The approximate message passing (AMP)-based data detection is a highly effective solution for terahertz (THz) multiple-input multiple-output (MIMO) communications, enabling reliable data detection at ultra-high data rates. However, in the uplink of THz MIMO systems, high channel correlation leads to performance degradation and computational inefficiencies. To address these challenges, we develop correlated probability estimation (CPE) for the standard AMP iterative data detection algorithm (AMP-IDA), achieving Bayesian-optimal (BO) bit error rate (BER) performance in highly correlated THz channels. To mitigate the significant computational complexity of CPE, we propose an effective signal-to-noise ratio (eSNR)-adjusted channel decorrelation preprocessing (ACDP) method, which leverages whitening transformation and convex optimization, mitigating the impact of row correlation without prior knowledge of correlation indices. By integrating eSNR-ACDP with the low-complexity standard AMP-IDA, we design the ACDP-AMP-IDA, which attains BER close to the BO benchmark with significantly reduced complexity. Compared to orthogonal AMP (OAMP) algorithms, ACDP-AMP-IDA outperforms standard OAMP by up to 8 dB and achieves performance comparable to OAMP with linear minimum mean square error (MMSE) while incurring only 3%–6% of its runtime. Additionally, it surpasses existing AMP-IDA-based and MMSE detectors by over 10 dB and guarantees robust convergence across various transmitter-receiver distances in uplink THz MIMO systems.

This work studies near-field secure communications through transmit beamfocusing. We examine the benefit of having a protected eavesdropper-free zone around the legitimate receiver, and we determine the worst-case secrecy performance against a potential eavesdropper located anywhere outside the protected zone. A max-min optimization problem is formulated for the beamfocusing design with and without artificial noise transmission. Despite the NP-hardness of the problem, we develop a synchronous gradient descent-ascent framework that approximates the global maximin solution. A low-complexity solution is also derived that delivers excellent performance over a wide range of operating conditions. We further extend this study to a scenario where it is not possible to physically enforce a protected zone. To this end, we consider secure communications through the creation of a virtual protected zone using a full-duplex legitimate receiver. Numerical results demonstrate that exploiting either the physical or virtual receiver-centered protected zone with appropriately designed beamfocusing is an effective strategy for achieving secure near-field communications.

We propose a novel framework to analyze the coverage performance of three-dimensional (3D) indoor terahertz (THz) communication systems and examine the impact of the location of a user equipment (UE) on such performance. Specifically, we employ Manhattan line processes to precisely characterize the deployment of wall blockages in the indoor environment. Moreover, we model locations of access points (APs) using a Poisson point process and adopt the nearest line-of-sight AP association strategy. Due to the high penetration loss caused by wall blockages, we consider that a UE, its associated AP, and interfering APs are all in the same rectangular area, i.e., a room. Based on the proposed rectangular area model, we first analyze the impact of the location of a UE on the distance to its associated AP. We then derive a new expression for the coverage probability by adopting the fluctuating two-ray distribution to accurately model the small-scale fading in THz communications. Supported by simulation results, we validate our analysis and demonstrate how the location of the UE affects its coverage probability, offering valuable insights for meeting the coverage requirements of future THz communication system deployments.

In this paper, we develop a tractable analytical framework for a three-dimensional (3D) indoor terahertz (THz) communication system to theoretically assess the impact of the pointing error on its coverage performance. Specifically, we model the locations of access points (APs) using a Poisson point process, human blockages as random cylinder processes, and wall blockages through a Boolean straight line process. A pointing error refers to beamforming gain and direction mismatch between the transmitter and receiver. We characterize it based on the inaccuracy of location estimate. We then analyze the impact of this pointing error on the received signal power and derive a tractable expression for the coverage probability, incorporating the multi-cluster fluctuating two-ray distribution to accurately model small-scale fading in THz communications. Aided by simulation results, we corroborate our analysis and demonstrate that the pointing error has a pronounced impact on the coverage probability. Specifically, we find that merely increasing the antenna array size is insufficient to improve the coverage probability and mitigate the detrimental impact of the pointing error, highlighting the necessity of advanced estimation techniques in THz communication systems.

This work studies near-field secure communications empowered by beamfocusing and demonstrates, for the first time, the benefit of having a protected eavesdropper-free zone around the legitimate receiver. We consider the worst-case secrecy performance against an eavesdropper potentially located anywhere outside the protected zone. Under this consideration, a max-min optimization problem for beamfocusing design is formulated, which can be interpreted as a two-player sequential game between the transmitter and eavesdropper. Despite the NPhardness of the problem, we propose a synchronous gradient descent ascent framework that approximates the global maximin solution. Moreover, we present a low-complexity heuristic beamfocusing solution that delivers excellent performance over a wide range of scenarios. Numerical results demonstrate that exploiting the receiver-centered protected zone with appropriately designed beamfocusing is an effective strategy for achieving near-field secure communications.

Backscatter communication and harvest-then-transmit (HTT) communication are regarded as promising technologies for enabling green Internet of Things (IoT). The current works on the joint use of backscatter communication and HTT are limited in single cell scenarios with the fixed backscatter-then-HTT transmission structure. In this work, we propose a transmission scheme with flexible mode selection for the hybrid backscatter-HTT multi-cell system to achieve much improved communication performance, and then study the joint design for such a system. Specifically, by utilizing multi-antenna technology and enabling the flexible mode selecting between backscatter and HTT, a novel transmission scheme is developed. With the aim to maximize the sum rate of the considered system, we formulate a joint optimization problem for the base station transmission beamforming (TB), the transmission mode (TM), and the transmit power (TP) of the hybrid backscatter-HTT devices. To address the formulated non-convex problem, we propose a block coordinate descent-based algorithm, namely J3TO, to jointly optimize TB, TM, and TP, by decoupling the original problem into three sub-problems. Therein, the weighted minimum mean square error approach, matching theory, and the fractional programming technique are leveraged to deal with the sub-problems efficiently. Simulation results show that the proposed algorithm flexibly integrates the merits of backscatter and HTT technologies, achieving superior performance across various scenarios, compared with the benchmark schemes, e.g., backscatter-only SDMA, HTT-only SDMA, and backscatter-HTT TDMA.

Device authentication in Low Earth Orbit (LEO) satellite-based Internet of Things (IoT) networks is critical for enabling secure and reliable communication between remote IoT devices and satellites. It prevents unauthorized access and security breaches. State-of-the-art authentication methods for terrestrial networks, such as Authentication and Key Management for Applications (AKMA), are inadequate when directly applied to such networks because IoT devices have constrained communication and computational capabilities. Further, the satellite environment is highly dynamic, with frequent handovers and variable latency, leading to vulnerabilities like man-in-the-middle (MITM) and spoofing attacks. To address these challenges, we propose a modified AKMA framework for decentralized and continuous authentication in LEO satellite-based IoT networks. Our proposed modification utilizes local key refreshment for seed generation, seed update, and seed refreshment in a decentralized manner, enabling tailored transmission patterns for IoT devices. This reduces the need for repeated authentication attempts with satellites and effectively mitigates handoff-associated threats. We examine the authentication performance of the system in the presence of an illegitimate Unmanned Aerial Vehicle (UAV) above the legitimate IoT devices. Our results through simulations and emulation show improvement in the authentication rate of legitimate IoT devices and a reduction in the misdetection rate of illegitimate UAVs compared to state-of-the-art physical channel-based authentication schemes. Our proposed modified AKMA enables its application in LEO satellite-based IoT networks.

In this work, we propose an adaptive system design for an Internet of Things (IoT) monitoring network with latency and reliability requirements, where IoT devices generate time-critical and event-triggered bursty traffic, and an unmanned aerial vehicle (UAV) aggregates and relays sensed data to the base station. Existing transmission schemes based on the overall average traffic rates over-utilize network resources when traffic is smooth, and suffer from packet collisions when traffic is bursty which occurs in an event of interest. We address such problems by designing an adaptive transmission scheme employing multiuser shared access (MUSA) based grant-free non-orthogonal multiple access and use short packet communication for low latency of the IoT-to-UAV communication. Specifically, to accommodate bursty traffic, we design an analytical framework and formulate an optimization problem to maximize the performance by determining the optimal number of transmission time slots, subject to the stringent reliability and latency constraints. We compare the performance of the proposed scheme with a non-adaptive power-diversity based scheme with a fixed number of time slots. Our results show that the proposed scheme has superior reliability and stability in comparison to the state-of-the-art scheme at moderate to high average traffic rates, while satisfying the stringent latency requirements.

In this paper, we develop a novel analytical framework for a three-dimensional (3D) indoor terahertz (THz) communication system. Our proposed model incorporates more accurate modeling of wall blockages via Manhattan line processes and precise modeling of THz fading channels via a multi-cluster fluctuating two-ray (MFTR) channel model. We also account for traditional unique features of THz, such as molecular absorption loss, user blockages, and 3D directional antenna beams. Moreover, we model locations of access points (APs) using a Poisson point process and adopt the nearest line-of-sight AP association strategy. Due to the high penetration loss caused by wall blockages, we consider that a user equipment (UE) and its associated AP and interfering APs are all in the same rectangular area, i.e., a room. Based on the proposed rectangular area model, we evaluate the impact of the UE’s location on the distance to its associated AP. We then develop a tractable method to derive a new expression for the coverage probability by examining the interference from interfering APs and considering the MFTR fading experienced by THz communications. Aided by simulation results, we validate our analysis and demonstrate that the UE’s location has a pronounced impact on its coverage probability. Additionally, we find that the optimal AP density is determined by both the UE’s location and the room size, which provides valuable insights for meeting the coverage requirements of future THz communication system deployment.

