Multi-packet reception (MPR) has been recognized as a powerful capacity-enhancement technique for randomaccess wireless local area networks (WLANs). As is common with all random access protocols, the wireless channel is often under-utilized in MPR WLANs. In this paper, we propose a novel multi-round contention random-access protocol to address this problem. This work complements the existing randomaccess methods that are based on single-round contention. In the proposed scheme, stations are given multiple chances to contend for the channel until there are a sufficient number of ?winning? stations that can share the MPR channel for data packet transmission. The key issue here is the identification of the optimal time to stop the contention process and start data transmission. The solution corresponds to finding a desired tradeoff between channel utilization and contention overhead. In this paper, we conduct a rigorous analysis to characterize the optimal strategy using the theory of optimal stopping. An interesting result is that the optimal stopping strategy is a simple threshold-based rule, which stops the contention process as soon as the total number of winning stations exceeds a certain threshold. Compared with the conventional single-round contention protocol, the multi-round contention scheme significantly enhances channel utilization when the MPR capability of the channel is small to medium. Meanwhile, the scheme automatically falls back to single-round contention when the MPR capability is very large, in which case the throughput penalty due to random access is already small even with single-round contention.
In related work, @cite_0 attempts to enhance the utilization of MPR channels by allowing stations to count down and transmit as long as there are less than @math ongoing transmissions in the air. To do this, one key assumption is that a station is able to detect the number of ongoing transmissions using an energy detector. This assumption, however, is not valid in wireless networks, where the received energy from each transmitting station is random and unknown a priori.
@cite_0
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In a cognitive radio network, the secondary users are allowed to utilize the frequency bands of primary users when these bands are not currently being used. To support this spectrum reuse functionality, the secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance in cognitive radio networks. There are two parameters associated with spectrum sensing: probability of detection and probability of false alarm. The higher the probability of detection, the better the primary users are protected. However, from the secondary users' perspective, the lower the probability of false alarm, the more chances the channel can be reused when it is available, thus the higher the achievable throughput for the secondary network. In this paper, we study the problem of designing the sensing duration to maximize the achievable throughput for the secondary network under the constraint that the primary users are sufficiently protected. We formulate the sensing-throughput tradeoff problem mathematically, and use energy detection sensing scheme to prove that the formulated problem indeed has one optimal sensing time which yields the highest throughput for the secondary network. Cooperative sensing using multiple mini-slots or multiple secondary users are also studied using the methodology proposed in this paper. Computer simulations have shown that for a 6 MHz channel, when the frame duration is 100 ms, and the signal-to-noise ratio of primary user at the secondary receiver is -20 dB, the optimal sensing time achieving the highest throughput while maintaining 90 detection probability is 14.2 ms. This optimal sensing time decreases when distributed spectrum sensing is applied. Cellular radios consume more power and suffer reduced data rate when the signal is weak. According to our measurements, the communication energy per bit can be as much as 6x higher when the signal is weak than when it is strong. To realize energy savings, applications must preferentially communicate when the signal is strong, either by deferring non-urgent communication or by advancing anticipated communication to coincide with periods of strong signal. Allowing applications to perform such scheduling requires predicting signal strength, so that opportunities for energy-efficient communication can be anticipated. Furthermore, such prediction must be performed at little energy cost. In this paper, we make several contributions towards a practical system for energy-aware cellular data scheduling called Bartendr. First, we establish, via measurements, the relationship between signal strength and power consumption. Second, we show that location alone is not sufficient to predict signal strength and motivate the use of tracks to enable effective prediction. Finally, we develop energy-aware scheduling algorithms for different workloads - syncing and streaming - and evaluate these via simulation driven by traces obtained during actual drives, demonstrating energy savings of up to 60 . Our