reduced computational complexity and backhaul/ signaling overhead. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output communications can achieve high-level security by concentrating radio frequency signals towards the legitimate users. However, this system is vulnerable in a Rician fading environment if the eavesdropper positions itself such that its channel is highly “similar” to the channel of a legitimate user. To address this problem, this paper proposes an angle aware user cooperation (AAUC) scheme, which avoids direct transmission to the attacked user and relies on other users for cooperative relaying. The proposed scheme only requires the eavesdropper’s angle information, and adopts an angular secrecy model to represent the average secrecy rate of the attacked system. With this angular model, the AAUC problem turns out to be nonconvex, and a successive convex optimization algorithm, which converges to a Karush-Kuhn-Tucker solution, is proposed. Furthermore, a closed-form solution and a Bregman first-order method are derived for the cases of large-scale antennas and large-scale users, respectively. Extension to the intelligent reflecting surfaces based scheme is also discussed. Simulation results demonstrate the effectiveness of the proposed successive convex optimization based AAUC scheme, and also validate the low-complexity nature of the proposed large-scale optimization algorithms. [ABSTRACT FROM AUTHOR]

Cloud radio access network (C-RAN) has been recognized as a promising architecture for next-generation wireless systems to support the rapidly increasing demand for higher data rate. However, the performance of C-RAN is limited by the backhaul capacities, especially for the wireless deployment. While C-RAN with fixed BS caching has been demonstrated to reduce backhaul consumption, it is more challenging to further optimize the cache allocation at BSs with multi-cluster multicast backhaul, where the inter-cluster interference induces additional non-convexity to the cache optimization problem. Despite the challenges, we propose an accelerated first-order algorithm, which achieves much higher content downloading sum-rate than a second-order algorithm running for the same amount of time. Simulation results demonstrate that, by simultaneously delivering the required contents to different multicast clusters, the proposed algorithm achieves significantly higher downloading sum-rate than those of time-division single-cluster transmission schemes. Moreover, it is found that the proposed algorithm allocates larger cache sizes to the farther BSs within the nearer clusters, which provides insight to the superiority of the proposed cache allocation. [ABSTRACT FROM AUTHOR]

Coordinated multi-point (CoMP) transmission is a cooperating technique among base stations (BSs) in a cellular network, with outstanding capability at inter-cell interference (ICI) mitigation. ICI is a dominant source of error, and has detrimental effects on system performance if not managed properly. Based on the theory of Poisson-Delaunay triangulation, this paper proposes a novel analytical model for CoMP operation in cellular networks. Unlike the conventional CoMP operation that is dynamic and needs on-line updating occasionally, the proposed approach enables the cooperating BS set of a user equipment (UE) to be fixed and off-line determined according to the location information of BSs. By using the theory of stochastic geometry, the coverage probability and spectral efficiency of a typical UE are analyzed, and simulation results corroborate the effectiveness of the proposed CoMP scheme and the developed performance analysis. [ABSTRACT FROM AUTHOR]

Two-way massive MIMO amplify-and-forward relaying systems with non-ideal transceivers are investigated in this paper. To be general, multiple-antenna nodes and antenna correlation at both the user equipments (UEs) and the relay are considered, which differentiates the analysis from the prior ones. The achievable rate is analyzed and derived deterministically in closed-form. Joint scaling of the transmission powers and hardware impairments is then particularly investigated. Feasible scaling speeds for the transmission powers and hardware impairments are discovered when the number of relay antennas grows large. It is shown that down scaling of the transmission powers at the UEs and the relay and up scaling of the hardware impairment at the relay with the number of relay antennas are tolerable without reducing the expected rate. However, UE hardware impairment is a key limiting factor to the achievable rate and is not allowed to scale up with the number of relay antennas in order to achieve a non-vanishing rate. Moreover, ceiling effect on the achievable rate is still observable and the ceiling rate varies among different scaling cases. More interestingly, scalings of the UEs transmission power and the relay hardware impairment are found to be offsettable, which means that the relay hardware cost and the UE transmission power are tradable. It is found that the best tradeoff is achieved in the medium scalings of both the relay hardware impairment and UE transmission power. Numerical results are provided to verify the analysis and the tradability between the relay hardware cost and the UE transmission power. The analytical results thus provide solid foundation for flexible system designs under various cost and energy constraints. [ABSTRACT FROM AUTHOR]