The beam split effect (BSE) can result in a serious loss in achievable rate in terahertz (THz) transmission. In this work, we propose a new sub-band assignment scheme in a multiuser THz communications system to address the BSE. We consider a base station employs true-time-delay hardware between radio frequency chains and uniform planar arrays (UPAs). The core idea of this hardware is that, rather than fine-tuning the UPA delays to form a single beam from each UPA, we facilitate multi-beam transmission from each UPA. We derive a novel expression for the maximum sub-band bandwidth for UPA, ensuring that the BSE is avoided within each beam. Based on this expression we design sub-band assignment across users and power allocation among sub-bands to maximize the sum-rate, relying on the principles of beam multiplexing and aggregation (BMA). Using numerical results, we demonstrate (i) the merits of our proposed sub-band assignment in contrast to the distance-aware sub-band assignment scheme, (ii) the effectiveness of BMA in comparison with the BSE, and (iii) the improved performance resulting from our proposed optimal power allocation relative to equal power allocation.

Physical-layer authentication is a popular alternative to the conventional key-based authentication for Internet of Things (IoT) devices due to their limited computational capacity and battery power. However, this approach has limitations due to poor robustness under channel fluctuations, reconciliation overhead, and no clear safeguard distance to ensure the secrecy of the generated authentication keys. In this regard, we propose a novel, secure, and lightweight continuous authentication scheme for IoT device authentication. Our scheme utilizes the inherent properties of the IoT devices’ transmission model as its source for seed generation and device authentication. Specifically, our proposed scheme provides continuous authentication by checking the access time slots and spreading sequences of the IoT devices instead of repeatedly generating and verifying shared keys. Due to this, access to a coherent key is not required in our proposed scheme, resulting in the concealment of the seed information from attackers. Our proposed authentication scheme for IoT devices demonstrates improved performance compared to the benchmark schemes relying on physical channels. Our empirical results find a near threefold decrease in the misdetection rate of illegitimate devices and close to zero false alarm rate in various system settings with varied numbers of active devices up to 200 and signal-to-noise ratio from 0 to 25 dB. Our proposed authentication scheme also has a lower computational complexity of at least half the computational cost of the benchmark schemes based on support vector machine and binary hypothesis testing in our studies. This further corroborates the practicality of our scheme for IoT deployments.

By exploiting the degree of freedom on the altitude, unmanned aerial vehicle (UAV) communication can provide ubiquitous communication for future wireless networks. In the case of concurrent transmission of multiple UAVs, the directional beamforming formed by multiple antennas is an effective way to reduce co-channel interference. However, factors, such as airflow disturbance or estimation error for UAV communications, can cause the occurrence of beam misalignment. In this article, we investigate the system performance of a multitier UAV communication network with the consideration of unstable beam alignment. In particular, we propose a tractable random model to capture the impacts of beam misalignment in the 3-D space. Based on this, by utilizing stochastic geometry, an analytical framework for obtaining the outage probability in the downlink of a multitier UAV communication network for the closest distance association scheme and the maximum average power association scheme is established. The accuracy of the analysis is verified by Monte Carlo simulations. The results indicate that in the presence of random beam misalignment, the optimal number of UAV antennas needs to be adjusted to be relatively larger when the density of UAVs increases or the altitude of UAVs becomes higher.

The temporary events are generally gathered around many users interested in the same content. An unmanned aerial vehicle (UAV) with caching and multicasting is attractive for such scenarios with high traffic demands, since the multicasting allows concurrently serving users, and the caching can alleviate the burden on backhaul links. Hence, in this work, we investigate a cellular network assisted by caching-and-multicasting-empowered UAVs, where UAVs multicast the files from their caching storage or base stations via wireless backhaul links. Particularly, a popularity-aware (PA) file selection and multicast scheme is proposed, where the files to be multicasted are determined by the instantaneous requested popularity, the maximum number of allowable fetched files and the performance on wireless backhaul links. By leveraging stochastic geometry, we obtain the approximated yet accurate result for the average number of successfully multicasted users. Our results confirm the effectiveness of the PA scheme, i.e., achieving a larger average number of successfully multicasted users compared to the random scheme for most cases. Moreover, the results suggest that a relatively smaller number of multicast channels or reducing the maximum number of allowable fetched files can benefit network performance in the case of the high signal-to-interference ratio threshold on the backhaul links.

Due to the flexible deployment of unmanned aerial vehicles (UAVs) and the wide-area coverage of satellites, the space–air–ground (SAG) communication network can provide flexible and pervasive connectivity, especially in remote areas. In this work, we investigate the uplink transmission in a SAG network, where the nonorthogonal multiple access mechanism is adopted at the UAVs to enhance the number of access from ground user equipments (UEs) and a low-earth orbit satellite offers the wireless backhaul for UAVs. In particular, the energy efficiency (EE) of the considered network is maximized by optimizing the user association (UA), power allocation (PA), and UAV 3-D trajectory jointly with the consideration of the movement of the satellite. To tackle the formulated problem, by leveraging the block coordinate descent (BCD) method, we develop a joint UA, PA, and UAV trajectory (namely, JUPT) optimization algorithm, i.e., the original problem is decomposed into three subproblems, and the subproblems are solved iteratively until convergence. Specifically, we propose to include the virtual UEs in the system and develop a low-complexity matching algorithm to effectively solve the UA problem. A successive convex approximation (SCA)-based Dinkelbach algorithm is then adopted to address the PA problem. Later, with the introduction of the auxiliary variables, the UAV 3-D trajectory subproblem is iteratively solved by the SCA method. Our numerical results demonstrate the superiority of the proposed JUPT algorithm, which obtains significantly higher EE compared to the benchmark schemes. Moreover, the rapid convergence of the JUPT algorithm is verified.

Intelligent reflecting surfaces (IRS) can improve the performance of backscatter communication systems by employing reconfigurable phase shifts (or passive beamforming) to favorably configure the wireless propagation medium. However, the design of optimal IRS phase shifts requires channel state information (CSI), which is hard to acquire in a multi-reflection channel. In this paper, we propose a deep learning based framework that learns the desired IRS phase shifts without knowing the channels, to assist the communication of a passive backscatter tag. This is achieved by parameterizing the mapping from the received pilots to the desired configuration of IRS by training a deep neural network (DNN) BIRS-Net on a sufficiently large dataset covering a variety of channel realizations and possible power splitting ratios at the backscatter tag. Simulation results show that the proposed DNN based solution can efficiently learn to maximize the SNR of backscatter transmission and exhibits near optimal performance.

We propose a new spectrum allocation strategy for terahertz (THz) band communication (THzCom) systems. Specifically, we design multi-band-based spectrum allocation with adaptive sub-band bandwidth (ASB), by allowing to divide the spectrum of interest into sub-bands with unequal bandwidths. Due to the frequency and distance-dependent nature of the molecular absorption loss, the variation in this loss between the sub-bands would be very high at the THz band when equal sub-band bandwidth (ESB) is considered, as in the literature. The proposed strategy reduces this variation by allowing changes in the sub-band bandwidth, which leads to an overall improvement in the data rate performance. To study the impact of our strategy, we formulate an optimization problem, with the main focus on spectrum allocation, to determine the optimal sub-band bandwidth and transmit power. Thereafter, we propose reasonable approximations and transformations to solve the formulated problem. Aided by numerical results, we show that by enabling and optimizing ASB, a significantly higher data rate can be achieved by our strategy, compared to adopting ESB, and it is more beneficial to adopt ASB when the spectrum with the highest average molecular absorption loss within the THz transmission window is selected during spectrum allocation.

Federated learning (FL) allows predictive model training on the sensed data in a wireless Internet of Things (IoT) network evading data collection cost in terms of energy, time, and privacy. In this article, for an FL setting, we model the learning gain achieved by an IoT device against its participation cost as its utility. The local model quality and the associated cost differ from device to device due to the device heterogeneity, which could be time varying. We identify that this results in utility unfairness because the same global model is shared among the devices. In the vanilla FL setting, the master is unaware of devices’ local model computation and transmission costs, thus, it is unable to address the utility unfairness problem. In addition, a device may exploit this lack of knowledge at the master to intentionally reduce its expenditure and thereby boost its utility. We propose to control the quality of the global model shared with the devices, in each round, based on their contribution and expenditure. This is achieved by employing differential privacy (DP) to curtail global model divulgence based on the learning contribution. Furthermore, we devise adaptive computation and transmission policies for each device to control its expenditure in order to mitigate utility unfairness. Our results show that the proposed scheme reduces the standard deviation of the energy cost of devices by 99% in comparison to the benchmark scheme, while the standard deviation of the training loss of devices varies around 0.103.

We study spectrum allocation for terahertz (THz) band communication (THzCom) systems, while considering the frequency and distance-dependent nature of THz channels. Different from existing studies, we explore multi-band-based spectrum allocation with adaptive sub-band bandwidth (ASB) by allowing the spectrum of interest to be divided into sub-bands with unequal bandwidths. Also, we investigate the impact of sub-band assignment on multi-connectivity (MC) enabled THzCom systems, where users associate and communicate with multiple access points simultaneously. We formulate resource allocation problems, with the primary focus on spectrum allocation, to determine sub-band assignment, sub-band bandwidth, and optimal transmit power. Thereafter, we propose reasonable approximations and transformations, and develop iterative algorithms based on the successive convex approximation technique to analytically solve the formulated problems. Aided by numerical results, we show that by enabling and optimizing ASB, significantly higher throughput can be achieved as compared to adopting equal sub-band bandwidth, and this throughput gain is most profound when the power budget constraint is more stringent. We also show that our sub-band assignment strategy in MC-enabled THzCom systems outperforms the state-of-the-art sub-band assignment strategies and the performance gain is most profound when the spectrum with the lowest average molecular absorption coefficient is selected during spectrum allocation.

This work investigates the data detection problem in an Intelligent Reflecting Surface (IRS) aided downlink communication between a multi-antenna access point (AP) and multiple user equipments (UEs). We utilise a deep learning-based approach, with a maximum likelihood detection (MLD)-based loss function, thereby bypassing the resource-consuming channel training and estimation requirement for detection. The proposed detection framework first trains a base deep neural network (DNN) offline with the simulated samples of the channel coefficients and IRS phase shifts in the IRS-assisted communications scenario. To deal with the significant challenge of the channel getting outdated, domain adaptation under the transfer learning paradigm is leveraged, i.e., the initial layers of the DNN are frozen, and the remaining layers are retrained on a smaller number of the received signal samples online to account for the channel mismatch. Our results show that the proposed detector achieves BER results close to the lower bound and outperforms conventional benchmark techniques, with relatively lower complexity.