experiments have been performed on four cellular networks across two large metropolitan areas, one in India and the other in the U.S. Wireless networks can be self-sustaining by harvesting energy from ambient radio-frequency (RF) signals. Recently, researchers have made progress on designing efficient circuits and devices for RF energy harvesting suitable for low-power wireless applications. Motivated by this and building upon the classic cognitive radio (CR) network model, this paper proposes a novel method for wireless networks coexisting where low-power mobiles in a secondary network, called secondary transmitters (STs), harvest ambient RF energy from transmissions by nearby active transmitters in a primary network, called primary transmitters (PTs), while opportunistically accessing the spectrum licensed to the primary network. We consider a stochastic-geometry model in which PTs and STs are distributed as independent homogeneous Poisson point processes (HPPPs) and communicate with their intended receivers at fixed distances. Each PT is associated with a guard zone to protect its intended receiver from ST's interference, and at the same time delivers RF energy to STs located in its harvesting zone. Based on the proposed model, we analyze the transmission probability of STs and the resulting spatial throughput of the secondary network. The optimal transmission power and density of STs are derived for maximizing the secondary network throughput under the given outage-probability constraints in the two coexisting networks, which reveal key insights to the optimal network design. Finally, we show that our analytical result can be generally applied to a non-CR setup, where distributed wireless power chargers are deployed to power coexisting wireless transmitters in a sensor network. We consider the problem of determining the capacity of an energy-harvesting transmitter with finite battery communicating over a discrete memoryless channel. When the battery is unlimited, or zero, the capacity has been determined, but it remains unknown for a finite non-zero battery. In this paper we assume that the harvested energy at each time, the total battery storage, and the transmitter signal energy at each time can be quantized to the same unit (i.e., the same energy interval). Under this assumption, we show that the capacity can be described using the Verdu-Han general framework. If we further assume that the transmitted symbol at each time depends only on the energy currently available, and not on the entire past history of energy harvests and symbols transmitted, then we show that the system reduces to a finite state channel (FSC) with the required ergodic and Markov properties so that lower bounds on the capacity can be readily numerically computed. We conjecture that our numerical bounds are tight. Our numerical results indicate that even the minimal possible battery storage can reap a significant fraction of the infinite battery capacity. The integration of wireless power transfer (WPT) with the low-power backscatter communications provides a promising way to sustain battery-less wireless networks. In this paper, we consider a backscatter communication network wirelessly powered by a power beacon station (PBS). Each backscatter radio uses the harvested energy to power its data transmissions, in which some other radios can help as the wireless relays with an aim to improve throughput performance by cooperative transmission. Under this setting, we formulate a throughput maximization problem to jointly optimize WPT and the relay strategy of the backscatter radios. An iterative algorithm with reduced complexity and communication overhead is proposed to decompose the original problem into two sub-problems distributed at the PBS and the backscatter receiver. Moreover, we take uncertain channel information into consideration and formulate robust counter-parts of the throughput maximization problem when either the backscatter or relay channel is subject to estimation errors. The difficulty of the robust counter-part lies in the coupling of the PBS’ power allocation and relay strategy in matrix inequalities, which is addressed by alternating optimization with guaranteed convergence. Numerical results reveal that the cooperative relay strategy of the backscatter radios significantly improves the throughput performance. In this paper, we investigate schemes for energy-efficient multi-hop broadcasting in large-scale dense Wireless Sensor Networks. We begin with an initial simplified study of the schemes for relay selection. Our first finding is that MPR-based (Multipoint Relay) mechanisms work poorly in a dense network while the recently proposed Multicast Protocol for Low power and Lossy Networks (MPL) protocol based on Trickle performs better. However, Trickle requires to overhear packet retransmissions in the vicinity, while sensor nodes try to avoid overhearing by periodically waking up and going to sleep to save energy. We propose Beacon-based Forwarding Tree (BFT), a new scheme that achieves similar performance to MPL, although it fits better the case of nodes with low radio duty