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In this paper, we present a practical application of dirty paper coding (DPC) for the Gaussian cognitive Z-interference channel. A two stage transmission scheme is proposed in which the cognitive transmitter first obtains the interference signal from the primary transmitter and then uses DPC to improve the performance of the cognitive link. Numerical results show that causal knowledge of the interference provides more than 3 dB improvement in performance in certain scenarios over a scheme that does not use interference cancellation. Results are also shown when the cognitive transmitter operates in both half-duplex and full-duplex modes.

The authors of [1] have become aware that several equations in [1] were presented with errors. As confirmed in this brief note, neither do the errors invalidate the game theoretic approach proposed in [1] , nor violate the equilibrium of the approach. However, the parameter selection, which is required to preserve the validity and unique equilibrium of the approach, is affected by the errors. We correct the equations with detailed mathematical derivations in the note.

International audience; The huge potential of ultra wide band impulse radio (UWB IR) with multiple access was initially evaluated using the standard Gaussian approximation which assumed that multi user interference could be approximated as classical Gaussian noise. Later the standard Gaussian approximation has been proved to be invalid in several cases. Thus the performance of the classical UWB IR correlation receiver dramatically reduces. We show in this paper how the non Gaussianity of the multi user interference can be exploited, turning the non Gaussianity into an advantage, increasing the performance with respect to the Gaussian interference case which was supposed to be too optimistic until now.

Direct-sequence code-division multiple-access (DSCDMA) cellular systems, such as wideband CDMA (WCDMA), are limited in performance by interference. Linear equalization, such as generalized Rake (G-Rake) receivers and transversal chip equalizers, can theoretically provide significant gains in performance by suppressing interference. In this paper, an adaptive G-Rake receiver is developed, employing practical algorithms for finger placement and weight computation. Finger placement is determined by selecting delays from a candidate set using a "maximal-weight" criterion. Weight computation includes estimation of an impairment covariance matrix using a parametric approach. While described in the context of the downlink, these algorithms can be used in the uplink as well. Both single-antenna and dual-antenna receivers are considered. Link and system performance are evaluated for the downlink, showing significant gains in high-rate coverage.

We consider a fading version of the dirty-paper problem, as proposed by Grover and Sahai. In this formulation, the various signals involved are complex-valued, and the interference (known only to the transmitter) is multiplied by a random complex-valued coefficient, whose phase is known only to the receiver. We focus on a compound channel formulation, and seek to maximize the worst-case performance. We present an achievable strategy modeled on the lattice-based approach of Erez, Shamai and Zamir and propose heuristic methods to optimize its parameters. We also derive an upper bound on the maximum achievable transmission rates. Our bounds are shown to be tight in some settings, yielding a complete characterization of capacity. We also provide simulation results, indicating the practical effectiveness of our approaches.

Previous research on sensor network security mainly considers homogeneous sensor networks, where all sensor nodes have the same capabilities. Research has shown that homogeneous ad hoc networks have poor performance and scalability. The many-to-one traffic pattern dominates in sensor networks, and hence a sensor may only communicate with a small portion of its neighbors. Key management is a fundamental security operation. Most existing key management schemes try to establish shared keys for all pairs of neighbor sensors, no matter whether these nodes communicate with each other or not, and this causes large overhead. In this paper, we adopt a Heterogeneous Sensor Network (HSN) model for better performance and security. We propose a novel routing-driven key management scheme, which only establishes shared keys for neighbor sensors that communicate with each other. We utilize Elliptic Curve Cryptography in the design of an efficient key management scheme for sensor nodes. The performance evaluation and security analysis show that our key management scheme can provide better security with significant reductions on communication overhead, storage space and energy consumption than other key management schemes.

We propose and analyze in this paper two new adaptive power controlled minimum selection generalized selection combining (MS-GSC) diversity schemes. The key idea behind these two schemes is to request the transmitter to vary its power level during very adverse channel conditions in order to communicate with the minimum required quality of service. For each scheme, four power control variants accounting for practical implementation constraints including discrete power levels and transmitter gain saturation are proposed and studied. Selected numerical examples, show that ideal continuous power control lowers the average bit error rate of MS-GSC and makes it always meet the specified target quality of service at the expense of an increase in the transmitter gain. Additional numerical examples, show that the power control variants that take into account practical implementation constraints conserve the main features of the ideal continuous power algorithm.

Device placement is a fundamental factor in determining the coverage, connectivity, cost and lifetime of a wireless sensor network (WSN). In this paper, we explore the problem of relay node placement in heterogeneous WSN. We formulate a generalized node placement optimization problem aimed at minimizing the network cost with constraints on lifetime and connectivity. Depending on the constraints, two representative scenarios of this problem are described. We characterize the first problem, where relay nodes are not energy constrained, as a minimum set covering problem. We further consider a more challenging scenario, where all nodes are energy limited. As an optimal solution to this problem is difficult to obtain, a two-phase approach is proposed, in which locally optimal design decisions are taken. The placement of the first phase relay nodes (FPRN), which are directly connected to sensor nodes (SN), is modeled as a minimum set covering problem. To ensure the relaying of the traffic from the FPRN to the base station, three heuristic schemes are proposed to place the second phase relay nodes (SPRN). Furthermore, a lower bound on the minimum number of SPRN required for connectivity is provided. The efficiency of our proposals is investigated by numerical examples.

A l-Bayesian erasure test (BET) based on order statistics with diversity reception for noncoherent M-ary frequency shift keying (FSK) signaling is examined. In the l-BET, the M-ary FSK symbol is erased whenever the a posteriori probability of correct decision, which is a function of the l-largest sums of energy detector outputs, is less than a threshold. For binary FSK signaling in Rayleigh flat fading channels, the 2-BET is found to be the same as the difference threshold test (DTT), which declares an erasure whenever the difference between the largest and the second largest sums of energy detector outputs is less than a threshold. The output threshold test (OTT), which declares an erasure whenever the largest sum of energy detector outputs is less than a threshold, is proved equivalent to the 1-BET for the Rayleigh flat fading channel. Closed-form expressions for the probabilities of correct decision and symbol error for the DTT, Viterbi's ratio threshold test (RTT), and the OTT are derived when square-law combining (SLC) diversity is used. Several erasure tests including the Z-BET, the RTT, the OTT, and the DTT are compared in terms of probability of codeword error. The 2-BET provides almost the same performance as the BET that requires all the sums of energy detector outputs. The 2-BET provides power gains of 0.9 dB and 0.7 dB over the RTT for (7, 3) and (15, 7) Reed-Solomon (RS) codes, respectively, when the probability of codeword error is 10-3 and the diversity order is one.

