A growing number of applications may benefit from embedding receiver with the capability to separate collided packets directly on a physical layer. For example, in IoT's random access channel scenario, a number of IoT devices are placed in the same cell, occasionally transmit messages, and are assigned non-orthogonal channel resources, giving rise to packet collisions. While channel access techniques for sharing a common channel are usually employed in most multi-user communication systems, they cause channel underutilization and increased latency. This paper considers a scenario where multiple users asynchronously transmit packets over a shared channel and are not subject to a random access mechanism, or the mechanism itself fails to prevent packet collisions. Assuming that each packet consists of a common preamble and information bearing payload, and experiences random delay, frequency offset, phase noise variation and block flat fading channel, we propose a packet separation algorithm which recovers collided packets, estimates their parameters and detects corresponding payload symbols. The performance of the proposed method is validated using simulations and benchmarked against bounds.

This paper studies energy efficiency (EE) and average throughput maximization for cognitive radio systems in the presence of unslotted primary users. It is assumed that primary user activity follows an ON-OFF alternating renewal process. Secondary users first sense the channel possibly with errors in the form of miss detections and false alarms, and then, start the data transmission only if no primary user activity is detected. The secondary user transmission is subject to constraints on collision duration ratio, which is defined as the ratio of average collision duration to transmission duration. In this setting, the optimal power control policy which maximizes the EE of the secondary users or maximizes the average throughput while satisfying a minimum required EE under average/peak transmit power and average interference power constraints is derived. Subsequently, low-complexity algorithms for jointly determining the optimal power level and frame duration are proposed. The impact of probabilities of detection and false alarm, transmit and interference power constraints on the EE, average throughput of the secondary users, optimal transmission power, and the collisions with primary user transmissions are evaluated. In addition, some important properties of the collision duration ratio are investigated. The tradeoff between the EE and average throughput under imperfect sensing decisions and different primary user traffic are further analyzed.

This paper studies energy-efficiency (EE) and throughput optimization in the presence of randomly arriving data and quality of service (QoS) constraints. For this purpose, maximum average arrival rates supported by transmitting signals with arbitrary input distributions are initially characterized in closed form by employing the effective bandwidth of time-varying sources (e.g., discrete-time Markov and Markov fluid sources, and discrete-time and continuous-time Markov modulated Poisson sources) and effective capacity of the time-varying wireless channel. Subsequently, EE is formulated as the ratio of the maximum average arrival rate to the total power consumption, in which circuit power is also taken into account. Following these characterizations, the optimal power control policies maximizing the EE or maximizing the throughput under a minimum EE constraint are obtained. Through numerical results, the performance of the optimal power control policies is evaluated for different signal constellations and is also compared with that of constant power transmission. The impact of QoS constraints, source characteristics, circuit power, input distributions on the EE, and the throughput is analyzed.

Inspired by the success of use of Virtual Machines (VMs) in cloud computing, virtualization has been introduced to wireless networking recently, enabling support for multiple Mobile Virtual Network Operators (MVNOs) via isolated slices over a shared wireless substrate. In this paper, we present design, implementation and evaluation of a novel cloud framework, R-Cloud, to enable radio resources at Base Stations (BSs) to be effectively allocated to multiple MVNOs as a service, which is referred to as Radio-as-a-Service (RaaS). R-Cloud employs a hybrid two-level control framework to enable coarse-grained and fine-grained resource allocation at the cloud and BS levels respectively. Specifically, R-Cloud not only coordinates resource allocation among BSs, MVNOs and mobile users across a Radio Access Network (RAN) and enables performance isolation by optimizing resource sharing and user association using an LP-rounding based algorithm at the cloud level; but also effectively schedule transmissions among multiple users at a BS using an optimal scheduling policy. We implemented R-Cloud over a wireless network testbed with software defined radios. It has been shown by extensive experimental and simulation results that R-Cloud can achieve effective RaaS over wireless networks and the proposed resource allocation algorithms outperform widely-used baseline solutions.

This paper presents an application of Gaussian process regression (GPR) to estimate a state of charge (SoC) of Lithium-ion (Li-ion) batteries with different kernel functions. One of the practical advantages of using GPR is that uncertainties in the estimates can be quantified, which enables reliability assessment of the SoC estimate. The inputs of GPR are voltage, current and temperature measurements of the battery and the output is an estimate of SoC. First, training is performed in which optimal hyperparameters of a kernel function are determined to model data properties. Then, the battery SoC is estimated online based on the trained model. The kernel function is the key element in the GPR model since it encodes the prior assumptions about the properties of the function being modeled. Therefore, the impact of kernel function selection on the estimation performance is analyzed using both simulated data and experimental data collected from a LiMn2O4/hard-carbon battery with a nominal capacity of 4.93Ah operating under constant charge and discharge currents.