cycling MACs of the type of beacon-enabled IEEE 802.15.4. Our scheme also guarantees network coverage and its optimized version results in the shortest path distance to the broadcast source at a cost of lesser load mitigation. We compare and discuss the measured performance of MPL on top of ContikiMAC and BFT over beacon-enabled 802.15.4 on a Contiki testbed. The experimental results of the comparisons show that BFT may achieve very good performance for a range of broadcast intensity, it has a predictable power consumption, a remarkable low power consumption for leaf nodes, and low loss rates. On the other hand, MPL over ContikiMAC can obtain very low duty cycles for low broadcast traffic. This paper considers the problem of power management and throughput maximization for energy neutral operation when using an energy harvesting sensor (EHS) to send data over a wireless link. The EHS is assumed to be able to harvest energy at a constant rate, and use a fixed part of the energy harvested in a slot for measuring the channel state. The rest of the energy harvested is available for transmission, however, it can be stored in an inefficient battery if it is not fully utilized. The key constraint that the EHS needs to satisfy is energy neutrality, i.e., the expected energy drawn from the battery should equal the expected energy deposited into the battery. In this scenario, two popular models for data transmission are contrasted: the constant bit rate (CBR) model and the variable bit rate (VBR) model. In the CBR model, it is assumed that the EHS are designed to transmit data at a constant rate (using a fixed modulation and coding scheme) but are power-controlled. In the VBR model, the EHS selects both the transmit power and the data rate of transmission in each slot based on the channel instantiation. A framework under which the system designer can optimize several parameters of the EHS that determine the average data rate performance when the channel is Rayleigh fading is developed. Using this framework, the two transmission schemes are contrasted. It is shown that, with the right choice of parameter settings, the CBR scheme can perform nearly as well as the VBR scheme at significantly lower complextiy. The usefulness and validity of the framework developed is illustrated through simulations for specific examples. In this paper, we address the problem of computing the probability that r out of n interfering wireless signals are "captured," i.e., received with sufficiently large Signal to Interference plus Noise Ratio (SINR) to correctly decode the signals by a receiver with multi-packet reception (MPR) and Successive Interference Cancellation (SIC) capabilities. We start by considering the simpler case of a pure MPR system without SIC, for which we provide an expression for the distribution of the number of captured packets, whose computational complexity scales with n and r. This analysis makes it possible to investigate the system throughput as a function of the MPR capabilities of the receiver. We then generalize the analysis to SIC systems. In addition to the exact expressions for the capture probability and the normalized system throughput, we also derive approximate expressions that are much easier to compute and provide accurate results in some practical scenarios. Finally, we present selected results for some case studies with the purpose of illustrating the potential of the proposed mathematical framework and validating the approximate methods. In this paper, we consider a wideband cognitive radio network (CRN) which can simultaneously sense multiple narrowband channels and thus aggregate the perceived available channels for transmission. We study the problem of designing the optimal spectrum sensing time and power allocation schemes so as to maximize the average achievable throughput of the CRN subject to the constraints of probability of detection and the total transmit power. The optimal sensing time and power allocation strategies are developed under two different total power constraints, namely, instantaneous power constraint and average power constraint. Finally, numerical results show that, under both cases, for a CRN with three 6 MHz channels, if the frame duration is 100 ms and the target probability of detection is 90 for the worst case signal-to-noise ratio of primary users being -12 dB, -15 dB and -20 dB, respectively, the optimal sensing time is around 6 ms and it is almost insensitive to the total transmit power. As the use of wireless sensor networks increases, the need for (energy-)efficient and reliable broadcasting algorithms grows. Ideally, a broadcasting algorithm should have the ability to quickly disseminate data, while keeping the number of transmissions low. In this paper we develop a model describing the message count in large-scale wireless sensor networks. We focus our attention on the popular Trickle algorithm, which has been proposed as a suitable communication protocol for code maintenance and propagation in wireless sensor networks. Besides providing a mathematical analysis of the algorithm, we propose a generalized version of Trickle, with