We consider the problem of cross-layer resource allocation for wireless networks operating with rechargeable batteries under general arrival, channel state and recharge processes. The objective is to maximize total system utility, defined as a function of the long-term rate achieved per link, while satisfying energy and power constraints. A policy with decoupled admission control and power allocation decisions is proposed that achieves asymptotic optimality for sufficiently large battery capacity to maximum transmission power ratio (explicit bounds are provided). We present first a downlink resource allocation scenario; the analysis is then extended to multihop networks. The policy is evaluated via simulations and is seen to perform very well even in the non-asymptotic regime. This policy is particularly suitable for sensor networks, which typically satisfy the asymptotic conditions required by our methodology.

In this paper, we characterize the cross-correlation function (CCF) between the time-frequency transfer functions (TF)s of two sub-channels of a multiple-input multiple-output (MIMO) wireless fading channel. The TF of each sub-channel consists of a number of multi-path components caused by propagation of a transmitted signal in random scattering media. The proposed CCF is expressed in terms of several environmental parameters (such as the moment generating function (MGF) of the delay profile (DP) and the pathloss exponent). It is a summation of two terms: the first term is due to the autocorrelation of multi-path components while the second term is due to the cross-correlation of multi-path components. Each term is a product of several correlation functions. Each of these correlation functions represents different dependencies of the wireless channel in terms of time, carrier frequency and the position of the antenna elements around both the transmitter and the receiver site. Interestingly, the last two terms of these functions are (n/2)-order (or n-order) integrations of the MGF of the DP, evaluated at two carrier frequencies (or the difference between carrier frequencies), where n is the pathloss exponent of the environment

The authors of the above titled paper (ibid., vol. 9, no. 8, pp. 2664-2675, Aug. 2010), have become aware that several of the equations in that paper were presented with errors. In this brief, we provide correct equations. The implementation of the model in the original paper was in accordance with the correct equations, so the implementation results presented in Section V. Performance Analysis are still valid.

In this paper, we revisit the fundamental problem of computing MIMO capacity under per-antenna power constraint (PAPC). Unlike the sum power constraint counterpart which likely admits water-filling-like solutions, MIMO capacity with PAPC has been largely studied under the framework of generic convex optimization. The two main shortcomings of these approaches are (i) their complexity scales quickly with the problem size, which is not appealing for large-scale antenna systems, and/or (ii) their convergence properties are sensitive to the problem data. As a starting point, we first consider a single user MIMO scenario and propose two provably-convergent iterative algorithms to find its capacity, the first method based on fixed-point iteration and the other based on alternating optimization and minimax duality. In particular, the two proposed methods can leverage the water-filling algorithm in each iteration and converge faster, compared to current methods. We then extend the proposed solutions to multi-user MIMO systems with dirty paper coding (DPC) based transmission strategies. In this regard, capacity regions of Gaussian broadcast channels with PAPC are also computed using closed-form expressions. Numerical results are provided to demonstrate the outperformance of the proposed solutions over existing approaches. European Commission - European Regional Development Fund Science Foundation Ireland

A simple diamond half-duplex relay network composed of a source, two decode-and-forward half-duplex relays, and a destination is considered, where a direct link between the source and the destination does not exist. For this network, we study the case of buffer-aided relays, where the relays are equipped with buffers. Each relay can receive data from the source, store it in the buffer, and forward it to the destination, when the channel conditions are advantageous. Thereby, buffering enables adaptive scheduling of the transmissions and receptions over time, which allows the network to exploit the diversity offered by the fading channels. For the considered half-duplex network, four transmission modes are defined based on whether the relay nodes receive or transmit. In this paper, we derive the locally optimal scheduling of the transmission modes over time and investigate the achievable average rate, when the relays are affected by inter-relay interference. Since the proposed buffer-aided transmission policies introduce unbounded delay, we provide a sub-optimal buffer-aided transmission policy with limited delay. Moreover, for inter-relay interference cancellation, we consider two coding schemes with different complexities. In the first scheme, we employ dirty paper coding, which entails a high complexity, whereas in the second scheme, we adopt a low-complexity technique based on successive interference cancellation at the receiving relay nodes and optimal power allocation at the transmitting nodes. Our numerical results show that the proposed protocols, with and without delay constraints, outperform existing protocols for the considered network from the literature.

The objective of this paper is to study the impact of advanced physical layer techniques on the maximum achievable throughput of wireless multihop mesh networks. We formulate a cross-layer optimization framework for the routing and scheduling problem jointly with the following physical layer techniques: successive interference cancellation, superposition coding, dirty-paper coding and their combinations. In the case when each node is enabled with superposition coding, we need to formulate a power allocation subproblem for the optimal power partition of the superimposed signals. We solve these joint problems exactly to compute the maximum achievable throughput in realistic size networks. This allows us to quantify the performance gains obtained by using these techniques (and their combinations). Specifically, we find that the use of dirty-paper coding (only at the gateway) is not justified in networks with mixed uplink and downlink flows. On the other hand, the combination of superposition coding with successive interference outperforms significantly other techniques across all transmission power range for both uplink and downlink flows. We also provide a number of interesting practical insights on throughput improvement by comparing different combinations of these techniques.

In a conventional wireless cellular system, signal processing is performed on a per-cell basis; out-of-cell interference is treated as background noise. This paper considers the benefit of coordinating base-stations across multiple cells in a multi-antenna beamforming system, where multiple base-stations may jointly optimize their respective beamformers to improve the overall system performance. Consider a multicell downlink scenario where base-stations are equipped with multiple transmit antennas employing either linear beamforming or nonlinear dirty-paper coding, and where remote users are equipped with a single antenna each, but where multiple remote users may be active simultaneously in each cell. This paper focuses on the design criteria of minimizing either the total weighted transmitted power or the maximum per-antenna power across the base-stations subject to signal-to-interference-and-noise-ratio (SINR) constraints at the remote users. The main contribution of the paper is an efficient algorithm for finding the joint globally optimal beamformers across all base-stations. The proposed algorithm is based on a generalization of uplink-downlink duality to the multicell setting using the Lagrangian duality theory. An important feature is that it naturally leads to a distributed implementation in time-division duplex (TDD) systems. Simulation results suggest that coordinating the beamforming vectors alone already provide appreciable performance improvements as compared to the conventional per-cell optimized network.