As the demand for supporting hybrid multicast and unicast services is rapidly increasing, a non-orthogonal multiplexing transmission scheme called layered-division multiplexing (LDM) has been recognized as an effective way to provide high spectrum efficiency (SE). However, high SE is not necessarily equivalent to high energy efficiency (EE). In fact, it is still unclear how much benefit LDM would provide for hybrid multicast and unicast services under EE maximization, which belongs to the more challenging class of fractional programs. To fill this gap, we formulate the problem of energy-efficient precoding design for the LDM-based multi-user multi-input-multi-output downlink system, under both multicast and unicast multi-stream data rate constraints of each user. Although the problem is nonsmooth and nonconvex, we propose a first-order algorithm for finding both the initial point and the final solution. Since the proposed first-order algorithm involves only gradient information, it achieves very low complexity. The simulation results demonstrate that, compared with the orthogonal transmission schemes, the LDM transmission under the proposed precoding can provide a much higher EE. Moreover, the proposed first-order algorithm achieves the same EE as that of a second-order based approach, but requires much shorter computation time. [ABSTRACT FROM AUTHOR]

This letter proposes a novel wirelessly-powered full-duplex amplify-and-forward (AF) relaying scheme, where the multi-antenna relay can harvest energy from the source and recycle energy from itself by using antenna switching protocol. Unlike conventional fixed antennas relay that can only harvest and recycle energy at the second transmission phase, in the proposed scheme, the relay can also harvest energy at the first transmission phase with dynamic antenna combination. To maximize the spectral efficiency of the system, the transmit power of the source, the beamforming matrix at the relay and its antenna allocation are jointly optimized. Also, to tackle the discontinuity and nonconvexity of the optimization problem, a semi-analytical iterative algorithm is developed. In particular, for a fixed antenna combination at the relay, a closed-form solution to the source power allocation and the beamforming matrix at the relay are derived. Then, a greedy antenna switching algorithm is designed to obtain the best antenna combination. Simulation results demonstrate the near optimality of the proposed algorithm and the superiority of the proposed scheme. [ABSTRACT FROM AUTHOR]

Collecting data from the massive Internet of Things (IoT) devices is a challenging task since communication circuits are power-demanding while energy supply at IoT devices is limited. To overcome this challenge, backscatter communication emerges as a promising solution as it eliminates radio frequency components in the IoT devices. Unfortunately, the transmission range of backscatter communication is short. To facilitate backscatter communication, this paper proposes to integrate unmanned ground vehicle (UGV) with backscatter data collection. With such a scheme, the UGV could improve the communication quality by approaching various IoT devices. However, moving also costs energy consumption and a fundamental question is: what is the right balance between spending energy on moving versus on communication? To answer this question, this paper studies energy minimization under a joint graph mobility and backscatter communication model. With the joint model, the mobility management and power allocation problem, unfortunately, involves nonlinear coupling between discrete variables brought by mobility and continuous variables brought by communication. Despite the optimization challenges, an algorithm that theoretically achieves the minimum energy consumption is derived, and it leads to automatic trade-off between spending energy on moving versus on communication in the UGV backscatter system. The simulation results show that if the noise power is small (e.g., ≤−100 dBm), the UGV should collect the data with small movements. However, if the noise power is increased to a larger value (e.g., −60 dBm), the UGV should spend more motion energy to get closer to the IoT users. [ABSTRACT FROM AUTHOR]

Activity detection in machine-type communication (MTC) has been recognized as an effective way to support massive connectivity of the Internet-of-Things (IoT) devices. However, due to the sporadic traffic pattern of the MTC, only a small portion of the massive potential devices are active, making the activity detection a challenging large-scale sparsity-constrained problem. On the other hand, since the low-cost IoT devices are commonly equipped with cheap crystal oscillators, the resulting frequency offsets would intensify the multi-user interference during the activity detection and invalidate existing detection methods that are designed under ideal frequency synchronization. To fill this gap, this paper proposes two methods for activity detection under unknown frequency offsets: a Lasso-based method and a sparsity-constrained method. Both the methods are first-order algorithms, making them suitable for large-scale IoT systems. Furthermore, the sparsity-constrained method can be executed in parallel and is proved to converge to a set of critical points. The simulation results show that both the proposed methods achieve much better detection performance than a two-stage approach that separately performs frequency synchronization and activity detection. Moreover, the proposed sparsity-constrained method is shown to perform better than two competing algorithms exploiting hierarchical sparsity. [ABSTRACT FROM AUTHOR]