an additional parameter defining the length of a listen-only period. This generalization proves to be useful for optimizing the design and usage of the algorithm. For single-cell networks we show how the message count increases with the size of the network and how this depends on the Trickle parameters. Furthermore, we derive distributions of inter-broadcasting times and investigate their asymptotic behavior. Our results prove conjectures made in the literature concerning the effect of a listen-only period. Additionally, we develop an approximation for the expected number of transmissions in multi-cell networks. All results are validated by simulations. One of the main requirements of cognitive radio systems is the ability to reliably detect the presence of licensed primary transmissions. Previous works on the problem of detection for cognitive radio have suggested the necessity of user cooperation in order to be able to detect at the low signal-to-noise ratios experienced in practical situations. We consider a system of cognitive radio users who cooperate with each other in trying to detect licensed transmissions. Assuming that the cooperating nodes use identical energy detectors, we model the received signals as correlated log-normal random variables and study the problem of fusing the decisions made by the individual nodes. We design a linear-quadratic (LQ) fusion strategy based on a deflection criterion for this problem, which takes into account the correlation between the nodes. Using simulations we show that when the observations at the sensors are correlated, the LQ detector significantly outperforms the counting rule, which is the fusion rule that is obtained by ignoring the correlation. We consider the minimization of the transmission completion time with a battery limited energy harvesting transmitter in an M-user AWGN broadcast channel where the transmitter is able to harvest energy from the nature, using a finite storage capacity rechargeable battery. The harvested energy is modeled to arrive (be harvested) at the transmitter during the course of transmissions at arbitrary time instants. The transmitter has fixed number of packets for each receiver. Due to the finite battery capacity, energy may overflow without being utilized for data transmission. We derive the optimal offline transmission policy that minimizes the time by which all of the data packets are delivered to their respective destinations. We analyze the structural properties of the optimal transmission policy using a dual problem. We find the optimal total transmit power sequence by a directional water-filling algorithm. We prove that there exist M-1 cut-off power levels such that user i is allocated the power between the i-1st and the ith cut-off power levels subject to the availability of the allocated total power level. Based on these properties, we propose an algorithm that gives the globally optimal offline policy. The proposed algorithm uses directional water-filling repetitively. Finally, we illustrate the optimal policy and compare its performance with several suboptimal policies under different settings. While cognitive radio enables spectrum-efficient wireless communication, radio frequency (RF) energy harvesting from ambient interference is an enabler for energy-efficient wireless communication. In this paper, we model and analyze cognitive and energy harvesting-based device-to-device (D2D) communication in cellular networks. The cognitive D2D transmitters harvest energy from ambient interference and use one of the channels allocated to cellular users (in uplink or downlink), which is referred to as the D2D channel, to communicate with the corresponding receivers. We investigate two spectrum access policies for cellular communication in the uplink or downlink, namely, random spectrum access (RSA) policy and prioritized spectrum access (PSA) policy. In RSA, any of the available channels including the channel used by the D2D transmitters can be selected randomly for cellular communication, while in PSA the D2D channel is used only when all of the other channels are occupied. A D2D transmitter can communicate successfully with its receiver only when it harvests enough energy to perform channel inversion toward the receiver, the D2D channel is free, and the signal-to-interference-plus-noise ratio @math at the receiver is above the required threshold; otherwise, an outage occurs for the D2D communication. We use tools from stochastic geometry to evaluate the performance of the proposed communication system model with general path-loss exponent in terms of outage probability for D2D and cellular users. We show that energy harvesting can be a reliable alternative to power cognitive D2D transmitters while achieving acceptable performance. Under the same @math outage requirements as for the non-cognitive case, cognitive channel access improves the outage probability for D2D users for both the spectrum access policies. When compared with the RSA policy, the PSA policy provides a better performance to the D2D users. Also, using an uplink channel provides improved performance to the D2D users