This paper deals with protocol design for cognitive cooperative systems with many secondary users. In contrast with previous cognitive configurations, the channel model considered assumes a cluster of secondary users which perform both a sensing process for transmitting opportunities and can relay data for the primary user. Appropriate relaying improves the throughput of the primary users and can increase the transmission opportunities for the cognitive users. Based on different multi-access protocols, the schemes investigated enable relaying either between the primary user and a selected secondary user or between two selected secondary users. This collaboration can be a simple distributed multiple-input single-output transmission of the primary data or a simultaneous transmission of primary and secondary data using dirty-paper coding (DPC). The parametrization of DPC as well as its combination with opportunistic relay selection yields an interesting trade-off between the primary and the secondary performance which is investigated by theoretical and simulation results under the perspective of a desired primary throughput. The proposed protocols are studied from a networking point of view and the stable throughput for primary and secondary users is derived based on the principles of queueing theory.

The cognitive radio paradigm is based on the ability of sensing the radio environment in order to make informed decisions. This paper describes the effects of sensing on the cognitive radio channels capacity region. Sensing is modeled as a compression channel, which results in partial knowledge of the primary messages at the cognitive transmitter. This model enables to impose constraints on the sensing strategy. First, the dirty paper channel capacity is derived when the channel encoder knows partially the side information. Then, the capacity area of the Gaussian cognitive channel with partial information is derived. Finally, numerical results illustrate the capacity reduction associated with constrained sensing, in comparison to the capacity of the cognitive radio channel.

The capacity and robustness of cellular MIMO systems is very sensitive to other-cell interference which will in practice necessitate network level interference reduction strategies. As an alternative to traditional static frequency reuse patterns, this paper investigates intercell scheduling among neighboring base stations. We show analytically that cooperatively scheduled transmission, which is well within the capability of present systems, can achieve an expanded multiuser diversity gain in terms of ergodic capacity as well as almost the same amount of interference reduction as conventional frequency reuse. This capacity gain over conventional frequency reuse is O (M t square-root of log Ns) for dirty paper coding and O (min (Mr, Mt) square-root of log Ns) for time division, where Ns is the number of cooperating base stations employing opportunistic scheduling in an M t x M r MIMO system. From a theoretical standpoint, an interesting aspect of this analysis comes from an altered view of multiuser diversity in the context of a multi-cell system. Previously, multiuser diversity capacity gain has been known to grow as O(log log K), from selecting the maximum of K exponentially-distributed powers. Because multicell considerations such as the positions of the users, lognormal shadowing, and pathless affect the multiuser diversity gain, we find instead that the gain is O(square-root of 2logic K), from selecting the maximum of a compound Iognormal-exponential distribution. Finding the maximum of such a distribution is an additional contribution of the paper.

The sum capacity of a Gaussian broadcast MIMO channel can be achieved with dirty paper coding (DPC). However, algorithms that approach the DPC sum capacity do not appear viable in the forseeable future, which motivates lower complexity interference suppression techniques. Block diagonalization (BD) is a linear preceding technique for downlink multiuser MIMO systems. With perfect channel knowledge at the transmitter, BD can eliminate other users' interference at each receiver. In this paper, we study the sum capacity of BD with and without receive antenna selection. We analytically compare BD without receive antenna selection to DPC for a set of given channels. It is shown that (1) if the user channels are orthogonal to each other, then BD achieves the same sum capacity as DPC; (2) if the user channels lie in the same subspace, then the gain of DPC over BD can be upper bounded by the minimum of the number of transmit and receive antennas. These observations also hold for BD with receive antenna selection. Further, we study the ergodic sum capacity of BD with and without receive antenna selection in a Rayleigh fading channel. Simulations show that BD can achieve a significant part of the total throughput of DPC. An upper bound on the ergodic sum capacity gain of DPC over BD is proposed for easy estimation of the gap between the sum capacity of DPC and BD without receive antenna selection.

We consider the downlink of a wireless cellular network where the base stations are equipped with multiple antennas and operate in the same frequency band. Since scheduling changes the spatial transmit signal processing with each time slot, information from neighboring base stations is required for data encoding. This can, in theory, be accomplished by a high-capacity backhaul network through which the base stations exchange channel state information (CSI) and other control signals. In reality, however, the temporal granularity of the scheduler does not allow for timely distribution of CSI among base stations. We propose a two-phase scheduler which optimizes the precoding in the first phase and allows the users to feed back their instantaneous interference power in the second phase. For the single-user case, we present a practical scheme that combines two-phase scheduling with precoders that maximize the signal-to-leakage-plus-noise ratio. If the users feed back the interference power together with a supported rate, communication between base stations can be limited to integers. By comparing the performance to multi-user two-phase scheduling with dirty paper coding and to algorithms that share CSI among base stations we show that two-phase scheduling is a technically and practically feasible solution to deal with non-stationary intercell interference.

This paper proposes a new transmission policy for intelligent reflecting surface (IRS) empowered wireless powered internet of things systems. Particularly, an energy station (ES) wirelessly charges for multiple IoT devices during downlink wireless energy transfer (WET) and then these devices deliver their own message to an access point (AP) during uplink wireless information transfer (WIT). Also, an IRS is deployed to improve energy harvesting and data transmission capabilities. To enhance self-sustainability of the IRS, the IRS harvests energy from the ES based on the harvest-then-transmit protocol. In this paper, we maximize the sum throughput via optimizing the phase shifts of the IRS, the transfer time scheduling as well as the power splitting ratio. Due to the non-convexity of the formulated problem, we divide the problem into two sub-problems, each of which can be handled separately. Then, we adopt an alternating optimization (AO) algorithm with the semidefinite programming (SDP) relaxation. Also, we consider a special case where the circuit power consumption of IoT devices can be neglected. In this case, we derive a closed form solution for the optimal transmission time slots, power allocation and phase shift by the Lagrange dual method. Finally, numerical evaluations validate effectiveness of the proposed scheme, which significantly benefits from the IRS in improving network throughput.