This paper proposes optimal power adaptation schemes that maximize the energy efficiency (EE) in the presence of Markovian sources and finite-alphabet inputs subject to quality of service (QoS) constraints. First, maximum average arrival rates supported by transmitting signals with arbitrary input distributions are characterized in closed-form by employing the effective bandwidth of time-varying sources (e.g., discrete-time Markov and Markov fluid sources) and effective capacity of the time-varying wireless channel. Subsequently, EE is defined as the ratio of the maximum average arrival rate to the total power consumption, in which circuit power is also taken into account. Following these characterizations, an optimization problem is formulated to maximize the EE of the system, and optimal power control schemes are determined. Through numerical results, the performance of the optimal power control policies is evaluated for different signal constellations and is also compared with that of constant power transmission. The impact of QoS constraints, source characteristics, input distributions on the maximum achievable EE and the throughput is analyzed.

Integration of offloading technologies into mobile network operator's infrastructures that provide heterogeneous access services is a challenging task for mobile operators. A connectivity management platform is a key element for heterogeneous mobile network operators in order to enable optimal offloading. In this study, development and integration of a connectivity management platform that uses a novel multiple attribute decision making algorithms for efficient Wi-Fi Offloading in heterogeneous wireless networks is presented. The proposed platform collects several terminal and network level attributes via infrastructure and client Application Programming Interfaces (APIs) and decides the best network access technology to connect for requested users. Through experimentation, we provide details on the platform integration with service provider's network and sensitivity analysis of the multiple attribute decision making algorithm.

This paper studies energy-efficient power allocation schemes for secondary users in sensing-based spectrum sharing cognitive radio systems. It is assumed that secondary users first perform channel sensing possibly with errors and then initiate data transmission with different power levels based on sensing decisions. In this setting, the optimization problem is to maximize energy efficiency (EE) subject to peak/average transmission power constraints and peak/average interference constraints. By exploiting the quasi-concave property of the EE maximization problem, the original problem is transformed into an equivalent parameterized concave problem, and an iterative power allocation algorithm based on Dinkelbach's method is proposed. The optimal power levels are identified in the presence of different levels of channel side information (CSI) regarding the transmission and interference links at the secondary transmitter, namely, perfect CSI of both transmission and interference links, perfect CSI of the transmission link, imperfect CSI of the interference link, imperfect CSI of both links, or only statistical CSI of both links. Through numerical results, the impact of sensing performance, different types of CSI availability, and transmit and interference power constraints on the EE of the secondary users is analyzed.

In this paper, energy efficiency of hybrid automatic repeat request (HARQ) schemes with statistical queuing constraints is studied for both constant-rate and random Markov arrivals by characterizing the minimum energy per bit and wideband slope. In particular, two queuing models are considered. Specifically, when outage occurs, the transmitter keeps the packet, lowers its priority, and attempts to retransmit it later in the first queue model, while the packet is discarded and removed from the buffer in the second queue model. For both models, energy efficiency is investigated when outage constraints, statistical queuing constraints, and deadline constraints are imposed. The deadline constraint provides a limitation on the number of retransmissions or equivalently the number of HARQ rounds. Under these assumptions, closed-form expressions are obtained for the minimum energy per bit and wideband slope for HARQ with chase combining, and comparisons among different arrival models are made. For instance, it is shown that stricter queuing constraints and more bursty sources degrade the energy efficiency by lowering the wideband slope. In the numerical results, analytical characterizations are verified through simulations. Moreover, the impact of source variations/burstiness, deadline constraints, outage probability, and queuing constraints on the energy efficiency is analyzed.

This paper studies the optimal power control policy and frame duration that maximize the throughput of secondary users operating under transmit power, interference power, and collision constraints in the presence of unslotted primary users. It is assumed that primary user activity follows an ON-OFF alternating renewal process. Secondary users first sense the channel albeit with errors in the form of miss detections and false alarms, and then start the data transmission only if no primary user activity is detected. Under these assumptions, we determine the optimal power control policy subject to peak transmit power and average interference power constraints and propose a lowcomplexity algorithm for the joint optimization of the power level and frame duration under collision constraints.We further analyze some important properties of the collision duration ratio, which is defined as the ratio of average collision duration to transmission duration, and investigate the impact of the probabilities of detection and false alarm on the throughput, optimal transmission power, and the collisions with primary user transmissions.