We consider uplink machine-type communication (MTC) from energy-constrained devices following the time division multiple access (TDMA) protocol. Conventionally, the energy efficiency performance in TDMA is optimized through multi-user scheduling, i.e., changing the transmission block length allocated to different devices. In such a system, the sequence of devices for transmission, i.e., who transmits first and who transmits second, etc., has not been considered as it does not have any impact on the energy efficiency. In this work, we consider that data compression is performed before transmission and show that the multi-user sequencing is indeed important. We propose to jointly optimize both multi-user sequencing and scheduling along with the compression and transmission rate control. Our results show that multi-user sequence optimization significantly improves the energy efficiency performance of the system, and especially the performance gain is large when the delay bound is stringent. This is advantageous for lower latency MTC.

One of the key challenges of the Internet of Things (IoT) is to sustainably power the large number of IoT devices in real-time. In this paper, we consider a wireless power transfer (WPT) scenario between an energy transmitter (ET) capable of retrodirective WPT and an energy receiver (ER) capable of ambient backscatter in the presence of an ambient source (AS). The ER requests WPT by backscattering signals from an AS towards the ET, which then retrodirectively beamforms an energy signal towards the ER. To remove the inherent directlink ambient interference, we propose a scheme of ambient backscatter training. Specifically, the ER varies the reflection coefficient multiple times while backscattering each ambient symbol according to a certain pattern called the training sequence, whose design criterion we also present. To evaluate the system performance, we derive an analytical expression for the average harvested power at the ER. Our numerical results show that with the proposed scheme, the ER harvests tens of μW of power, without any CSI estimation or active transmission from the ER, which is a significant improvement for low-power and low-cost ambient backscatter devices.

The consideration of unmanned aerial vehicle (UAV) trajectory is of crucial importance in the performance evaluation of cellular-connected UAV networks. In this work, we consider a cellular-connected aerial user equipment (AUE) employed for surveillance and monitoring. The AUE moves along a given trajectory, while periodically transmitting to a terrestrial base station (BS) in the uplink, with a specific quality of service (QoS) requirement. To avoid the underutilization of spectrum resources, we enable simultaneous uplink transmissions of the AUE and a terrestrial user equipment (TUE) using power-domain uplink aerial-terrestrial non-orthogonal multiple access (NOMA). We take the trajectory of AUE into consideration and develop an analytical framework to compute the total rate coverage probability, i.e., the probability where both AUE and TUE are decoded, at a given transmission point in the trajectory. In addition, we numerically determine the minimum height of AUE to achieve a certain QoS constraint for different AUE target data rates and built-up areas. Our results show that, for a spiral trajectory, the minimum height increases as the AUE moves from cell center to the boundary, and as the severity of the environmental parameters increases.

In this paper, we propose joint sequencing and scheduling optimization for uplink machine-type communication (MTC). We consider multiple energy-constrained MTC devices that transmit data to a base station following the time division multiple access (TDMA) protocol. Conventionally, the energy efficiency performance in TDMA is optimized through multi-user scheduling, i.e., changing the transmission block length allocated to different devices. In such a system, the sequence of devices for transmission, i.e., who transmits first and who transmits second, etc., has not been considered as it does not have any impact on the energy efficiency. In this work, we consider that data compression is performed before transmission and show that the multi-user sequencing is indeed important. We apply three popular energy-minimization system objectives, which differ in terms of the overall system performance and fairness among the devices. We jointly optimize both multi-user sequencing and scheduling along with the compression and transmission rate control. Our results show that multi-user sequence optimization significantly improves the energy efficiency performance of the system. Notably, it makes the TDMA-based multi-user transmissions more likely to be feasible in the lower latency regime, and the performance gain is larger when the delay bound is stringent.

This paper considers an emerging cellular-connected unmanned aerial vehicle (UAV) architecture for surveillance or monitoring applications. We study a scenario of interest where a cellular-connected aerial user equipment (AUE) periodically transmits in uplink to a base station (BS) with a given data rate requirement, while moving along a given trajectory. For an efficient spectrum usage, we enable the concurrent uplink transmission of the AUE and a terrestrial user equipment (TUE) by employing power-domain aerial-terrestrial non-orthogonal multiple access (NOMA), while accounting for the AUE's known trajectory. To characterize the system performance, we develop an analytical framework to compute the rate coverage probability, i.e., the probability that the achievable data rate of both the AUE and TUE exceeds the respective target rates. We use our analytical results to numerically determine the minimum height that the AUE needs to fly, at each transmission point along the given trajectory, in order to satisfy a certain quality of service (QoS) constraint of various AUE target data rates in different built-up environments. Specifically, our results show that the minimum height of the AUE depends on its distance from the BS as the AUE moves along the given trajectory which indicates the importance of modeling AUE trajectory in cellular-connected UAV systems.

Wireless power transfer (WPT) using energy beamforming is a promising solution for low power Internet of Things (IoT) devices. In this work, we consider WPT from an energy transmitter (ET) employing retrodirective WPT using a large phased antenna array to an energy receiver (ER) capable of ambient backscatter. The advantage of retrodirective WPT is that no explicit channel estimation is needed at the ET and the use of ambient backscattering eliminates the need for active transmission at the ER. We propose a training sequence design, i.e., pattern of varying the reflection coefficient at the ER, to eliminate the direct-link interference from the ambient source. We show that when the ambient symbol duration is known, the ambient interference is fully cancelled by the proposed design. We analytically model the system and find the average harvested power at the ER considering Nakagami-m fading channels and non-linear energy harvesting model. Our results clearly show that the proposed solution is robust to a small timing offset mismatch at the correlator. When interference from undesired neighbouring sources in the ambient environment is not significant, the ER can successfully harvest tens to hundreds of μW of power, which is an important improvement for low-power IoT devices.

We consider a machine-type communication (MTC) node that is served by a hybrid access point (HAP) which provides RF power transfer to the node and receives data transmission from the node. Due to the lossy wireless medium and limited efficiency of RF energy transducer, the energy cost at the HAP is substantial. To minimize the energy cost while still satisfying the system requirement, the harvested energy at the MTC node must be used efficiently. To this end, we consider that the MTC node employs data compression in order to reduce the energy cost of data transmission. Data compression itself consumes time and energy, which needs to be carefully controlled. Thus, we propose to jointly optimize the harvesting-time, compression and transmission design, to minimize the energy cost of the system under given delay constraint. The proposed scheme achieves up to 19% performance gain, under given system constraints, as compared to optimizing harvesting-time ratio and transmission rate without employing compression.

Providing seamless connection to a large number of devices is one of the biggest challenges for the Internet of Things (IoT) networks. Using a drone as an aerial base station (ABS) to provide coverage to devices or users on ground is envisaged as a promising solution for IoT networks. In this paper, we consider a communication network with an underlay ABS to provide coverage for a temporary event, such as a sporting event or a concert in a stadium. Using stochastic geometry, we propose a general analytical framework to compute the uplink and downlink coverage probabilities for both the aerial and the terrestrial cellular system. Our framework is valid for any aerial channel model for which the probabilistic functions of line-of-sight (LOS) and non-LOS links are specified. The accuracy of the analytical results is verified by Monte Carlo simulations considering two commonly adopted aerial channel models. Our results show the nontrivial impact of the different aerial channel environments (i.e., suburban, urban, dense urban, and high-rise urban) on the uplink and downlink coverage probabilities and provide design guidelines for best ABS deployment height.

For a sensor based machine-type communication (MTC) device, transmission is a power hungry operation and blindly applying too much data compression may even exceed the cost of transmitting raw data, thus losing its purpose. Hence, it is important to investigate the trade-off between data compression and transmission energy costs. We consider a system that is composed of an energy constrained sensor based MTC device and a sink node, and devise an optimal data compression and transmission policy with an objective to maximize the lifetime of the sensor based MTC device whilst satisfying specific delay and bit error rate (BER) constraints when statistical channel gain is known at the sensor node. Our results show that a jointly optimized compression-transmission policy achieves 100% to 1500% better performance as compared to optimizing transmission only without compression under given BER and delay constraints. Importantly, the gain is most profound in the low latency regime.

This work aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN), where data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. We formulate a new mixed-integer nonlinear problem in which the ratio of network throughput and total power consumption is maximized. This challenging problem formulation includes practical constraints on routing, predefined minimum data rates, fronthaul capacity and maximum RRH transmit power. By employing the successive convex quadratic programming framework, an iterative algorithm is proposed with guaranteed convergence to a Fritz John solution of the formulated problem. Significantly, each iteration of the proposed algorithm solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization design markedly improves the C-RAN's energy efficiency compared to benchmark schemes.

This paper aims to maximize the spectral and energy efficiencies of a content-centric cloud radio access network (C-RAN), where users requesting the same contents are grouped together. Data are transferred from a central baseband unit to multiple remote radio heads (RRHs) equipped with local caches. The RRHs then send the received data to each group's user. Both multicast and unicast schemes are considered for data transmission. We formulate mixed-integer nonlinear problems in which user association, RRH activation, data rate allocation, and signal precoding are jointly designed. These challenging problems are subject to minimum data rate requirements, limited fronthaul capacity, and maximum RRH transmit power. Employing successive convex quadratic programming, we propose iterative algorithms with guaranteed convergence to Fritz John solutions. Numerical results confirm that the proposed joint designs markedly improve the spectral and energy efficiencies of the considered content-centric C-RAN compared to benchmark schemes. Importantly, they show that unicasting outperforms multicasting in terms of spectral efficiency in both cache and cache-less scenarios. In terms of energy efficiency, multicasting is the best choice for the system without cache whereas unicasting is best for the system with cache. Finally, edge caching is shown to improve both spectral and energy efficiencies.