This paper investigates a joint resource allocation for ultra-reliable and low-latency radio access networks (URLLRANs) with edge computing. Compared with conventional networks, URLLRANs have more restrictive latency and reliability requirements, and always feature short packet communications. It is a challenging work to provide edge computing services in URLLRANs, since the processing and transmission delay as well as packet loss during computation and communications should all be taken into considerations. Along these lines, to specify the trade-off between latency and reliability, this paper defines computation rates and transmission rates for short packets. Different from the existing work, the proposal takes effective information as well as energy consumption as performance metrics based on the definition. The packet request rates, computation latency, service rates, communication power, blocklength, and transmission information amounts are jointly optimized to reduce energy consumption and meanwhile generate more effective information for both the computation system and the communication system. To solve the NP-hard problem, the locally optimal solution and global optimal solution are both derived. Simulation results validate the performance advantage of the proposal and also indicate that the locally optimal solution can greatly reduce the computation complexity with only a small performance loss when compared with the global optimal solution.

In this paper, the problem of Quality-of-Service (QoS) differentiation is studied in the context of Buffer-Aided (BA) cooperative Free Space Optical (FSO) communication systems. This is particularly true since the existing relevant literature overlooked the possibility of preferential treatment of data packets based on their delay requirements. Inspired by this observation, this paper proposes to classify the data packets emanating from the source node into either Delay Tolerant (DT) packets or Non Delay Tolerant (NDT) ones and to service these packets according to this classification at the relay node. Priority queueing is first introduced in the relay’s buffer with a view to improving the delay experienced by NDT packets. Class of service mutation is then proposed as a starvation mitigation strategy to better manage the interesting dynamics resulting from the co-existence of packets having different QoS requirements in the same buffer. The various performance measures of interest for the explored QoS-aware communication system are both evaluated mathematically based on a Markov chain analysis and validated through extensive simulations. An asymptotic analysis is also carried out highlighting the dependence of the performance on the system parameters in an intuitive manner.

International audience; Massive machine-type communications (mMTC) is one of the main services delivered by the fifth Generation (5G) mobile network. The traditional cellular architecture where all devices connect to the base station is not energy efficient. For this reason, the use of device-to-device (D2D) communications is considered to reduce the energy consumption of mMTC devices. The main idea is to use nearby user equipment (UE) as a relay and establish with it D2D communication. However, the relay selection process also consumes energy, and this consumption can be significant compared to the energy consumed during the data transmission phase. In this paper, we propose a distributed energy-efficient D2D relaying mechanism for mMTC applications. This mechanism favors the selection of the UEs with low path loss with the mMTC device. Through mathematical analysis and simulations, we show that our mechanism allows a reduction of the total energy consumption of mMTC devices (up to 75% compared to direct transmission) when they have an unfavorable link budget. Moreover, our mechanism achieves almost constant energy consumption for a large range of UE densities and distances between the mMTC device and the base station.

In this paper, we focus on wireless sensor network-based localization for user devices (UDs). Prior to UD localization, signal propagation model (SPM) is required to calibrate using training samples from a number of location grids. However, SPM usually suffers from measurement noise and inevitable error in calibration grid (CG) locations. This will significantly degrade UD localization performance. Nevertheless, the impact of CG location error, CG layout and the number of CGs on SPM calibration performance has not been characterized. Furthermore, the effect of SPM calibration error on UD localization performance has not been developed. In this paper, we aim to provide a unified framework for performance analysis of SPM calibration and UD localization. Firstly, we establish a closed-form Cramer-Rao lower bound on SPM calibration error and UD localization error, respectively. Secondly, the impact of measurement noise, CG location error and the number of CGs on SPM calibration performance is revealed. Thirdly, the influence of SPM calibration error, CG location error and measurement noise on UD localization performance is studied. The effect of modeling mismatch is also studied. The obtained analysis framework builds a theoretical basis for the design of efficient system optimization strategies, including resource allocation and CG deployment optimization, for UD localization performance enhancement.

Wireless caching networks have been extensively researched as a promising technique for supporting the massive data traffic of multimedia services. Many of the existing studies on real-data traffic have shown that users of a multimedia service consecutively request multiple contents and this sequence is strongly dependent on the related list of the first content and/or the top referrer in the category. This paper thus introduces the notion of “temporary preference”, characterizing the behavior of users who are highly likely to request the next content from a certain target category (i.e., related content list). Based on this observation, this paper proposes both probabilistic caching and dynamic delivery policies for categorized contents and consecutive user demands. The proposed caching scheme maximizes the minimum of the cache hit rates for all users. In the delivery phase, a dynamic helper association policy for receiving multiple contents in a row is designed to reduce the delivery latency. By comparing with the content placement optimized for one-shot requests, numerical results verify the effects of categorized contents and consecutive user demands on the proposed caching and delivery policies.

Over the past decades, multiple antenna technologies have appeared in many different forms, most notably as multiple-input multiple-output (MIMO), to transform wireless communications for extraordinary diversity and multiplexing gains. The variety of technologies has been based on placing a number of antennas at fixed locations which dictates the fundamental limit on the achievable performance. By contrast, this paper envisages the scenario where the physical position of an antenna can be switched freely to one of the N positions over a fixed-length line space to pick up the strongest signal in the manner of traditional selection combining. We refer to this system as a fluid antenna system (FAS) for tremendous flexibility in its possible shape and position. The aim of this paper is to study the achievable performance of a single-antenna FAS system with a fixed length and N in arbitrarily correlated Rayleigh fading channels. Our contributions include exact and approximate closed-form expressions for the outage probability of FAS. We also derive an upper bound for the outage probability, from which it is shown that a single-antenna FAS given any arbitrarily small space can outperform an L-antenna maximum ratio combining (MRC) system if N is large enough. Our analysis also reveals the minimum required size of the FAS, and how large N is considered enough for the FAS to surpass MRC., 26 pages, 5 figures