This paper studies the performance of hierarchical-modulation-based image and video transmission in cognitive radio systems with imperfect channel sensing results under constraints on both transmit and interference power. Data intended for transmission is first compressed via source coding techniques and then divided into two priority classes, namely high priority (HP) data and low priority (LP) data, by taking into consideration the unequal importance of bits in the output codestream. After dividing the compressed data into packets of equal size, turbo coding is applied. Finally, the resulting packets are modulated using hierarchical quadrature amplitude modulation (HQAM). In this setting, closed-form bit error probability expressions for HP data and LP data are derived over Nakagami-m fading channels in the presence of sensing errors. Subsequently, the effects of probabilities of detection and false alarm on error rate performance of cognitive transmissions are evaluated. In addition, tradeoffs between the number of retransmissions and peak signal-to-noise ratio (PSNR) quality are analyzed numerically. Moreover, performance comparisons of multimedia transmission with conventional QAM and hierarchical QAM are carried out in terms of the received data quality and number of retransmissions.

This paper studies optimal power control policies that maximize the achievable rates of underlay cognitive radio systems with arbitrary input distributions under both peak/average transmit power and peak/average interference power constraints for general fading distributions. In particular, optimal power adaptation schemes are formulated and low-complexity optimal power control algorithms are proposed. Additionally, simpler approximations of optimal power control policies in the low-power regime are determined. By considering gamma distributed channel power gains of the interference link between the secondary transmitter and the primary receiver and of the transmission link between the secondary transmitter and the secondary receiver, closed-form expressions for the maximum achievable rate attained with optimal power control in the low-power regime are provided. Through numerical results, the impact of the fading severity of both interference and transmission links and transmit power and interference power constraints on the maximum achievable rate of the cognitive user for different practical constellations and Gaussian signals are investigated.

This paper studies optimal power control policies that maximize the achievable rates of underlay cognitive radio systems with arbitrary input distributions under both peak transmit/peak interference power constraints and peak transmit/average interference power constraints for general fading distributions. A low-complexity optimal power control algorithm is proposed. Also, the optimal power control policy in the low-power regime is analyzed. In particular, by considering gamma distributed channel power gains of the interference link between the secondary transmitter and the primary receiver and the transmission link between the secondary transmitter and the secondary receiver, closed-form expressions for the maximum achievable rate attained with the optimal power control strategy in the low-power regime are provided. Through numerical results, the impact of the fading severity of both interference and transmission links and transmit power and interference power constraints on the maximum achievable rate of the cognitive user for different constellations and Gaussian signals are investigated.

In this paper, the optimal power control policy that maximizes the effective capacity for arbitrary input distributions in fading channels subject to an average power constraint is studied. A low-complexity power control algorithm is proposed. In addition, energy efficiency is investigated by characterizing both the minimum energy per bit and wideband slope for arbitrary signaling in the low-power regime when channel side information (CSI) is available only at the receiver. With perfect CSI at both the transmitter and receiver, the optimal power adaptation strategy in this regime is also determined. Through numerical results, performance comparison with constant power scheme and optimal power adaptation strategy for different signal constellations and Gaussian signals is given. The impact of QoS constraints, input distributions, and average transmit power level on the proposed power control policy, maximum achievable effective capacity and energy efficiency is analyzed.

This paper studies the symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. Two different transmission schemes, namely sensing-based spectrum sharing (SSS) and opportunistic spectrum access (OSA), are considered. In both schemes, secondary users first perform channel sensing, albeit with possible errors. In SSS, depending on the sensing decisions, they adapt the transmission power level and coexist with primary users in the channel. On the other hand, in OSA, secondary users are allowed to transmit only when the primary user activity is not detected. Initially, for both transmission schemes, general formulations for the optimal decision rule and error probabilities are provided for arbitrary modulation schemes under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading, and the primary user's received faded signals at the secondary receiver has a Gaussian mixture distribution. Subsequently, the general approach is specialized to rectangular quadrature amplitude modulation (QAM). More specifically, the optimal decision rule is characterized for rectangular QAM, and closed-form expressions for the average symbol error probability attained with the optimal detector are derived under both transmit power and interference constraints. The effects of imperfect channel sensing decisions, interference from the primary user and its Gaussian mixture model, and the transmit power and interference constraints on the error rate performance of cognitive transmissions are analyzed.