Deployment of low-cost power beacons (PBs) is a promising solution for dedicated wireless power transfer (WPT) in future wireless networks. In this paper, we present a tractable model for PB-assisted millimeter wave (mmWave) wireless ad hoc networks, where each transmitter (TX) harvests energy from all PBs and then uses the harvested energy to transmit information to its desired receiver. Our model accounts for realistic aspects of WPT and mmWave transmissions, such as power circuit activation threshold, allowed maximum harvested power, maximum transmit power, beamforming, and blockage. Using stochastic geometry, we obtain the Laplace transform of the aggregate received power at the TX to calculate the power coverage probability. We approximate and discretize the transmit power of each TX into a finite number of discrete power levels in log scale to compute the channel and total coverage probability. We compare our analytical predictions to simulations and observe good accuracy. The proposed model allows insights into effect of system parameters, such as transmit power of PBs, PB density, main lobe beamwidth, and power circuit activation threshold on the overall coverage probability. The results confirm that it is feasible and safe to power TXs in an mmWave ad hoc network using PBs.

We consider a system that is composed of an energy constrained sensor node and a sink node, and devise optimal data compression and transmission policies with an objective to prolong the lifetime of the sensor node. While applying compression before transmission reduces the energy consumption of transmitting the sensed data, blindly applying too much compression may even exceed the cost of transmitting raw data, thereby losing its purpose. Hence, it is important to investigate the trade-off between data compression and transmission energy costs. In this paper, we study the joint optimal compression-transmission design in three scenarios which differ in terms of the available channel information at the sensor node, and cover a wide range of practical situations. We formulate and solve joint optimization problems aiming to maximize the lifetime of the sensor node whilst satisfying specific delay and bit error rate constraints. Our results show that a jointly optimized compression-transmission policy achieves significantly longer lifetime (90% to 2000%) as compared to optimizing transmission only without compression. Importantly, this performance advantage is most profound when the delay constraint is stringent, which demonstrates its suitability for low latency communication in future wireless networks.

Backscatter communication (BackCom), which allows a backscatter node (BN) to communicate with the reader by modulating and reflecting the incident continuous wave from the reader, is considered a promising solution to power the future Internet-of-Things. In this paper, we consider a single BackCom system, where multiple BNs are served by a reader. We propose using the power-domain non-orthogonal multiple access (NOMA), i.e., multiplexing the BNs in different regions or with different backscattered power levels, to enhance the spectrum efficiency of the BackCom system. To better exploit power-domain NOMA, we propose setting the reflection coefficients for multiplexed BNs to be different. Based on this considered model, we develop the reflection coefficient selection criteria. To illustrate the enhanced system with the proposed criteria, we analyze the performance of the BackCom system in terms of the average number of bits that can be successfully decoded by the reader for the two-node pairing case and the average number of successful BNs for the general multiplexing case. Our results show that NOMA achieves the much better performance gain in the BackCom system as compared to its performance gain in the conventional system, which highlights the importance of applying NOMA to the BackCom system.

This paper aims to maximize the energy efficiency of a downlink cloud radio access network (C-RAN). Here, data is transferred from a baseband unit in the core network to several remote radio heads via a set of edge routers over capacity-limited fronthaul links. The remote radio heads then send the received signals to their users via radio access links. Both data sharing and compression-based strategies are considered for fronthaul data transfer. New mixed-integer nonlinear problems are formulated, in which the ratio of network throughput and total power consumption is maximized. These challenging problem formulations include practical constraints on routing, predefined minimum data rates, fronthaul capacity, and maximum remote radio head transmit power. By employing the successive convex quadratic programming, iterative algorithms are proposed with guaranteed convergence to the Fritz John solutions of the formulated problems. Significantly, each iteration of the proposed algorithms solves only one simple convex program. Numerical examples with practical parameters confirm that the proposed joint optimization designs markedly improve the C-RAN's energy efficiency compared to benchmark schemes. They also show that the fronthaul data-sharing strategy outperforms its compression-based counterpart in terms of energy efficiency, in both single-hop and multi-hop network scenarios.

Enabling machine-type communication (MTC) over large scale cellular networks is a promising solution to handling the emerging MTC traffic. To enable a massive number of machines to connect to the base station, random access mechanisms and data aggregation have been largely studied separately in the literature. In this paper, we use stochastic geometry to investigate MTC over cellular with access class barring enhanced random access and data aggregation. We present an approximate yet accurate and tractable analytical framework for characterizing the MTC performance in terms of the machine type device (MTD) success probability, average number of successful MTDs and probability of successful preamble utilization. We validate the proposed model by comparison with simulations. Our results show that while the provision of more resources for the relaying phase benefits MTC, the provision of more preambles in the random access is not always beneficial to MTC. Thus, system parameters need to be chosen carefully to benefit the MTC traffic.

Future Internet-of-Things (IoT) is expected to wirelessly connect tens of billions of low- complexity devices. Extending the finite battery life of massive number of IoT devices is a crucial challenge. The ultra-low-power backscatter communications (BackCom) with the inherent feature of RF energy harvesting is a promising technology for tackling this challenge. Moreover, many future IoT applications will require the deployment of dense IoT devices, which induces strong interference for wireless information transfer (IT). To tackle these challenges, in this paper, we propose the design of a novel multiple-access scheme based on time-hopping spread-spectrum (TH-SS) to simultaneously suppress interference and enable both two-way wireless IT and one-way wireless energy transfer (ET) in coexisting backscatter reader-tag links. The performance analysis of the BackCom network is presented, including the bit-error rates for forward and backward IT and the expected energy-transfer rate for forward ET, which account for non-coherent and coherent detection at tags and readers, and energy harvesting at tags, respectively. Our analysis demonstrates a tradeoff between energy harvesting and interference performance. Thus, system parameters need to be chosen carefully to satisfy given BackCom system performance requirement.

This paper considers the downlink of a cache- enabled fog radio access network (F-RAN) with limited fronthaul capacity, where user association (UA), data delivery rate (DDR) and signal precoding are jointly optimized. We formulate a mixed-integer nonlinear programming problem in which the weighted difference of network throughput and total power consumption is maximized, subject to the predefined DDR requirements and the maximum transmit power at each eRRH. To address this challenging problem, we first apply the l0-norm approximation and l1-norm minimization techniques to deal with the UA. After this key step, we arrive at an approximated problem that only involves the joint optimization of DDR and precoding. By using the alternating descent method, we further decompose this problem into a convex subproblem for DDR allocation and a nonconvex subproblem for precoding design. While the former is globally solved by the interior-point method, the latter is solved by a specifically tailored successive convex quadratic programming method. Finally, we propose an iterative algorithm for the original joint optimization that is guaranteed to converge. Importantly, each iteration of the developed algorithm only involves solving simple convex problems. Numerical examples demonstrate that the proposed design significantly improves both throughput and power performances, especially in practical F-RANs with limited fronthaul capacity. Compared to the sole precoder design for a given cache placement, our joint design is shown to improve the throughput by 50% while saving at least half of the total power consumption in the considered examples.

For the fast and exact computation of spherical harmonic transform (SHT) of a band-limited signal defined on the sphere from its samples, the Gauss-Legendre (GL) and equiangular sampling schemes on the sphere require asymptotically least number of samples. In comparison to the equiangular scheme, the GL scheme has larger spatial dimensionality, defined as the number of the samples required for the exact computation of SHT. In this work, we propose an efficient GL sampling scheme with spatial dimensionality equal to that of equiangular scheme. We also propose optimisation of samples along longitude to further reduce the spatial dimensionality of equiangular, GL and efficient GL sampling schemes. Furthermore, we demonstrate that the accuracy of the SHT is not affected with the proposed reduction in the spatial dimensionality.

Residual self-interference cancellation is an important practical requirement for realizing the full potential of full-duplex (FD) communication. Traditionally, the residual selfinterference is cancelled via digital processing at the baseband, which requires accurate knowledge of channel estimates of the desired and self-interference channels. In this work, we consider point-to-point FD communication and propose a superimposed signaling technique to cancel the residual self-interference and detect the data without estimating the unknown channels. We show that when the channel estimates are not available, data detection in FD communication results in ambiguity if the modulation constellation is symmetric around the origin. We demonstrate that this ambiguity can be resolved by superimposed signalling, i.e., by shifting the modulation constellation away from the origin, to create an asymmetric modulation constellation. We compare the performance of the proposed detection method to that of the conventional channel estimation-based detection method, where the unknown channels are first estimated and then the data signal is detected. Simulations show that for the same average energy over a transmission block, the bit error rate performance of the proposed detection method is better than that of the conventional method. The proposed method does not require any channel estimates and is bandwidth efficient.

Wireless power transfer (WPT) has emerged as an attractive solution to power future wireless communication networks. In this paper, we consider WPT using power beacons (PBs) for a millimeter wave (mmWave) wireless ad hoc network. Using stochastic geometry, we derive the moment generating function (MGF) and the nth cumulant of the aggregate received power from PBs at a reference receiver in closed-form. The MGF allows the complementary cumulative distribution function (CCDF) of the aggregate received power from PBs to be numerically evaluated. We also compare different closed-form distributions which can be used to approximate the CCDF of the aggregate received power. Our results show that the lognormal distribution provides the best CCDF approximation compared to other distributions considered in the literature. The results also show that under practical setups, it is feasible to power users in a mmWave ad hoc network using PBs.

Access class barring (ACB) is regarded as an efficient and practically implementable method to reduce the traffic overload in cellular networks. In this paper, we present a unified analytical framework to analyze the performance of the fixed ACB scheme for a simple random access procedure (i.e., one-shot transmission model) in machine type communication (MTC) over cellular networks. We derive the exact expressions for the probability of a machine's packet being served by the base station (BS), the average number of machine type devices (MTDs) successfully served by the BS per second and the noncollision slot access probability. We verify the accuracy of the derived expressions by comparison with simulations. Based on the analytical expressions, we then maximize the probability of a MTD's packet being served and obtain the sub-optimal probability factor value for the fixed ACB in closed-form. Our results confirm that, the use of ACB scheme is important for scenarios with high MTD packet arrival rate, which is relevant for massive MTC. The proposed framework allows fine tuning and accurate prediction of the MTC performance with ACB.