Depending on the actual demand of maritime security, this paper analyzes the specific requirements of video encryption algorithm for maritime monitoring system. Based on the technology of Internet of things, the intelligent monitoring system of unmanned surface vessels (USV) is designed and realized, and the security technology and network technology of the Internet of things are adopted. The USV are utilized to monitor and collect information on the sea, which is critical to maritime security. Once the video data were captured by pirates and criminals during the transmission, the security of the sea will be affected awfully. The shortcomings of traditional algorithms are as follows: the encryption degree is not high, computing cost is expensive, and video data is intercepted and captured easily during the transmission process. In order to overcome the disadvantages, a novel encryption algorithm, i.e., the improved Hill encryption algorithm, is proposed to deal with the security problems of the unmanned video monitoring system in this paper. Specifically, the Hill algorithm of classical cryptography is transplanted into image encryption, using an invertible matrix as the key to realize the encryption of image matrix. The improved Hill encryption algorithm combines with the process of video compression and regulates the parameters of the encryption process according to the content of the video image and overcomes the disadvantages that exist in the traditional encryption algorithm and decreases the computation time of the inverse matrix so that the comprehensive performance of the algorithm is optimal with different image information. Experiments results validate the favorable performance of the proposed improved encryption algorithm. [ABSTRACT FROM AUTHOR]

In multimedia-rich communication scenarios, popular contents are requested by many users. This calls for the communication system design perspective transferring from user-centric to content-centric. To realize the content-centric paradigm, one of the dominant approaches is the multi-group multicast transmission. However, different content groups may cause interference with each other, and the quality of service is difficult to be guaranteed without coordination. Fortunately, a cloud radio access network (C-RAN) perfectly fills this gap as all the computations in the network are off-loaded to the computation center, making the central coordination possible. But a major challenge that C-RAN faces is that the resultant problem size could be extremely large, invalidating many existing second-order algorithms. In this paper, content-centric sparse multicast beamforming in a large-scale C-RAN is studied. In addition to the large-scale nature, this problem is further complicated by the discontinuity and non-convexity of the cost function and constraints. Despite the challenges, a first-order algorithm is proposed. Not only is the proposed algorithm guaranteed to converge to a critical point, but its complexity order is only linear with respect to the problem size. This is in sharp contrast to the cubic order of an existing solution, making the proposed algorithm indispensable for large-scale C-RAN with hundreds or thousands of users. [ABSTRACT FROM AUTHOR]

For Poisson-Delaunay triangulations in $d$ -dimensional Euclidean space $\mathbb {R}^{d}$ , a structured and computationally efficient form of the probability density function (PDF) of the volume of a typical cell is analytically derived in this paper. In particular, the ensuing PDF and the corresponding cumulative density function are exact and unified, applicable to spaces of arbitrary dimension ( $d \ge 1$ ). Then, the special cases and shape characteristics of the resulting PDF are thoroughly examined. Finally, various applications of the obtained distribution functions are outlined and, in particular, a novel coordinated multi-point transmission scheme based on Poisson-Delaunay triangulation is developed and the pertinent void cell effect is precisely evaluated by using the obtained distribution functions. [ABSTRACT FROM AUTHOR]

To prolong the lifetime of energy constrained devices in Internet of Things, devices can harvest wireless energy from the control signal multicast from the access point. Unfortunately, hampered by the path-loss, the efficiency of such multicast wirelessly powered network is low. While large-scale antennas at access point can be used to improve the efficiency, the beamforming design problem in multicast wirelessly powered network is known to be NP-hard, and the traditional difference of convex programming becomes prohibitively time consuming in large-scale settings. On the other extreme, by using the assumption of infinite number of antennas and applying the law of large numbers, simple beamforming solution is possible. However, when applied to scenarios with finite number of antennas, the performance of such asymptotic solution is far from that of difference of convex programming. To resolve this apparent complexity-performance dilemma, this paper develops an algorithm which reduces the computation time by orders of magnitude, while still guaranteeing the same performance compared with the difference of convex programming. In particular, the proposed algorithm consists of two fast-convergent iterative procedures and is guaranteed to obtain a Karush–Kuhn–Tucker solution. Furthermore, in each iteration, the algorithm only requires the computation of inner products between channel vectors and can be run in parallel for all the users. Thus, the complexity scales linearly with the number of antennas at access point. Finally, numerical results validate the performance and the speed of the proposed scheme. [ABSTRACT FROM AUTHOR]