in dense networks when compared to a downlink channel. For cellular users, the PSA policy provides almost the same outage performance as the RSA policy. The ability of a receiver to determine when a transmitter has changed location is important for energy conservation in wireless sensor networks, for physical security of radio-tagged objects, and for wireless network security in detection of replication attacks. In this paper, we propose using a measured temporal link signature to uniquely identify the link between a transmitter and a receiver. When the transmitter changes location, or if an attacker at a different location assumes the identity of the transmitter, the proposed link distinction algorithm reliably detects the change in the physical channel. This detection can be performed at a single receiver or collaboratively by multiple receivers. We record over 9,000 link signatures at different locations and over time to demonstrate that our method significantly increases the detection rate and reduces the false alarm rate, in comparison to existing methods. Wireless power transfer (WPT) is a promising new solution to provide convenient and perpetual energy supplies to wireless networks. In practice, WPT is implementable by various technologies such as inductive coupling, magnetic resonate coupling, and electromagnetic (EM) radiation, for short- mid- long-range applications, respectively. In this paper, we consider the EM or radio signal enabled WPT in particular. Since radio signals can carry energy as well as information at the same time, a unified study on simultaneous wireless information and power transfer (SWIPT) is pursued. Specifically, this paper studies a multiple-input multiple-output (MIMO) wireless broadcast system consisting of three nodes, where one receiver harvests energy and another receiver decodes information separately from the signals sent by a common transmitter, and all the transmitter and receivers may be equipped with multiple antennas. Two scenarios are examined, in which the information receiver and energy receiver are separated and see different MIMO channels from the transmitter, or co-located and see the identical MIMO channel from the transmitter. For the case of separated receivers, we derive the optimal transmission strategy to achieve different tradeoffs for maximal information rate versus energy transfer, which are characterized by the boundary of a so-called rate-energy (R-E) region. For the case of co-located receivers, we show an outer bound for the achievable R-E region due to the potential limitation that practical energy harvesting receivers are not yet able to decode information directly. Under this constraint, we investigate two practical designs for the co-located receiver case, namely time switching and power splitting, and characterize their achievable R-E regions in comparison to the outer bound. Energy harvesting is a promising solution to prolong the operation of energy-constrained wireless networks. In particular, scavenging energy from ambient radio signals, namely wireless energy harvesting (WEH), has recently drawn significant attention. In this paper, we consider a point-to-point wireless link over the narrowband flat-fading channel subject to time-varying co-channel interference. It is assumed that the receiver has no fixed power supplies and thus needs to replenish energy opportunistically via WEH from the unintended interference and or the intended signal sent by the transmitter. We further assume a single-antenna receiver that can only decode information or harvest energy at any time due to the practical circuit limitation. Therefore, it is important to investigate when the receiver should switch between the two modes of information decoding (ID) and energy harvesting (EH), based on the instantaneous channel and interference condition. In this paper, we derive the optimal mode switching rule at the receiver to achieve various trade-offs between wireless information transfer and energy harvesting. Specifically, we determine the minimum transmission outage probability for delay-limited information transfer and the maximum ergodic capacity for no-delay-limited information transfer versus the maximum average energy harvested at the receiver, which are characterized by the boundary of so-called "outage-energy" region and "rate-energy" region, respectively. Moreover, for the case when the channel state information (CSI) is known at the transmitter, we investigate the joint optimization of transmit power control, information and energy transfer scheduling, and the receiver's mode switching. The effects of circuit energy consumption at the receiver on the achievable rate-energy trade-offs are also characterized. Our results provide useful guidelines for the efficient design of emerging wireless communication systems powered by opportunistic WEH. The advance in RF energy transfer and harvesting technique over the past decade has enabled wireless energy replenishment for electronic devices, which is deemed as a promising alternative to address the energy bottleneck of conventional battery-powered devices. In this