In this paper, we consider underlay in-band device-to-device (D2D) communication in a finite cellular network region. To minimize the D2D interference generated at the base station (BS), we adopt the exclusion zone mechanism, i.e., only D2D users outside the BS exclusion zone share the same resource with the cellular uplink user. Using the stochastic geometry, we develop a general framework to analytically compute the outage probability at the center-located BS and the outage probability at an arbitrarily located D2D receiver in a disk-shaped network region. To quantify the overall D2D communication performance in the finite region, the average number of successful D2D transmissions is also derived. It shows that the D2D receiver close to the cell edge or the exclusion zone experiences lower outage probability compared to the D2D receiver not close to the edge region, which illustrates the location-dependent performance. Moreover, given the outage probability constraint at the BS, which is controlled by varying the radius of the exclusion zone, we find that there is an optimum D2D receiver sensitivity that results in the maximum average number of successful D2D transmissions. The results highlight the importance of carefully choosing system parameters to extract the benefit from the exclusion zone.

Increasing densification in future wireless networks means that user association will play an ever more critical role in the network decision process in order to manage the large number of base stations and users. Though conventional user association aims to maximize a sum rate or capacity related objective, user rate fairness could become a more important consideration for dense networks. In this paper, we propose a downlink base station preference association scheme where users connect to the base station where it is most preferred in terms of the maximum received power. We prove analytically that this scheme results in roughly the same number of users associated to each base station regardless of base station transmit power, and will result in high user rate fairness in dense networks. In addition, we study how the associations change with network dynamics, i.e., users entering and exiting the network (e.g., due to users crossing boundaries of small cells) or base stations entering and exiting the network (e.g., due to base station switching ON or OFF to reduce energy consumption). Our results show that there exists a type of user most likely to re-associate, and that a shrinking network leads to more re-association than a growing one.

To enable massive machine type communication (mMTC), data aggregation is a promising approach to reduce the congestion caused by a massive number of machine type devices (MTDs). In this paper, we consider a two-phase cellular-based mMTC network, where MTDs transmit to aggregators (i.e., aggregation phase) and the aggregated data is then relayed to base stations (i.e., relaying phase). Due to the limited resources, the aggregators not only aggregate data, but also schedule resources among MTDs. We consider two scheduling schemes: random resource scheduling (RRS) and channel-aware resource scheduling (CRS). By leveraging the stochastic geometry, we present a tractable analytical framework to investigate the signal-to-interference ratio (SIR) for each phase, thereby computing the MTD success probability, the average number of successful MTDs and probability of successful channel utilization, which are the key metrics characterizing the overall mMTC performance. Our numerical results show that, although the CRS outperforms the RRS in terms of SIR at the aggregation phase, the simpler RRS has almost the same performance as the CRS for most of the cases with regards to the overall mMTC performance. Furthermore, the provision of more resources at the aggregation phase is not always beneficial to the mMTC performance.

In this paper, we propose a novel splitting receiver, which involves a joint processing of coherently and non-coherently received signals. Using a passive RF power splitter, the received signal at each receiver antenna is split into two streams, which are then processed by a conventional coherent detection (CD) circuit and a power-detection (PD) circuit, respectively. The streams of the signals from all the receiver antennas are then jointly used for information detection. We show that the splitting receiver creates a 3-D received signal space due to the joint coherent and non-coherent processing. We analyze the achievable rate of a splitting receiver, which shows that the splitting receiver provides a rate gain of 3/2 compared with either the conventional (CD-based) coherent receiver or the PD-based non-coherent receiver in the high SNR regime. We also analyze the symbol error rate (SER) for practical modulation schemes, which shows that the splitting receiver achieves asymptotic SER reduction by a factor of at least √M-1 for M-QAM compared with either the conventional (CD-based) coherent receiver or the PD-based non-coherent receiver.

Underlay in-band device-to-device (D2D) communication can improve the spectrum efficiency of cellular networks. However, the coexistence of D2D and cellular users causes inter-cell and intra-cell interference. The former can be effectively managed through inter-cell interference coordination and, therefore, is not considered in this paper. Instead, we focus on the intra-cell interference and propose a D2D mode selection scheme to manage it inside a finite cellular network region. The potential D2D users are controlled by the base station (BS) to operate in D2D mode based on the average interference generated to the BS. Using stochastic geometry, we study the outage probability experienced at the BS and a D2D receiver, and spectrum reuse ratio, which quantifies the average fraction of successfully transmitting D2D users. The analysis shows that the outage probability at the D2D receiver varies for different locations. In addition, without impairing the performance at the BS, if the path-loss exponent on the cellular link is slightly lower than that on the D2D link, the spectrum reuse ratio can have negligible decrease, while the D2D users' average number of successful transmissions increases with increasing D2D node density. This indicates that an increasing level of D2D communication can be beneficial in future networks.

Future Internet-of-Things (IoT) is expected to wirelessly connect billions of low-complexity devices. For wireless information transfer (IT) in IoT, high density of IoT devices and their ad hoc communication result in strong interference, which acts as a bottleneck on wireless IT. Furthermore, battery replacement for the massive number of IoT devices is difficult if not infeasible, making wireless energy transfer (ET) desirable. This motivates: 1) the design of full-duplex wireless IT to reduce latency and enable efficient spectrum utilization and 2) the implementation of passive IoT devices using backscatter antennas that enable wireless ET from one device (reader) to another (tag). However, the resultant increase in the density of simultaneous links exacerbates the interference issue. This issue is addressed in this paper by proposing the design of full-duplex backscatter communication (BackCom) networks, where a novel multiple-access scheme based on time-hopping spread-spectrum is designed to enable both one-way wireless ET and two-way wireless IT in coexisting backscatter reader-tag links. Comprehensive performance analysis of BackCom networks is presented in this paper, including forward/backward bit-error rates and wireless ET efficiency and outage probabilities, which accounts for energy harvesting at tags, non-coherent and coherent detection at tags and readers, respectively, and the effects of asynchronous transmissions.

In this paper, we investigate the performance of simultaneous wireless information and power transfer (SWIPT) in a point-to-point system, adopting practical M-ary modulation. We take into account the fact that the receiver's radio-frequency (RF) energy harvesting circuit can only harvest energy when the received signal power is greater than a certain sensitivity level. For both power-splitting (PS) and time-switching (TS) schemes, we derive the energy harvesting performance as well as the information decoding performance for the Nakagami-m fading channel. We also analyze the performance tradeoff between energy harvesting and information decoding by studying an optimization problem, which maximizes the information decoding performance and satisfies a constraint on the minimum harvested energy. Our analysis shows that (i) for the PS scheme, modulations with high peak-to-average power ratio achieve better energy harvesting performance, (ii) for the TS scheme, it is desirable to concentrate the power for wireless power transfer in order to minimize the non-harvested energy caused by the RF energy harvesting sensitivity level, and (iii) channel fading is beneficial for energy harvesting in both PS and TS schemes.

Millimeter-wave (mm-wave) systems suffer from an assortment of propagation and hardware challenges such as extremely high pathloss/shadowing and amplifier non-linearity/phase noise, respectively. In this paper, we demonstrate via simulations that non-coherent frequency shift keying (FSK) can utilize the vast bandwidth at mm-wave frequencies to combat significant pathloss and shadowing in this band, while being robust to amplifier non-linearity and phase noise. To support our findings, we establish a comprehensive simulation setup and set of parameters that consider the impact of pathloss, shadowing, amplifier non-linearity, and phase noise, at mm-wave frequencies. Our results indicate that non-coherent FSK outperforms other modulation schemes such as phase shift keying and quadrature amplitude modulation. This outcome combined with the low detection complexity of non-coherent FSK make it an attractive modulation for achieving multi Gbps wireless links at mm-wave frequencies. The proposed comprehensive simulation setup can also be applied to investigate and validate the performance of various mm-wave systems in practical settings.

We consider the downlink of a dense multicell network where each cell region is divided into two zones. The users nearby their serving base station (BS) in the inner zone implement simultaneous wireless information and power transfer (SWIPT), thus harvest energy and decode information using the power splitting approach. Further, they try to eavesdrop the information intended for other users within the same cell. The users in the outer zone of each cell only implement information decoding. Our objective is to maximize the minimum user equipment (UE) signal-to-interference-and-noise ratio (SINR) under constraints on the BS transmit power, minimum energy harvesting levels of near-by users, and maximum SINR of eavesdroppers in the presence of multi-cell interference. For such a highly non-convex problem, semidefinite relaxation (SDR) may even fail to locate a feasible solution. We propose two methods to address such a difficult problem. In the spectral optimization, we express the rank-one constraints as a single reverse convex nonsmooth constraint and incorporate it into the optimization objective. In the difference-of-convex-functions iteration method, we directly solve for the beamforming vectors via quadratic programming (QP), avoiding the matrix rank constraints. In each iteration of the proposed algorithms, we only solve one simple convex semidefinite program (SDP) or QP. Our simulation results confirm that the proposed algorithms converge quickly after a few iterations. More importantly, our algorithms yield the performance that is very close to the theoretical bound given by SDP relaxation with comparable computational complexity.

We propose a Gauss-Legendre quadrature based sampling on the rotation group for the representation of a band-limited signal such that the Fourier transform (FT) of a signal can be exactly computed from its samples. Our figure of merit is the sampling efficiency, which is defined as a ratio of the degrees of freedom required to represent a band-limited signal in harmonic domain to the number of samples required to accurately compute the FT. The proposed sampling scheme is asymptotically as efficient as the most efficient scheme developed very recently. For the computation of FT and inverse FT, we also develop fast algorithms of complexity similar to the complexity attained by the fast algorithms for the existing sampling schemes. The developed algorithms are stable, accurate and do not have any pre-computation requirements. We also analyse the computation time and numerical accuracy of the proposed algorithms and show, through numerical experiments, that the proposed Fourier transforms are accurate with errors on the order of numerical precision.