In this paper, the transmission optimization of a dual-hop decode-and-forward relaying system is investigated, where the relay capable of energy harvesting from ambient environment can work in hybrid half-duplex (HD) and/or full-duplex (FD) mode. To maximize the throughput from source to destination, the relay’s working mode is optimized under the constraint of random energy arrival. In particular, upon the availability of channel state information (CSI), two cases are sequentially studied: one is that CSI is unavailable to the transmitter and the other means CSI is available to the transmitter. In the former case, a dynamic programming (DP) algorithm is proposed to find the optimal working mode of the relay; moreover, to reduce the computational complexity, a linear programming (LP)-based heuristic algorithm is developed, which performs similar to the DP algorithm. In the latter case, the optimal mode of the relay is also obtainable by the DP algorithm and an approximate DP algorithm is further developed for lower computational complexity. Simulation results demonstrate that the hybrid mode outperforms pure HD and FD modes given that self-interference is efficiently suppressed. [ABSTRACT FROM PUBLISHER]

In this paper, the performance of an underlay multiple-input multiple-output (MIMO) cognitive radio system is analytically studied. In particular, the secondary transmitter operates in a spatial multiplexing transmission mode, while a zero-forcing detector is employed at the secondary receiver. Additionally, the secondary system is interfered by multiple randomly distributed single-antenna primary users (PUs). To enhance the performance of secondary transmission, optimal power allocation is performed at the secondary transmitter with a constraint on the interference temperature (IT) specified by the PUs. The outage probability of the secondary receiver is explicitly derived in an exact closed-form expression. Also, some special cases of practical interest, including colocated PUs and massive MIMO, are discussed. Further, to mitigate instantaneous excessive interference onto PUs caused by the time-average IT, an iterative antenna reduction algorithm is developed for the secondary transmitter and, accordingly, the average number of transmit antennas is analytically computed. Extensive numerical and simulation results corroborate the effectiveness of our analysis. [ABSTRACT FROM PUBLISHER]

In simultaneous wireless information and power transfer systems, optimization of transmit signals is critical to system performance. Although the optimization problem can be efficiently solved under a linear energy harvesting model, the obtained solution may not work well in practice, since the energy harvester contains nonlinear elements, such as diodes. On the other hand, while a nonlinear model can be used to capture the dynamics of energy harvesting circuits, it introduces additional complexity at the optimization stage. Specifically, under a nonlinear model, traditional convex optimization is not applicable, since the energy harvesting function is fractional. To address this challenge, this letter first derives an optimal solution for static channels by introducing pseudo-inverse of the nonlinear model. Then, an iterative algorithm that converges to a sub-optimal solution is proposed for time varying channels. With the developed methods, the performance-complexity tradeoff between linear and nonlinear models is illustrated. [ABSTRACT FROM PUBLISHER]

While two-way communication can improve the spectral efficiency of wireless networks, distances from the relay to the two users are usually asymmetric, leading to excessive wireless energy at the nearby user. To exploit the excessive energy, energy harvesting at user terminals is a viable option. Unfortunately, the exact gain brought by wireless power transfer (WPT) in two-way communication is currently unknown. To fill this gap, in this paper, the achievable rate region of wirelessly powered two-way communication with a fixed relay is derived. Not only this newly established result is shown to enclose the existing achievable rate region of two-way relay channel without energy harvesting but also the gain is precisely quantified. On the other hand, it is well-known that a major obstacle to WPT is the path-loss. By endowing the relay with mobility, the distances between the relay and users can be varied, thus providing a potential solution to combat pathloss at the expense of energy for transmission. To characterize the consequence brought by such a scheme, a pair of inner and outer bounds to the achievable rate region of wirelessly powered two-way communication under a mobile relay is further derived. By comparing the exact achievable rate region for the fixed relay case and the achievable rate bounds for the mobile relay case, it is possible to quantify the relative advantage of spending energy on moving versus on transmission in wirelessly powered two-way communication. [ABSTRACT FROM PUBLISHER]