Despite the promising high-data rate features of visible light communications (VLC), they still suffer from unbalanced services due to blockages and channel fluctuation among users. This paper introduces and evaluates a new transmission scheme which adopts cooperative non-orthogonal multiple access (Co-NOMA) in hybrid VLC/radio-frequency (RF) systems, so as to improve both system sum-rate and fairness. Consider a network consisting of one VLC access point (AP) and multiple strong and weak users, where each weak user is paired with a strong user. Each weak user can be served either directly by the VLC AP, or via the strong user which converts light information received through the VLC link, and forwards the information to the weak user via the RF link. The paper then maximizes a network-wide weighted sum-rate, so as to jointly determine the strong-weak user-pairs, the serving link of each weak user (i.e., either direct VLC or hybrid VLC/RF), and the power of each user message, subject to user connectivity and transmit power constraints. The paper tackles such a mixed-integer non-convex optimization problem using an iterative approach. Simulations show that the proposed scheme significantly improves the VLC network performance (i.e., sum-rate and fairness) as compared to the conventional NOMA scheme.

Vehicle-to-vehicle (V2V) wireless communications have many envisioned applications for ensuring traffic safety and for addressing traffic congestion. However, developing suitable communication systems and standards for this purpose requires developers to have accurate models for the V2V propagation channel. Likewise, the dynamic evolution of multipath components (MPCs) in V2V channels has not been well modeled in existing models. In this paper, we propose a geometry-based stochastic channel model for a lightly built-up urban environment and then parametrize the model from measurements. The MPCs are extracted based on a high-resolution parameter estimation; they are tracked and clustered through a joint algorithm. The identified clusters are classified as line-of-sight, reflections from static scatterers, reflections from mobile scatterers, multiple-bounce reflections, and diffuse scattering. Specifically, the multiple-bounce reflections are modeled as twin clusters that follow the COST 273/COST2100 approach. The paper gives a full parameterization of the channel model and supplies a step-by-step implementation recipe. We verify the model by comparing two second-order statistics, i.e., the root-mean-square (RMS) delay spread and the angular spreads of arrival/departure derived from the channel model, to the results obtained directly from the measurements. Furthermore, we also identify several key factors that strongly impact the synthetic channel performance.

An artificial noise (AN)-aided beamformer specific to correlated main and wiretap channels is designed in this paper. We consider slow-fading multiple-input-single-output wiretap channels with multiple passive single-antenna eavesdroppers in which an independent transmitter side and correlated receiver side are assumed. Additionally, the source has accurate main channel information and statistical wiretap channel information. To reduce the secrecy loss due to receiver-side correlation, this paper proposes a channel-correlation-enabled transmission optimization scheme. In particular, the correlation is viewed as a resource to acquire more knowledge about wiretap channels. Based on this, the statistical distribution of wiretap channels is described more precisely, and an elaborate channel-correlation-enabled AN-aided beamformer is designed. Then, the achievable secrecy rate under transmit power and secrecy outage constraints is derived. Finally, the study is also extended to a specific scenario of multiple-antenna eavesdroppers. Simulation results show that the secrecy rate under transmit power and secrecy outage constraints can be improved under high correlation.

Backscatter communication which enables wireless-powered backscatter devices (BDs) to transmit information by reflecting incident signals, is an energy- and cost-efficient communication technology for Internet-of-Things. This paper considers an unmanned aerial vehicle (UAV)-assisted backscatter communication network (UBCN) consisting of multiple BDs and carrier emitters (CEs) on the ground as well as a UAV. A communicate-while-fly scheme is first designed, in which the BDs illuminated by their associated CEs transmit information to the flying UAV in a time-division-multiple-access manner. Considering the critical issue of the UAV's limited on-board energy and the CEs' transmission energy, we maximize the energy efficiency (EE) of the UBCN by jointly optimizing the UAV's trajectory, the BDs' scheduling, and the CEs' transmission power, subject to the BDs' throughput constraints and harvested energy constraints, as well as other practical constraints. Furthermore, we propose an iterative algorithm based on the block coordinated decent method to solve the formulated mixed-integer non-convex problem, in each iteration of which the variables are alternatively optimized by leveraging the cutting-plane technique, the Dinkelbach's method and the successive convex approximation technique. Also, the convergence and complexity of the proposed algorithm are analyzed. Finally, simulation results show that the proposed communicate-while-fly scheme achieves significant EE gains compared with the benchmark hover-and-fly scheme. Useful insights on the optimal trajectory design and resource allocation are also obtained., Comment: This paper has 31 pages and 10 figures. It is submitted for possible journal publications

In this paper, we study an unmanned aerial vehicle (UAV)-enabled multiuser wireless power transfer (WPT) network, where a UAV is responsible for providing wireless energy for a set of ground devices (GDs) deployed in an area. We focus on the design of UAV trajectory subject to the maximum flight speed limit, in order to maximize the minimum harvested energy among GDs over a particular charging duration. Different from prior works that considered simplified linear energy harvesting models, this paper for the first time takes into account the realistic nonlinear energy harvesting model for the UAV trajectory design. However, the formulated trajectory design problem is highly non-convex and has infinite number of variables, thus making it be challenging to be solved optimally. To tackle this difficulty, we adopt the following three-step approach to obtain an efficient solution. First, we rigorously characterize that the optimal trajectory follows a new successive-hover-and-fly (SHF) structure, where the UAV hovers at a certain set of points for efficiently transferring energy, and flies among these hovering points with the maximum speed following certain arcs (not necessarily straight lines). Next, based on this SHF structure, we transform the original problem to a new one for finding a set of turning point variables during the maximum-speed flight, at which the UAV changes the flight direction without hovering. Finally, we use the techniques of convex approximation to solve the transformed problem. According to the convexity of the nonlinear energy harvesting model, we iteratively solve a series of convex optimization problems to update the UAV trajectory towards a high-quality solution. Numerical results show the convergence of the proposed approach, and validate its performance gain over conventional designs.