This paper proposes a centralized decision making framework at the macro base station (MBS) for device-to-device (D2D) communication underlaying a two-tier cellular network. We consider a D2D pair in the presence of an MBS and a femto access point, each serving a user, with quality of service constraints for all users. Our proposed solution encompasses mode selection (choosing between cellular or reuse or dedicated mode), resource allocation (in cellular and dedicated mode), and power control (in reuse mode) within a single framework. The framework prioritizes D2D dedicated mode if the D2D pair is close to each other and orthogonal resources are available. Otherwise, it allows D2D reuse mode if the D2D satisfies both the maximum distance and an additional interference criteria. For reuse mode, we present a geometric vertex search approach to solve the power allocation problem. We analytically prove the validity of this approach and show that it achieves near optimal performance. For cellular and dedicated modes, we show that frequency sharing maximizes sum rate and solve the resource allocation problem in a closed form. Our simulations demonstrate the advantages of the proposed framework in terms of the performance gains achieved in the D2D mode.

Energy harvesting (EH) provides a means of greatly enhancing the lifetime of wireless sensor nodes. However, the randomness inherent in the EH process may cause significant delay for performing sensing operations and transmitting sensed information to the sink. Unlike most existing studies on the delay performance of EH sensor networks, where only the energy consumption of transmission is considered, we consider the energy costs of both sensing and transmission. Specifically, we consider an EH sensor that monitors some status property and adopts a harvest-then-use protocol to perform sensing and transmission. To comprehensively study the delay performance, we consider two complementary metrics and analytically derive their statistics: 1) update age-measuring the time taken from when information is obtained by the sensor to when the sensed information is successfully transmitted to the sink, i.e., how timely the updated information at the sink is, and 2) update cycle-measuring the time duration between two consecutive successful transmissions, i.e., how frequently the information at the sink is updated. Our results show that the consideration of sensing energy cost leads to an important tradeoff between the two metrics: more frequent updates result in less timely information available at the sink.

In this paper, we propose using a wireless-powered friendly jammer to enable secure communication between a source node and destination node, in the presence of an eavesdropper. We consider a two-phase communication protocol with fixed-rate transmission. In the first phase, wireless power transfer is conducted from the source to the jammer. In the second phase, the source transmits the information-bearing signal under the protection of a jamming signal sent by the jammer using the harvested energy in the first phase. We analytically characterize the long-term behavior of the proposed protocol and derive a closed-form expression for the throughput. We further optimize the rate parameters for maximizing the throughput subject to a secrecy outage probability constraint. Our analytical results show that the throughput performance differs significantly between the single-antenna jammer case and the multiantenna jammer case. For instance, as the source transmit power increases, the throughput quickly reaches an upper bound with single-antenna jammer, while the throughput grows unbounded with multiantenna jammer. Our numerical results also validate the derived analytical results.

Realistic modeling of energy consumption is crucial for accurate performance analysis of wireless-powered sensor nodes. In this paper, we analyze the performance of wireless-powered sensor transmissions taking into account both the energy cost of sensing and transmission. We consider a sensor that is harvesting energy from an ambient radio-frequency (RF) signal and using this energy to perform sensing and transmission. Since energy harvesting is time-varying in nature, it introduces a delay in the sensor transmissions. We study two delay-related metrics, one measuring how frequent the sensed information is updated at the sink and the other measuring the time taken from the sensing operation to successful transmission of sensed information. We analytically characterize the statistical behavior of both metrics and find an important tradeoff between them. In particular, our results illustrate that more frequent update of sensed information at the sink increases the time taken from the sensing operation to successful transmission of sensed information.

This paper considers a heterogeneous multicell network where the base station (BS) in each cell communicates with its cell-edge user with the assistance of an amplify-and-forward relay node. Equipped with a power splitter and a wireless energy harvester, the relay scavenges RF energy from the received signals to process and forward the information. In the face of strong intercell interference and limited radio resources, we develop a resource allocation scheme that jointly optimizes (i) BS transmit powers, (ii) power splitting factors for energy harvesting and information processing at the relays, and (iii) relay transmit powers. To solve the highly non-convex problem formulation of sum-rate maximization, we propose to apply the successive convex approximation (SCA) approach and devise an iterative algorithm based on geometric programming. The proposed algorithm transforms the nonconvex problem into a sequence of convex problems, each of which is solved very efficiently by the interior-point method. We prove that our developed algorithm converges to an optimal solution that satisfies the Karush-Kuhn-Tucker conditions of the original nonconvex problem. Numerical results confirm that our joint optimization solution substantially improves the network performance, compared to the existing solution wherein only the received power splitting factors at the relays are optimized.

We consider wireless-powered amplify-and-forward relaying in cooperative communications and propose block-wise time-switching based energy harvesting protocol to implement wireless energy harvesting (EH) and information transmission (IT) at the energy constrained relay node. The time-switching EH protocol switches the relay operation between EH and IT such that during EH, relay harvests energy through the received radio-frequency signal from the source and during IT, the relay receives information signal from the source and uses the harvested energy to amplify and forward source signal to the destination. In our proposed block-wise time-switching EH protocol, the whole transmission block time is used for either EH or IT. The attractive feature of our proposed protocol is that the relay transmits at preset fixed transmit power and no channel state information is required either by the source or relay node. We derive exact expression of the analytical throughput for the proposed protocol and verify it through simulation. In addition, we show that our proposed protocol outperforms the existing time-switching EH protocol because it allows efficient use of resources by intelligently switching between EH and IT in an online fashion.

In this paper, we build a unified analytical framework that allows for analysis and comparison of three device-to-device (D2D) mode selection schemes proposed in the literature to date, namely the distance cut-off scheme, the link gain scheme and the guard zone scheme. In the framework we adopt Poisson point process (PPP) assumptions to model the cellular and D2D interference, respectively. Using stochastic geometry, we derive easy to implement expressions for the success probability at a typical base station (BS) and a typical D2D receiver (RX) in an underlay in-band D2D-enabled single tier cellular network. Comparing the derived analytical results with simulations, we show that the PPP assumptions are accurate for the success probability at the BS. Moreover, they provide a good approximation for the success probability at the D2D RX when the D2D RX is located close to the cell edge. Furthermore, the distance cut-off scheme generally outperforms other mode selection schemes.

This paper analyzes the performance of the primary users (PUs) and secondary users (SUs) in an arbitrarily-shaped underlay cognitive network. In order to meet the interference threshold requirement for a primary receiver at an arbitrary location, we consider different SU activity protocols that limit the number of active SUs. We propose a framework, based on the moment-generating function of the interference due to a random SU, to analytically compute the outage probability in the primary network, as well as the average number of active SUs in the secondary network. We also propose a cooperation-based SU activity protocol in the underlay cognitive network that includes the existing threshold-based protocol as a special case. We study the average number of active SUs for the different SU activity protocols, subject to a given outage probability constraint at the PU, and we employ it as an analytical approach to compare the effect of different SU activity protocols on the performance of the primary and secondary networks.

We consider wireless-powered amplify-and-forward and decode-and-forward relaying in cooperative communications, where an energy constrained relay node first harvests energy through the received radio-frequency signal from the source and then uses the harvested energy to forward the source information to the destination node. We propose time-switching based energy harvesting (EH) and information transmission (IT) protocols with two modes of EH at the relay. For continuous time EH, the EH time can be any percentage of the total transmission block time. For discrete time EH, the whole transmission block is either used for EH or IT. The proposed protocols are attractive because they do not require channel state information at the transmitter side and enable relay transmission with preset fixed transmission power. We derive analytical expressions of the achievable throughput for the proposed protocols. The derived expressions are verified by comparison with simulations and allow the system performance to be determined as a function of the system parameters. Finally, we show that the proposed protocols outperform the existing fixed time duration EH protocols in the literature, since they intelligently track the level of the harvested energy to switch between EH and IT in an online fashion, allowing efficient use of resources.

This paper characterizes the aggregate interference at the primary user (PU) due to M secondary users (SUs) in an underlay cognitive network, where appropriate SU activity protocols are employed in order to limit the interference generated by the SUs. Different from prior works, we assume that the PU can be located anywhere inside an arbitrarily-shaped convex network region. Using the moment generating function (MGF) of the interference from a random SU, we derive general expressions for the n-th moment and the n-th cumulant of the aggregate interference for guard zone and multiple-threshold SU activity protocols. Using the cumulants, we study the convergence of the distribution of the aggregate interference to a Gaussian distribution. In addition, we compare the well-known closed-form distributions in the literature to approximate the complementary cumulative distribution function (CCDF) of the aggregate interference. Our results show that care must be undertaken in approximating the aggregate interference as a Gaussian distribution, even for a large number of SUs, since the convergence is not monotonie in general. In addition, the shifted lognormal distribution provides the overall best CCDF approximation, especially in the distribution tail region, for arbitrarily-shaped network regions.

This paper investigates spectral filtering using isotropic spectral windows, which is a computationally efficient method of spatial smoothing on the sphere. We propose a Slepian eigenfunction window, which is obtained as a solution of the concentration problem on the sphere, as a good choice of the window function. We also unify a comprehensive set of quantitative tools, both spatial and spectral, to assess and compare the performance of different smoothing windows (i.e., smoothers). We analyze and compare the performance of the proposed window against the two best available candidates in the literature: von-Hann window and von Mises-Fisher distribution window. We establish that the latter window includes the popular Gauss window as a subcase. We show that the Slepian eigenfunction window has the smallest spatial variance (better spatial localization) and the smallest side-lobe level.

In this paper, we consider a decode-and-forward (DF) relaying network based on wireless energy harvesting. The energy constrained relay node first harvests energy through radio-frequency (RF) signals from the source node. Next, the relay node uses the harvested energy to forward the decoded source information to the destination node. The source node transfers energy and information to the relay node through two mechanisms, i) time switching-based relaying (TSR) and ii) power splitting-based relaying (PSR). Considering wireless energy harvesting constraint at the relay node, we derive the exact analytical expressions of the achievable throughput and ergodic capacity of a DF relaying network for both TSR and PSR schemes. Through numerical analysis, we study the throughput performance of the overall system for different system parameters, such as energy harvesting time, power splitting ratio, and signal-to-noise-ratio (SNR). In particular, the throughput performance of the PSR scheme outperforms the throughput performance of the TSR scheme for a wide range of SNRs.