In this paper, outage performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) is analyzed. Unlike prior analyses, time-correlated Nakagami- $m$ fading channel is considered. The outage analysis thus involves the probability distribution analysis of a product of multiple correlated shifted Gamma random variables and is more challenging than prior analyses. Based on the findings of the conditional independence of the received signal-to-noise ratios, the outage probability is exactly derived by using conditional Mellin transform. Specifically, the outage probability of HARQ-IR under time-correlated Nakagami- $m$ fading channels can be written as a weighted sum of outage probabilities of HARQ-IR over independent Nakagami fading channels, where the weightings are determined by a negative multinomial distribution. This result enables not only an efficient truncation approximation of the outage probability with uniform convergence but also asymptotic outage analysis to further extract clear insights, which have never been discovered for HARQ-IR even under fast fading channels. The asymptotic outage probability is then derived in a simple form, which clearly quantifies the impacts of transmit powers, channel time correlation, and information transmission rate. It is proved that the asymptotic outage probability is an inverse power function of the product of transmission powers in all HARQ rounds, an increasing function of the channel time correlation coefficients, and a monotonically increasing and convex function of information transmission rate. The simple expression of the asymptotic result enables optimal power allocation and optimal rate selection of HARQ-IR with low complexity. Finally, numerical results are provided to verify our analytical results and justify the application of the asymptotic result for optimal system design. [ABSTRACT FROM PUBLISHER]

This paper provides a generalized analysis for the spectral efficiency of both regular- and large-scale (massive) multiple-input multiple-output (MIMO) systems, where major radio-propagation characteristics and antenna-array parameters are taken into account, including path loss, shadowing effect, multipath fading, antenna correlation, antenna polarization, environmental cross-polarization coupling, and antenna cross-polarization discrimination. After developing the channel model, where the Weichselberger method is exploited to reformulate the Kronecker model and allow closed-form analysis, an upper bound on the spectral efficiency of the MIMO channel is first established, by using the Hadamard's determinant inequality for positive semidefinite matrices. Then, an asymptotic analysis in high signal-to-noise ratio regime is conducted, which explicitly reveals the effects of the aforementioned parameters on the spectral efficiency. Moreover, the asymptotic behavior of the spectral efficiency in the sense of massive MIMO, i.e., as the number of transmit and/or receive antennas approaches infinity, is investigated. Monte Carlo simulation results corroborate the effectiveness of the analysis and the accuracy of the obtained upper bound on the spectral efficiency. Thanks to its high generality, the analysis developed in this paper benefits system designers in the design and performance evaluation of either regular or massive MIMO systems while accounting for realistic phenomena characterizing both the propagation environment and the antenna arrays. [ABSTRACT FROM AUTHOR]

A novel multi-hop cooperative relaying transmission is proposed, where neither the source nor the relays have constant power supplies but rather scavenge energy from cochannel interferences (CCIs) and then use it for subsequent data transmission. The results show that, larger CCIs, usually detrimental to system performance, offer higher transmit power to each node and benefit improving system performance. Given a prescribed end-to-end (e2e) outage threshold, this letter identifies the largest number of hops that energy harvesting from CCIs can support. Moreover, the e2e outage probability is shown to be dominated by the quality of the desired channels along with the relaying link, almost regardless of the CCIs. [ABSTRACT FROM PUBLISHER]

While two-way relaying is a promising way to enhance the spectral efficiency of wireless networks, the imbalance of relay-user distances may lead to excessive wireless power at the nearby users. To exploit the excessive power, the recently proposed harvest-then-transmit technique can be applied. However, it is well known that harvest-then-transmit introduces uplink-downlink coupling for a user. Together with the codependent relationship between paired users and interference among multiple user pairs, wirelessly powered two-way relay network suffers from the unique pairwise uplink-downlink coupling, and the joint uplink-downlink network design is nontrivial. To this end, for the one pair users case, we show that a global optimal solution can be obtained. For the general case of multipair users, based on the rank-constrained difference of convex program, a convergence guaranteed iterative algorithm with an efficient initialization is proposed. Furthermore, a lower bound to the performance of the optimal solution is derived by introducing virtual receivers at relay. Numerical results on total transmit power show that the proposed algorithm achieves a transmit power value close to the lower bound. [ABSTRACT FROM PUBLISHER]