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users execute an FL algorithm while training their local FL models using their own data and transmitting the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and the availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to: 1) An optimal user selection algorithm with random resource allocation and 2) a standard FL algorithm with random user selection and resource allocation., This paper has been accepted by IEEE Transactions on Wireless Communications. Our code is available at: https://github.com/mzchen0/Wireless-FL

In this paper, the problem of dynamical deployment of unmanned aerial vehicles (UAVs) equipped with visible light communication (VLC) capabilities for optimizing the energy efficiency of UAV-enabled networks is studied. In the studied model, the UAVs can simultaneously provide communications and illumination to service ground users. Since ambient illumination increases the interference over VLC links while reducing the illumination threshold of the UAVs, it is necessary to consider the illumination distribution of the target area for UAV deployment optimization. This problem is formulated as an optimization problem which jointly optimizes UAV deployment, user association, and power efficiency while meeting the illumination and communication requirements of users. To solve this problem, an algorithm that combines the machine learning framework of gated recurrent units (GRUs) with convolutional neural networks (CNNs) is proposed. Using GRUs and CNNs, the UAVs can model the long-term historical illumination distribution and predict the future illumination distribution. Given the prediction of illumination distribution, the original nonconvex optimization problem can be divided into two sub-problems and is then solved using a low-complexity, iterative algorithm. Then, the proposed algorithm enables UAVs to determine the their deployment and user association to minimize the total transmit power. Simulation results using real data from the Earth observations group (EOG) at NOAA/NCEI show that the proposed approach can achieve up to 68.9% reduction in total transmit power compared to a conventional optimal UAV deployment that does not consider the illumination distribution and user association., This paper has been accepted by IEEE Transactions on Wireless Communications. arXiv admin note: text overlap with arXiv:1909.07554

Scalable video coding (SVC) and video caching are two promising techniques in the 5th generation networks to improve the users’ quality of experience (QoE) in terms of video retrieval. In this paper, we study the video content retrieval in cache-aided device-to-device (D2D) networks, where each video content is coded into multiple layers via SVC. In video caching placement phase, the probabilistic caching placement policy is applied, while in content retrieval phase, the non-orthogonal transmission scheme is utilized. Besides, different D2D transmitter selection algorithms are considered and cache-aided data rate (CADR) is formulated as a metric in this paper, and it is maximized by jointly optimizing the probability caching policy and the power allocation policy in two phases. Analytical results show that probabilistic caching policy incorporated with power domain non-orthogonal transmission scheme can achieve significant benefits compared with other benchmark schemes.

Recently, the so-called cell-free (CF) massive multiple-input multiple-output (MIMO) architecture has been introduced, wherein a very large number of distributed access points (APs) simultaneously and jointly serve a much smaller number of mobile stations (MSs). The paper extends the CF approach to the case in which both the APs and the MSs are equipped with multiple antennas, proposing a beamfoming scheme that, relying on the zero-forcing strategy, does not require channel estimation at the MSs. We contrast the originally proposed formulation of CF massive MIMO with a user-centric (UC) approach wherein each MS is served only by a limited number of APs. Exploiting the framework of successive lower-bound maximization, the paper also proposes and analyzes power allocation strategies aimed at either sum-rate maximization or minimum-rate maximization, both for the uplink and downlink. Results show that the UC approach, which requires smaller backhaul overhead and is more scalable that the CF deployment, also achieves generally better performance than the CF approach for the vast majority of the users, especially on the uplink.

Rendezvous is a fundamental building block in distributed cognitive-radio networks (CRNs), in which pairs or groups of users must find a jointly available channel. Research on the rendezvous problem has focused so far on minimizing the time to rendezvous (to find a suitable channel) or on maximizing the rendezvous degree (percentage of channels on which rendezvous can take place). In this paper, we model the rendezvous problem in a more realistic way that acknowledges the fact that available channels may suffer from interference which varies from location to location as well as over time. In other words, channels are influenced by heterogeneous interference. In this setting, CRNs benefit from rendezvous methods that find a quiet channel, which supports high symbol rates and does not suffer much from dropped packets. In this paper, we propose three important rendezvous design disciplines to achieve bounded rendezvous time, full rendezvous degree, and to rendezvous on quiet channels that suffer little interference. We first present the Disjoint Relaxed Difference Set (DRDS) based rendezvous algorithm as a cornerstone, which ensures rendezvous on every channel (full rendezvous degree) in bounded time. When channels suffer from heterogeneous interference, we propose the Interference based DRDS (I-DRDS) algorithm which ensures rendezvous on channels with less interference, incorporating the interference normalization and interference mapping methods. We conduct extensive simulations to evaluate the proposed algorithms; compared with the state-of-the-art algorithms, the results show that I-DRDS has the best rendezvous performance on less interfered channels, with slightly larger rendezvous time.

This paper proposes a video delivery strategy for dynamic streaming services which maximizes time-average streaming quality under a playback delay constraint in wireless caching networks. The network where popular videos encoded by scalable video coding are already stored in randomly distributed caching nodes is considered under adaptive video streaming concepts, and distance-based interference management is investigated in this paper. In this network model, a streaming user makes delay-constrained decisions depending on stochastic network states: 1) caching node for video delivery, 2) video quality, and 3) the quantity of video chunks to receive. Since wireless link activation for video delivery may introduce delays, different timescales for updating caching node association, video quality adaptation, and chunk amounts are considered. After associating with a caching node for video delivery, the streaming user chooses combinations of quality and chunk amounts in the small timescale. The dynamic decision making process for video quality and chunk amounts at each slot is modeled using Markov decision process, and the caching node decision is made based on the framework of Lyapunov optimization. Our intensive simulations verify that the proposed video delivery algorithm works reliably and also can control the tradeoff between video quality and playback latency., Comment: 28 pages, 11 figures, submission to IEEE TWC

This paper considers sparse device activity detection for cellular machine-type communications with non-orthogonal signatures using the approximate message passing algorithm. This paper compares two network architectures, massive multiple-input multiple-output (MIMO) and cooperative MIMO, in terms of their effectiveness in overcoming inter-cell interference. In the massive MIMO architecture, each base station (BS) detects only the users from its own cell while treating inter-cell interference as noise. In the cooperative MIMO architecture, each BS detects the users from neighboring cells as well; the detection results are then forwarded in the form of log-likelihood ratio (LLR) to a central unit where final decisions are made. This paper analytically characterizes the probabilities of false alarm and missed detection for both architectures. Numerical results validate the analytic characterization and show that as the number of antennas increases, a massive MIMO system effectively drives the detection error to zero, while as the cooperation size increases, the cooperative MIMO architecture mainly improves the cell-edge user performance. Moreover, this paper studies the effect of LLR quantization to account for the finite-capacity fronthaul. Numerical simulations of a practical scenario suggest that in that specific case cooperating three BSs in a cooperative MIMO system achieves about the same cell-edge detection reliability as a non-cooperative massive MIMO system with four times the number of antennas per BS., Comment: 14 pages, 9 figures, to appear in IEEE Transactions on Wireless Communications