Smallcell is emerging as a cost-effective solution for satisfying the huge demands of mobile data. It can be deployed at any place where mobile traffic is required without the need for cell planning. However, coexistence of many uncontrolled smallcells using the same licensed frequency band can result in serious interference problems. In order to utilize smallcell efficiently, it is highly desirable that the smallcell can self-organize the network and mitigate interference automatically. In this paper, we propose a dynamic fractional frequency reuse (FFR) method for reducing the intercell interference automatically and improving the spectral efficiency. Key features of the proposed method are sub-band optimization with a central manner and sub-band size adjustment with a distributed manner. The proposed method has a low complexity and can be implemented as a feature of a self-organizing network (SON) in smallcell. Simulation results verify the effectiveness of the proposed method.

This paper analyzes the outage performance in finite wireless networks. Unlike most prior works, which either assumed a specific network shape or considered a special location of the reference receiver, we propose two general frameworks for analytically computing the outage probability at any arbitrary location of an arbitrarily-shaped finite wireless network: (i) a moment generating function-based framework which is based on the numerical inversion of the Laplace transform of a cumulative distribution and (ii) a reference link power gain-based framework which exploits the distribution of the fading power gain between the reference transmitter and receiver. The outage probability is spatially averaged over both the fading distribution and the possible locations of the interferers. The boundary effects are accurately accounted for using the probability distribution function of the distance of a random node from the reference receiver. For the case of the node locations modeled by a Binomial point process and Nakagami-m fading channel, we demonstrate the use of the proposed frameworks to evaluate the outage probability at any location inside either a disk or polygon region. The analysis illustrates the location-dependent performance in finite wireless networks and highlights the importance of accurately modeling the boundary effects.

Oscillator phase noise (PHN) and carrier frequency offset (CFO) can adversely impact the performance of orthogonal frequency division multiplexing (OFDM) systems, since they can result in inter carrier interference and rotation of the signal constellation. In this paper, we propose an expectation conditional maximization (ECM) based algorithm for joint estimation of channel, PHN, and CFO in OFDM systems. We present the signal model for the estimation problem and derive the hybrid Cramér-Rao lower bound (HCRB) for the joint estimation problem. Next, we propose an iterative receiver based on an extended Kalman filter for joint data detection and PHN tracking. Numerical results show that, compared to existing algorithms, the performance of the proposed ECM-based estimator is closer to the derived HCRB and outperforms the existing estimation algorithms at moderate-to-high signal-to-noise ratio (SNR). In addition, the combined estimation algorithm and iterative receiver are more computationally efficient than existing algorithms and result in improved average uncoded and coded bit error rate (BER) performance.

In this paper, we investigate the performance of compressive sampling (CS) for ECG compression in telecardiology, when the signal acquisition is noisy and unavoidable body movements lead to varying heartbeat rate and sparsity of the signal. We show analytically that CS recovery noise does not scale linearly with the input noise. Hence, it is not easy to reduce the adverse impact of noise in CS. Additionally, any variation in the heartbeat rate changes the sparsity and can adversely affect compression. We compare the performance of CS with thresholding discrete wavelet transform (TH-DWT), which is the best technique for real-time ECG compression. We show that CS is quite sensitive to sparsity and compression ratio, while the reconstruction quality of TH-DWT is quite stable. Our results suggest that while CS is an attractive option for telecardiology due to its encoder simplicity, caution should be exercised in applying it for ECG signal compression.

In this study, we analyse the error performance of decode and forward (DF) and amplify and forward (AF) multi‐way relay networks (MWRNs). The authors consider a MWRN with pair‐wise data exchange protocol using binary phase shift keying (BPSK) modulation in both additive white Gaussian noise (AWGN) and Rayleigh fading channels. The authors quantify the possible error events in an L ‐user DF or AF MWRN and derive accurate asymptotic bounds on the probability for the general case that a user incorrectly decodes the messages of exactly k ( k ∈ [1, L − 1]) users. They show that at high signal‐to‐noise ratio (SNR), the higher order error events ( k ≥ 3) are less probable in AF MWRN, but all error events are equally probable in a DF MWRN. They derive the average BER of a user in a DF or AF MWRN in both AWGN and Rayleigh fading channels under high SNR conditions. Simulation results validate the correctness of the derived expressions. The authors results show that at medium to high SNR, DF MWRN provides better error performance than AF MWRN in AWGN channels even with a large number of users (e.g. L = 100). Whereas, AF MWRN outperforms DF MWRN in Rayleigh fading channels even for much smaller number of users (e.g. L > 10).

For distributed multi-user and multi-relay cooperative networks, the received signal may be affected by multiple timing offsets (MTOs) and multiple channels that need to be jointly estimated for successful decoding at the receiver. This paper addresses the design of optimal training sequences for efficient estimation of MTOs and multiple channel parameters. A new hybrid Cramer-Rao lower bound (HCRB) for joint estimation of MTOs and channels is derived. Subsequently, by minimizing the derived HCRB as a function of training sequences, three training sequence design guidelines are derived and according to these guidelines, two training sequences are proposed. In order to show that the proposed design guidelines also improve estimation accuracy, the conditional Cramer-Rao lower bound (ECRB), which is a tighter lower bound on the estimation accuracy compared to the HCRB, is also derived. Numerical results show that the proposed training sequence design guidelines not only lower the HCRB, but they also lower the ECRB and the mean-square error of the proposed maximum a posteriori estimator. Moreover, extensive simulations demonstrate that application of the proposed training sequences significantly lowers the bit-error rate performance of multi-relay cooperative networks when compared to training sequences that violate these design guidelines.

An emerging solution for prolonging the lifetime of energy constrained relay nodes in wireless networks is to avail the ambient radio-frequency (RF) signal and to simultaneously harvest energy and process information. In this paper, an amplify-and-forward (AF) relaying network is considered, where an energy constrained relay node harvests energy from the received RF signal and uses that harvested energy to forward the source information to the destination. Based on the time switching and power splitting receiver architectures, two relaying protocols, namely, i) time switching-based relaying (TSR) protocol and ii) power splitting-based relaying (PSR) protocol are proposed to enable energy harvesting and information processing at the relay. In order to determine the throughput, analytical expressions for the outage probability and the ergodic capacity are derived for delay-limited and delay-tolerant transmission modes, respectively. The numerical analysis provides practical insights into the effect of various system parameters, such as energy harvesting time, power splitting ratio, source transmission rate, source to relay distance, noise power, and energy harvesting efficiency, on the performance of wireless energy harvesting and information processing using AF relay nodes. In particular, the TSR protocol outperforms the PSR protocol in terms of throughput at relatively low signal-to-noise-ratios and high transmission rates.

In this paper an efficient energy curtailment scheme is studied, which enables the power users of a smart grid network to decide on the reduction in energy supplied to them in the event of a power outage in the system. Considering the advantages of a two-way communications infrastructure for any future smart grid, a non-cooperative generalized Nash game is proposed where the players are users of power in the network. They adopt a strategy to choose the amount of reduction in energy supplied to them based on their energy requirements so as to minimize the total cost incurred to the system due to the power outage (i.e., social optimality). The game is modeled as a variational inequality problem, and it is shown that the socially optimum solution is obtained at the variational equilibrium of the energy curtailment game. An algorithm that enables the users to efficiently reach this equilibrium is proposed. Simulation results show that the proposed game yields an improvement of about 15% on average, in terms of average total cost reduction, compared to a standard equal power curtailment scheme.

The ambiguity function and the Wigner distribution are fundamental tools in the time-frequency analysis. In this paper, we present an analog of the ambiguity function and the Wigner distribution for signals on the sphere. First, we formulate the ambiguity function for signals on the sphere which represents the signals in joint spatio-spectral domain and derive an inversion operation to obtain the signal from its ambiguity function. Next, we formulate the Wigner distribution for azimuthally symmetric signals on the sphere as a two dimensional spherical harmonics transform of the ambiguity function. We provide the matrix formulation of the Wigner distribution and discuss some of its useful properties. Finally, we illustrate the use of Wigner distribution for spatial and/or spectral localization of a signal in joint spatio-spectral domain. The obtained results provide the first step in designing more sophisticated transforms on the sphere.

The uncertainty principle is an important and powerful tool, with many applications in signal processing. This paper presents two concentration uncertainty principles for signals on the sphere which relate the localization of the concentration of a signal in spatial and spectral domains, as an analogue of the general Donoho and Stark uncertainty principles in time-frequency analysis. Using the spherical and spectral truncation operators, we derive the L1-norm and L2-norm uncertainty principles which respectively relate the signal concentration in spatial and spectral domains as absolute value and the energy of a signal. We also analyze the sharpness of the bound imposed by the derived L2-norm uncertainty principle. The proposed uncertainty measures can be applied to signal processing problems on the sphere.

In this paper, we consider the problem of signal extrapolation for discrete (i.e., sampled) signals on the sphere. We propose conjugate gradient based algorithm for estimating a signal on the sphere from limited or incomplete measurements in a spatial domain. We prove that the proposed algorithm is guaranteed to converge and show that it has faster convergence compared to the Papoulis algorithm. The results also show that the incomplete measurements distributed in different non-connected spatial regions yield better extrapolation results, compared to the connected region case.