In this paper, we consider a cognitive multiple access channel (MAC) in which the secondary users seek to communicate with the secondary base station in a spectrum-sharing environment. The base station selects only one secondary user, which maximizes the weighted difference between the channel power gains of the interference link and the cognitive transmission link. This selection strategy can also be specialized to selecting the secondary user that has either the least interference channel gain or the highest data channel gain. Consequently, the selected secondary user is subject to an interference power constraint in order to avoid harmful interference inflicted on the primary user. It is assumed that the primary user and secondary users operate under statistical quality of service (QoS) constraints imposed as limitations on the buffer size. In this setting, we characterize the effective capacity for both primary user and the secondary users under different selection methods. The impacts of the secondary user selection method, the number of secondary users, QoS constraints on the performance of both the primary user and secondary users are investigated. We interestingly show that for a large number of secondary users, the performance of the primary user is not affected by the selection method in the interference-limited regime.

In this paper, a cognitive multiple access channel in which the secondary transmitters seek to communicate with a common secondary receiver is considered. Before data transmission, it is assumed that cooperative spectrum sensing is performed with possible errors. For this channel, achievable rate regions are initially derived under two scenarios depending on how secondary transmitters access the channel. In the first scenario, secondary users can send data under both busy and idle sensing decisions by adapting the energy level of the transmitted signals according to the sensing result. In the second scenario, the secondary transmitters are not allowed to perform data transmission if the channel is sensed as busy. Subsequently, the performance in the low-power regime is analyzed by characterizing minimum energy per bit and slope regions in these scenarios. The impact of channel sensing performance (e.g., the probabilities of detection and false alarm, channel sensing duration) on achievable rate region, energy efficiency and slope region considering both scenarios are investigated.

In this paper, effective capacity of a two-way amplify and forward (AF) relay channel, in which two source nodes exchange their signals via a relay node, is analyzed. Depending on the availability of the channel side information (CSI), the relay, which operates in half-duplex mode, has a fixed-gain or variable-gain factor. It is assumed that source nodes are subject to individual statistical quality of service (QoS) constraints in the form of limitations on buffer size. Under these assumptions and constraints, a characterization of the throughput for source nodes is provided by obtaining closed-form expressions for lower bounds on the effective capacity. Performances of two-way fixed- gain and variable-gain AF relaying are compared. The relations between buffer constraints, transmission power, the location of the relay node and effective capacity are also studied.

In this paper, error rate performance of cognitive radio transmissions is studied in the presence of imperfect channel sensing decisions. It is assumed that cognitive users first perform channel sensing, albeit with possible errors. Then, depending on the sensing decisions, they select the transmission energy level and employ MI× MQrectangular quadrature amplitude modulation (QAM) for data transmission over a fading channel. In this setting, the optimal decision rule is formulated under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading. It is shown that the thresholds for optimal detection at the receiver are the midpoints between the signals under any sensing decision. Subsequently, minimum average error probability expressions for M-ary pulse amplitude modulation (M-PAM) and MI× MQrectangular QAM transmissions attained with the optimal detector are derived. The effects of imperfect channel sensing decisions on the average symbol error probability are analyzed.

In this paper, throughput achieved in cognitive radio channels with finite blocklength codes under buffer limitations is studied. Cognitive users first determine the activity of the primary users' through channel sensing and then initiate data transmission at a power level that depends on the channel sensing decisions. It is assumed that finite blocklength codes are employed in the data transmission phase. Hence, errors can occur in reception and retransmissions can be required. Primary users' activities are modeled as a two-state Markov chain and an eight-state Markov chain is constructed in order to model the cognitive radio channel. Channel state information (CSI) is assumed to be perfectly known by either the secondary receiver only or both the secondary transmitter and receiver. In the absence of CSI at the transmitter, fixed-rate transmission is performed whereas under perfect CSI knowledge, for a given target error probability, the transmitter varies the rate according to the channel conditions. Under these assumptions, throughput in the presence of buffer constraints is determined by characterizing the maximum constant arrival rates that can be supported by the cognitive radio channel while satisfying certain limits on buffer violation probabilities. Tradeoffs between throughput, buffer constraints, coding blocklength, and sensing duration for both fixed-rate and variable-rate transmissions are analyzed numerically. The relations between average error probability, sensing threshold and sensing duration are studied in the case of variable-rate transmissions.