paper, by using a stochastic geometry approach, we aim to analyze the performance of an RF-powered wireless sensor in a downlink simultaneous wireless information and power transfer (SWIPT) system with ambient RF transmitters. Specifically, we consider the point-to-point downlink SWIPT transmission from an access point to a wireless sensor in a network, where ambient RF transmitters are distributed as a Ginibre α-determinantal point process (DPP), which becomes the Poisson point process when a approaches zero. In the considered network, we focus on analyzing the performance of a sensor equipped with the power-splitting architecture. Under this architecture, we characterize the expected RF energy harvesting rate of the sensor. Moreover, we derive the upper bound of both power and transmission outage probabilities. Numerical results show that our upper bounds are accurate for different value of a. Ambient radio frequency (RF) energy harvesting technique has recently been proposed as a potential solution for providing proactive energy replenishment for wireless devices. This paper aims to analyze the performance of a battery-free wireless sensor powered by ambient RF energy harvesting using a stochastic geometry approach. Specifically, we consider the point-to-point uplink transmission of a wireless sensor in a stochastic geometry network, where ambient RF sources, such as mobile transmit devices, access points and base stations, are distributed as a Ginibre @math -determinantal point process (DPP). The DPP is able to capture repulsion among points, and hence, it is more general than the Poisson point process (PPP). We analyze two common receiver architectures: separated receiver and time-switching architectures. For each architecture, we consider the scenarios with and without co-channel interference for information transmission. We derive the expectation of the RF energy harvesting rate in closed form and also compute its variance. Moreover, we perform a worst-case study which derives the upper bound of both power and transmission outage probabilities. Additionally, we provide guidelines on the setting of optimal time-switching coefficient in the case of the time-switching architecture. Numerical results verify the correctness of the analysis and show various tradeoffs between parameter setting. Lastly, we prove that the RF-powered sensor performs better when the distribution of the ambient sources exhibits stronger repulsion. Far-field microwave power transfer (MPT) will free wireless sensors and other mobile devices from the constraints imposed by finite battery capacities. Integrating MPT with wireless communications to support simultaneous wireless information and power transfer (SWIPT) allows the same spectrum to be used for dual purposes without compromising the quality of service. A novel approach is presented in this paper for realizing SWIPT in a broadband system where orthogonal frequency division multiplexing and transmit beamforming are deployed to create a set of parallel sub-channels for SWIPT, which simplifies resource allocation. Based on a proposed reconfigurable mobile architecture, different system configurations are considered by combining single-user multi-user systems, downlink uplink information transfer, and variable fixed coding rates. Optimizing the power control for these configurations results in a new class of multi-user power-control problems featuring the circuit-power constraints, specifying that the transferred power must be sufficiently large to support the operation of the receiver circuitry. Solving these problems gives a set of power-control algorithms that exploit channel diversity in frequency for simultaneously enhancing the throughput and the MPT efficiency. For the system configurations with variable coding rates, the algorithms are variants of water-filling that account for the circuit-power constraints. The optimal algorithms for those configurations with fixed coding rates are shown to sequentially allocate mobiles their required power for decoding in ascending order until the entire budgeted power is spent. The required power for a mobile is derived as simple functions of the minimum signal-to-noise ratio for correct decoding, the circuit power and sub-channel gains. In Cognitive Radio, the key to overcome the spectrum scarcity is reliable spectrum sensing, which will allow efficient opportunistic spectral reuse with minimum interference to licensed users. We consider energy detection based sensing that requires efficient noise power estimation technique in order to reliably detect the primary transmissions. We consider three algorithms, viz. Akaike Information Criterion (AIC), Minimum Description Length (MDL), and Rank Order Filtering (ROF), which estimate the noise power in the presence of the signal. The ROF key property is that it attempts to track the non-flat noise floor. Algorithm probability of missed detection is evaluated using data obtained from USRP2 devices. We illustrate that ROF may gain up to 6dB when signal to be detected is in the sub-band where the noise oor is not at (e.g., at the edges of the observed band).