In cooperative networks, multiple carrier frequency offsets (MCFOs) and multiple timing offsets (MTOs) originate due to multiple distributed nodes. In this paper, algorithms for joint estimation of these parameters and channels in amplify-and-forward (AF) relaying networks are proposed. A new training model and transceiver structure at the relays for achieving synchronization throughout the network is devised. New exact closed-form expressions for the Cramér-Rao lower bounds (CRLBs) for the multi-parameter estimation problem are derived. An estimation method is proposed for jointly estimating MCFOs, MTOs, and channel gains at the destination based on space-alternating generalized expectation maximization (SAGE) and compared to a computationally-intensive least squares (LS) approach. The proposed estimator's performance is shown to be close to the CRLB at mid-to-high signal-to-noise ratio (SNR) resulting in significant cooperative performance gains in the presence of practical impairments.

This paper proposes a framework for joint blind timing and carrier offset estimation and data detection using a Sequential Importance Sampling (SIS) particle filter in Additive White Gaussian Noise (AWGN) channels. We assume baud rate sampling and model the intractable posterior probability distribution functions for sampling timing and carrier offset particles using beta distributions. To enable the SIS approach to estimate static synchronization parameters, we propose new resampling guidelines for dealing with the degeneracy problem and fine tuning the estimated values. We derive the Weighted Bayesian Cramer Rao Bound (WBCRB) for joint timing and carrier offset estimation, which takes into account the prior distribution of the estimation parameters and is an accurate lower bound for all considered Signal to Noise Ratio (SNR) values. Simulation results are presented to corroborate that the Mean Square Error (MSE) performance of the proposed algorithm is close to optimal at higher SNR values (above 20 dB). In addition, the bit error rate performance approaches that of the perfectly synchronized case for small unknown carrier offsets and any unknown timing offset. The advantage of our particle filter algorithm, compared to existing techniques, is that it can work for the full range acquisition of carrier offsets.

This paper considers the problem of construction of low-pass filters on the unit sphere, which has wide ranging applications in the processing of signals on the unit sphere. We propose a design criterion for the construction of strictly bandlimited low-pass filters in the spectral domain with optimal concentration in the specified polar cap region in the spatial domain. Our approach uses the weighted sum of the first optimally concentrated eigenfunctions from appropriately formulated Slepian concentration problems on the sphere. Furthermore, in order to reduce the computational complexity of the proposed algorithm, we develop a closed-form expression to accurately model these eigenfunctions. We illustrate the construction of low-pass filters using the proposed approach and demonstrate the advantage of our method approach compared to a diffusion based approach in the literature in terms of control over both bandwidth in the spectral domain and concentration in the spatial domain.

This paper proposes a new blind algorithm, based on Mixture Kalman Filtering (MKF), for joint carrier recovery and channel estimation in time-selective Rayleigh fading channels. MKF is a powerful tool for estimating unknown parameters in non-linear, non-Gaussian, real-time applications. We use a combination of Kalman filtering and Sequential Monte Carlo Sampling to estimate the channel fading coefficients and joint posterior probability density of the unknown carrier offset and transmitted data respectively. We study the effect of Signal to Noise Ratio (SNR) and doppler shift on Mean Square Error (MSE) and Bit Error Rate (BER) performance of the proposed algorithm through computer simulations. The results show that BER of the proposed algorithm achieves the theoretical performance slope for the full acquisition range of normalized carrier frequency offset.

Synchronization in Decode and Forward (DF) cooperative communication systems is a complex and challenging task requiring estimation of many independent timing and carrier offsets at each relay in the broadcasting phase and multiple timing and carrier offsets at the destination in the relaying phase. This paper presents a scheme for blind channel, timing and carrier offset estimation in a DF cooperative system with one source, M relays and one destination equipped with N antennas. In particular, we exploit blind source separation at the destination to convert the difficult problem of jointly estimating multiple synchronization parameters in the relaying phase into more tractable sub-problems of estimating many individual timing and carrier offsets for the independent relays. We also modify and propose a criteria for best relay selection at the destination. Simulation results demonstrate the excellent end-to-end Bit Error Rate (BER) performance of the proposed blind scheme with relay selection, which is shown to achieve the maximum diversity order with M = 4 relays using N = 5 antennas at the destination. The presented work is a complete solution to blind synchronization and channel estimation in DF cooperative communication systems.

This study addresses the problem of joint blind timing and carrier synchronisation in a (distributed-M)×N antenna system where the objective is to estimate the M carrier offsets, the M timing offsets and to recover the transmitted symbols for each of the M users given only the measured signal at the N antennas of the receiver. The authors propose a modular receiver structure that exploits blind source separation to reduce the problem into more tractable sub-problems of estimating individual timing and carrier offsets for multiple users. This leads to a robust solution of low complexity. The authors investigate the performance of the estimators analytically using modified Cramer–Rao bounds and computer simulations. The results show that the proposed receiver exhibits robust performance over a wide range of parameter values, even with worst-case Doppler of 200–300 Hz and frame size as small as 400 symbols. This work is relevant to future wireless networks and is a complete solution to the problem of estimating multiple timing and carrier offsets in distributed multiple input multiple output (MIMO) communication systems.

Connectivity is a fundamental requirement in the planning, design and evaluation of vehicular ad hoc networks (VANET). In this paper, we propose a new equivalent speed parameter and develop an analytical model to explain the effect of vehicle mobility on the connectivity of highway segments in a VANET. We prove that the equivalent speed is different from the average vehicle speed and it decreases as the standard deviation of the vehicle speed increases. Using the equivalent speed we derive a novel analytical expression for the average number of vehicles on a highway segment, which allows us to accurately predict the network 1 connectivity. We verify the correctness of our analytical approach by comparing the numerical results with simulations. The results show that increasing the average vehicle speed increases the equivalent speed, which leads to a decrease in the average number of vehicles on a highway segment and consequently degrades connectivity. On the other hand increasing the standard deviation of the vehicle speed decreases the equivalent speed, which leads to an increase in the average number of vehicles on a highway segment and consequently improves connectivity. The results also show that vehicles in a VANET can adaptively choose their transmission range to ensure network connectivity in highway segments while minimising power consumption.

In this letter, we consider multiple-input single-output (MISO) systems with two-way training based transmission. We focus on the long-term system performance and study the optimal power allocation between reverse training, forward training and data transmission. We derive closed-form solutions for the optimal power allocation using high signal-to-noise ratio (SNR) approximations, and show that they achieve near optimal performance in terms of symbol error rate (SER) for different modulation schemes over a wide range of SNR values.

In this paper, we consider multiple-input multiple-output (MIMO) communication systems with combined channel covariance feedback (CCF) and channel gain feedback (CGF), hereafter called hybrid CCF-CGF systems. Using an ergodic capacity lower bound as the figure of merit, we investigate the optimal training and data transmission strategies as well as the optimal transmit resource allocation. We prove that the optimal structure for data transmission follows a water-filling solution according to the estimated channel gains, rotated and truncated into the trained eigen-directions. We analytically find the range of the optimal training length. Through numerical evaluations we also show that a closed-form solution of the training power allocation achieves near optimal performance. Finally, we show that the capacity of hybrid CCF-CGF systems can be significantly increased by adding extra transmit antennas without increasing the training resources or feedback overhead.

We study the optimal antenna configuration (i.e. number of transmit and receive antennas) for multiple-input multiple-output systems in pilot-symbol-assisted modulation schemes with imperfect channel estimation. We assume block flat-fading channels and focus on a practical range of high signal-to-noise ratio. An ergodic capacity lower bound is used as the objective function to be maximized. We analytically study the capacity gain from adding extra antennas to the transmitter or to the receiver in two different scenarios. Our numerical results show that the optimal antenna configuration under imperfect channel estimation can be significantly different from that under perfect channel estimation assumption. In addition, we investigate the capacity gain from optimizing antenna configuration and find that the gain can be larger than that achieved by optimizing transmit power over pilot and data symbols, particularly for large block lengths.

Random beamforming, where each node selects a main beam direction without any coordination with other nodes, has been proposed as a simple technique to improve connectivity in wireless ad hoc networks. This paper presents an analytical model for evaluating the impact of random beamforming on the connectivity of wireless ad hoc networks in the presence of path loss and shadowing effects. We investigate the connectivity with random beamforming from the view points of a single node and the entire network. The correctness of our analytical approach is validated by comparing the analytical results with simulations. We show that for a path loss exponent of alpha<3, irrespective of shadowing effects, random beamforming improves both the local and overall connectivity compared to omnidirectional antennas.

We study the design parameters of pilot-symbol-assisted modulation (PSAM) schemes for spatially correlated single-input multiple-output (SIMO) systems in time-varying Gauss-Markov flat-fading channels. We use an information capacity lower bound as our figure of merit. We investigate the optimum design parameters, including the ratio of power allocated to the pilots and the fraction of time occupied by the pilots, for SIMO systems with different antenna sizes and with spatial channel correlation. Our main finding is that by optimally designing the training parameters for single-input single-output (SISO) systems, the same parameters can be used to achieve near optimum capacity in both spatially independent and correlated SIMO systems for the same fading rate and signal-to-noise ratio (SNR). In addition, we show that spatially independent channels give the lowest capacity at sufficiently low SNR. These findings provide insights into the design of practical PSAM systems.

Parametric channel models for multiple input multiple output (MIMO) systems have received much attention in recent years. This paper investigates the statistical properties of a parametric channel model for MIMO systems in an urban macro-cell environment. We assess the performance of the proposed channel model (in terms of autocorrelation, cross-correlation, level crossing rate, average fade duration and spatial correlation at base and mobile station) by comparison with statistical properties of a reference MIMO channel model. We investigate the important problem of how many subpaths are sufficient to accurately model the statistical behaviour of the MIMO wireless channel. Comparison of the simulated and reference model results provides new insights into the statistical accuracy of the parametric channel modelling approach.

This paper presents a parameterized physical channel model for evaluating the performance of smart antenna systems. The channel model assumes a single antenna at the mobile station and a uniform linear array of omni-directional antenna elements at the base station. It incorporates parameters such as azimuth angle of arrival and departure, angle spread, power delay profiles and Doppler frequency, which have critical influence on the performance of smart antennas. A new feature of the channel model is a thorough framework for the incorporation of user mobility. The proposed model allows for efficient and accurate representation of smart antenna channel aspects, while maintaining low complexity for system level simulations