Unmanned aerial vehicle (UAV) wireless communication has recently been recognized to be inevitable and prevalent in the fifth-generation (5G) wireless networks. In this paper, we propose a secure transmission scheme for a wiretap channel, where a source communicates with a legitimate UAV in the presence of an eavesdropper. We consider the full-duplex active eavesdropper, which performs both eavesdropping and malicious jamming simultaneously. The source transmits artificial noise (AN) signals, in addition to information signals, to confuse this eavesdropper. By considering the ground-to-UAV channel model, we analyze the hybrid outage probability, which takes both the transmission outage probability and the secrecy outage probability into consideration. We further provide the asymptotic hybrid outage probability in a more compact form, where both the transmit power at the source and the jamming power at the eavesdropper become large with a fixed ratio. Through the analysis and the numerical results, we determine the optimal power allocation factor between information signals and AN signals as well as the operating height of UAV that minimize the hybrid outage probability. We also provide the most harmful antenna configuration of the eavesdropper to the UAV communications, and this paper can be a useful framework for the design of confidential UAV communication system.

This paper investigates the robust transceiver design in a wireless multiple-input multiple-output (MIMO) switching network in which multiple users exchange messages via a multi-antenna relay. Previous works assume that perfect channel state information (CSI) is known at the relay, which is intractable in practice. In this paper, channel uncertainty is considered in both frequency division duplex (FDD) and time division duplex (TDD) systems. Regarding different types of CSI imperfection of uplink and downlink transmission in FDD, a statistical and norm-bounded uncertainty models are adopted to characterize the imperfect CSI of uplink and downlink, respectively. In contrast to FDD, the uplink and downlink channels are reciprocal in TDD, and an identical statistical model is adopted for both uplink and downlink channel uncertainty. For each duplex system, an optimization problem is formulated by minimizing the worst-case mean square error (MSE) with respect to channel uncertainty in the constraint of the maximum transmit power of the relay. In FDD, since the problem is non-convex and difficult to solve, we divide the original problem into two subproblems in which the channel uncertainty of uplink and downlink are treated individually. For the uplink subproblem, we propose an iterative approach to determine a closed-form solution of the robust transceiver. In addition, a Sylvester equation is formulated which closed-form solution is provided explicitly for the downlink channel uncertainty subproblem. An overall iterative algorithm is proposed by combining the two algorithms for the subproblems, which can solve the original problem efficiently. Moreover, in TDD, the optimization problem is non-convex and difficult to solve as well. However, the involved channel uncertainty reduces to uplink only due to the reciprocity of uplink and downlink. We propose an iterative algorithm directly for the overall problem to determine the robust transceiver for TDD systems. The simulation results show that the proposed iterative algorithms reduce the sum MSE efficiently and outperform the existing schemes in the channel uncertain scenarios, which validate our conclusion.

Recently, wireless streaming of on-demand videos of mobile users (MUs) has become the major form of data traffic over cellular networks. As a response, caching popular videos in the storage of small base stations (SBSs) has been regarded as an efficient approach to reduce the transmission latency and alleviate the data traffic loaded over backhaul channels. This paper considers a small-cell based caching market composed of one mobile network operator (MNO) and multiple video service providers (VSPs). In this system, the MNO manages and operates its SBSs, and assigns these SBSs’ storage to different VSPs, who have caching requirements. However, videos have different popularities and MUs present different preferences to these VSPs when they request videos. In addition, the caching service brings different utilities to different VSPs as well as that providing caching service to different VSPs causes distinct costs to the MNO. Such privacy information cannot be aware of among VSPs and the MNO. Therefore, to elicit this hidden information, this paper designs a double auction-based caching mechanism, which ensures the efficient operation of the market by maximizing the social welfare, i.e., the gap between VSPs’ caching utilities and MNO’s caching costs. Moreover, this paper demonstrates the economic properties of the designed caching mechanism, which are also validated by the simulation results.

Internet-of-Things (IoT) is a promising technology to connect massive machines and devices in the future communication networks. In this paper, we study a wireless-powered IoT network (WPIN) with short packet communication (SPC), in which a hybrid access point (HAP) first transmits power to the IoT devices wirelessly, then the devices in turn transmit their short data packets achieved by finite blocklength codes to the HAP using the harvested energy. Different from the long packet communication in conventional wireless network, SPC suffers from transmission rate degradation and a significant packet error rate. Thus, conventional resource allocation in the existing literature based on Shannon capacity achieved by the infinite blocklength codes is no longer optimal. In this paper, to enhance the transmission efficiency and reliability, we first define effective-throughput and effective-amount-of-information as the performance metrics to balance the transmission rate and the packet error rate, and then jointly optimize the transmission time and packet error rate of each user to maximize the total effective-throughput or minimize the total transmission time subject to the users’ individual effective-amount-of-information requirements. To overcome the non-convexity of the formulated problems, we develop efficient algorithms to find high-quality suboptimal solutions for them. The simulation results show that the proposed algorithms can achieve similar performances as that of the optimal solution via exhaustive search, and outperform the benchmark schemes.