In this paper, spectrum-sharing technology is integrated into cellular systems to improve spectrum efficiency. Macrocell users are primary users (PUs), whereas those within local cells, e.g., femtocell users, or desiring cost-effective services, e.g., roamers, are identified as secondary users (SUs). The SUs share the spectrum resources of the PUs in an underlay way; thus, the transmit power of a secondary is strictly limited by the primary's tolerable interference power. Given such constraints, a cooperative relaying transmission between an SU and the macrocell base station (BS) is necessary. To guarantee the success of dual-hop relaying and avoid multihop relaying, a new cooperative paradigm is proposed, where an idle PU (instead of a secondary, as assumed in general) in the vicinity of a target SU is chosen to serve as a relaying node, due to the fact that any PU can always transmit to the macrocell BS directly. Moreover, a two-way relaying strategy is applied at the chosen relaying node to further improve spectral efficiency. Our results demonstrate that the proposed system is particularly suitable for delay-tolerant wireless services with asymmetric downlink/uplink traffic, such as e-mail checking, web browsing, social networking, and data streaming, which are the most popular applications for SUs in spectrum-sharing cellular networks. [ABSTRACT FROM PUBLISHER]

This paper analyzes the spectral efficiency of massive multiple-input multiple-output (MIMO) systems in both centralized and distributed configurations, referred to as C-MIMO and D-MIMO, respectively. By accounting for real environmental parameters and antenna characteristics, namely, path loss, shadowing effect, multipath fading, and antenna correlation, a novel comprehensive channel model is first proposed in closed-form, which is applicable to both types of MIMO schemes. Then, based on the proposed model, the asymptotic behavior of the spectral efficiency of the MIMO channel, under both the centralized and distributed configurations is analyzed and compared in exact forms, by exploiting the theory of very long random vectors. Afterwards, a case study is performed by applying the obtained results into MIMO networks with circular coverage. In such a case, it is attested that for the D-MIMO of cell radius r\text{c} and circular antenna array of radius r\text{a}, the optimal value of r\text{a} that maximizes the average spectral efficiency is accurately established by r\text{a}^{\text{opt}}=r\text{c}/1.31. Monte Carlo simulation results corroborate the developed spectral-efficiency analysis. [ABSTRACT FROM PUBLISHER]

Two major relay-selection strategies widely applied in cooperative decode-and-forward (DF) relaying networks, namely, reactive relay selection (RRS) and proactive relay selection (PRS), are generally looked upon as independent and studied separately. In this paper, RRS and PRS are proven to be equivalent with respect to the end-to-end outage probability from the first principle, i.e., their respective relay-selection criteria. On the other hand, RRS is shown to be superior to PRS with respect to the end-to-end symbol error rate. Afterwards, a case study of a general DF relaying system, subject to co-channel interferences and additive white Gaussian noise at both the relaying nodes and the destination, is performed to explicitly illustrate the aforementioned outage equivalence. These fundamental relations provide intuitive yet insightful performance benchmarks for comparing various applications of these two relay-selection strategies. [ABSTRACT FROM PUBLISHER]

Far-field wireless power transfer (WPT) is a promising technique to resolve the painstaking power-charging problem inherent in various wireless terminals. This paper investigates the power transfer efficiency of the WPT segment in future communication systems in support of simultaneous power and data transfer, by means of analytically computing the time-average output direct current (DC) power at user equipments (UEs). In order to investigate the effect of channel variety among UEs on the average output DC power, different policies for the scheduling of the power transfer among the users are implemented and compared in two scenarios: homogeneous, whereby users are symmetric and experience similar path loss, and heterogeneous, whereby users are asymmetric and exhibit different path losses. Specifically, if opportunistic scheduling is performed among N symmetric/asymmetric UEs, the power scaling laws are attained by using extreme value theory and reveal that the gain in power transfer efficiency is \lnN if UEs are symmetric whereas the gain is N if UEs are asymmetric, compared with that of conventional round-robin scheduling. Thus, the channel variety among UEs inherent to the wireless environment can be exploited by opportunistic scheduling to significantly improve the power transfer efficiency when designing future wireless communication systems in support of simultaneous power and data transfer. [ABSTRACT FROM PUBLISHER]