In this paper, we propose a method to reuse the cyclic-prefix (CP) intervals of an ongoing OFDM link, by another link, without degrading the OFDM link performance. We label the link that reuses the CP-intervals as CPLink, as they use only the CP-intervals of the ongoing link labeled, MainLink. We leverage the fact that the most commonly used OFDM receivers discard the samples in CP-intervals to design the CPLink that ensures below-noise-floor interference at every MainLink-receiver. The key contribution in this paper is the design and study of zero-knowledge CPLink, in which the CPLink-transmitter ensures below-noise-floor interference at every MainLink-receiver with no knowledge about the locations or the number of MainLink-receivers. We analytically show that the zero-knowledge CPLink capacity is positive. For LTE frame structure with 20-MHz bandwidth and 2-km cell-radius, we evaluate the CPLink for two kinds of CPLink-receivers: full-duplex base-station and a half-duplex device. Even for the cell-edge CPLink-transmitters, full-duplex zero-knowledge CPLink data rates can be up to 20 Mbps (60 Mbps) when the CP duration is ~7% (25%) of the data-symbol duration. The half-duplex CPLink rate is 5–40 Mbps when the CPLink-transmitter is within 250 m of CPLink-receiver. CPLink capacity is found to increase near-linearly with the CP duration, thus mitigating CP overhead.

Wireless nodes in future communication systems need to overcome three barriers when compared to their transitional counterparts, namely, to support significantly higher data rates, have long-lasting energy supplies, and remain fully operational in interference-limited heterogeneous networks. This could be partially achieved by providing three promising features, which are radio frequency (RF) energy harvesting, improper Gaussian signaling, and operating in full-duplex communication mode, i.e., transmit and receive at the same time within the same frequency band. In this paper, we consider these aspects jointly in a multi-antenna heterogeneous two-tier network. In this network, the users in the femtocell share the scarce resources with the cellular users in the macro-cell and have to cope with the interference from the macro-cell base station as well as the transmitter noise and residual self-interference due to imperfect full-duplex operation. Interestingly enough, while these impairments are detrimental from the achievable rate perspective, they are beneficial from the energy harvesting aspect, as they carry RF energy. In this paper, we consider this natural tradeoff jointly and propose appropriate optimization problems for beamforming and optimal resource allocation. Moreover, various receiver structures are employed for both information detection and energy harvesting (EH) capabilities. This paper aims at characterizing the tradeoff between the achievable rates and harvested energies. Rate and energy maximization problems are thoroughly investigated. Interestingly, with sufficiently high EH demands, we observe the convergence of the rate region obtained by non-linear precoding to the rate region achieved by widely linear precoding at the base station.

In this paper, we characterize the wireless interference of a mobile ad hoc network, where the nodes move according to the random waypoint model. The interferers are assumed to be located within an interference region that is defined as a circular region centered in a fixed node located at a given point of the mobility scenario. The main contribution of this paper is the characterization of the aggregate interference caused to the fixed node by mobile interferers located within the interference region. The distribution of the interference is analyzed taking into account the stochastic nature of the path loss due to the mobility of the nodes, as well as fast fading and shadowing effects. The derivation of the characteristic function of the aggregate interference is used in two different estimators, which successfully characterize the interference using only a small set of samples. The theoretical approach is validated through simulations, which confirm its effectiveness. Finally, we assess the accuracy of the proposed estimators, demonstrating the practical value of this paper.

The benefit of having a complementary network such as device-to-device (D2D) communications underlaying cellular systems lies in alleviating the traffic overloading burdens at base stations (BSs). This is critical in modern-day scenarios, e.g., in offloading and caching of mobile data traffic, and delivery of popular Internet contents to users. In this paper, we are chiefly interested in the establishment of D2D networks and how such a network evolves dynamically over time as D2D links are continuously formed and broken. Another important yet challenging research opportunity, i.e., the leverage of human users’ social ties for D2D communications, will also be addressed in this paper. To investigate the D2D network formation problem, we look at a game-theoretic framework where the utility function is designed to balance the physical and social domains—where we systematically extract social tie strengths from a real-world data set. To study the evolution of such D2D networks through time in a dynamic stochastic context, we propose a dynamic potential game and show that its equilibrium behaviors can be achieved through some strategy and payoff learning mechanisms. Simulation results verify our analysis. Using a content delivery application to assess the performance of our proposed scheme, it can be shown that major improvements can be obtained in terms of network delay, content accessibility, and BS load reduction.

This paper deals with the problem of the interference between multiple co-channel transmissions in the downlink of a multi-antenna wireless system. In this framework, symbol-level precoding is a promising technique which is able to constructively exploit the multi-user interference and to transform it into useful power at the receiver side. While previous works on symbol-level precoding were focused on exploiting the multi-user interference, in this paper we extend this concept by jointly handling the interference both in the spatial dimension (multi-user interference) and in the temporal dimension (inter-symbol interference). Accordingly, we propose a novel precoding method, referred to as spatio-temporal symbol-level precoding. In this new precoding paradigm, faster-than-Nyquist signaling can be applied over multi-user MISO systems, and the inter-symbol interference can be tackled at the transmitter side, without additional complexity for the user terminals. While applying faster-than-Nyquist signaling, the proposed optimization strategies perform a sum power minimization with Quality-of-Service constraints. Numerical results are presented in a comparative fashion to show the effectiveness of the proposed techniques, which outperform the state of the art symbol-level precoding schemes in terms of symbol error rate, effective rate, and energy efficiency.

Wireless body area networks (BANs) have many important applications such as wearable communication devices and Internet of Things. Wireless propagation in on-body channels has been measured and modeled in the past. However, a crucial element that is usually ignored is the impact of the body size of the user—a 50-kg person obviously creates a different on-body channel than a 150-kg person. The status-quo “one-size-fits-all” approach to channel characterization in BAN is thus incomplete. In this paper, we provide a detailed description of a propagation measurement campaign that employs a self-developed $4 \times 4$ ultrawideband multiple-input-multiple-output array channel sounding system to perform BAN channel sounding both in an anechoic chamber and indoor laboratory environments. A total of 60 human subjects were investigated in our work. These human subjects had widely varying body mass index (BMI) values, which were grouped into three different categories, i.e., 20 per BMI category. Various propagation properties such as path gain, frequency-decay factor, shadowing gain, rms delay spread, amplitude fading, and spatial correlation are extracted for each on-body channel under consideration. A comparison of statistics among the BMI categories reveals considerable differences, emphasizing the fact that the aforementioned propagation properties are BMI dependent. Parameters such as path gain showed a monotonic decrease across the BMI categories with values ranging from 1–2 to almost 13 dB in some channels. This paper proposes a propagation channel model for the BMI-dependent parameters and validates that it can reproduce the measured channel capacities.