Showing papers in "IEEE Transactions on Wireless Communications in 2013"

MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer

Abstract: 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.

Relaying Protocols for Wireless Energy Harvesting and Information Processing

Abstract: 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.

User Association for Load Balancing in Heterogeneous Cellular Networks

Abstract: For small cell technology to significantly increase the capacity of tower-based cellular networks, mobile users will need to be actively pushed onto the more lightly loaded tiers (corresponding to, e.g., pico and femtocells), even if they offer a lower instantaneous SINR than the macrocell base station (BS). Optimizing a function of the long-term rate for each user requires (in general) a massive utility maximization problem over all the SINRs and BS loads. On the other hand, an actual implementation will likely resort to a simple biasing approach where a BS in tier j is treated as having its SINR multiplied by a factor Aj ≥ 1, which makes it appear more attractive than the heavily-loaded macrocell. This paper bridges the gap between these approaches through several physical relaxations of the network-wide association problem, whose solution is NP hard. We provide a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee, and we observe that simple per-tier biasing loses surprisingly little, if the bias values Aj are chosen carefully. Numerical results show a large (3.5x) throughput gain for cell-edge users and a 2x rate gain for median users relative to a maximizing received power association.

Offloading in Heterogeneous Networks: Modeling, Analysis, and Design Insights

Abstract: Pushing data traffic from cellular to WiFi is an example of inter radio access technology (RAT) offloading. While this clearly alleviates congestion on the over-loaded cellular network, the ultimate potential of such offloading and its effect on overall system performance is not well understood. To address this, we develop a general and tractable model that consists of M different RATs, each deploying up to K different tiers of access points (APs), where each tier differs in transmit power, path loss exponent, deployment density and bandwidth. Each class of APs is modeled as an independent Poisson point process (PPP), with mobile user locations modeled as another independent PPP, all channels further consisting of i.i.d. Rayleigh fading. The distribution of rate over the entire network is then derived for a weighted association strategy, where such weights can be tuned to optimize a particular objective. We show that the optimum fraction of traffic offloaded to maximize SINR coverage is not in general the same as the one that maximizes rate coverage, defined as the fraction of users achieving a given rate.

Energy-Optimal Mobile Cloud Computing under Stochastic Wireless Channel

Abstract: This paper provides a theoretical framework of energy-optimal mobile cloud computing under stochastic wireless channel. Our objective is to conserve energy for the mobile device, by optimally executing mobile applications in the mobile device (i.e., mobile execution) or offloading to the cloud (i.e., cloud execution). One can, in the former case sequentially reconfigure the CPU frequency; or in the latter case dynamically vary the data transmission rate to the cloud, in response to the stochastic channel condition. We formulate both scheduling problems as constrained optimization problems, and obtain closed-form solutions for optimal scheduling policies. Furthermore, for the energy-optimal execution strategy of applications with small output data (e.g., CloudAV), we derive a threshold policy, which states that the data consumption rate, defined as the ratio between the data size (L) and the delay constraint (T), is compared to a threshold which depends on both the energy consumption model and the wireless channel model. Finally, numerical results suggest that a significant amount of energy can be saved for the mobile device by optimally offloading mobile applications to the cloud in some cases. Our theoretical framework and numerical investigations will shed lights on system implementation of mobile cloud computing under stochastic wireless channel.

Wireless Information Transfer with Opportunistic Energy Harvesting

Abstract: 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.

Opportunistic Wireless Energy Harvesting in Cognitive Radio Networks

Abstract: 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.

Wireless Information and Power Transfer: Energy Efficiency Optimization in OFDMA Systems

Abstract: This paper considers orthogonal frequency division multiple access (OFDMA) systems with simultaneous wireless information and power transfer. We study the resource allocation algorithm design for maximization of the energy efficiency of data transmission (bits/Joule delivered to the receivers). In particular, we focus on power splitting hybrid receivers which are able to split the received signals into two power streams for concurrent information decoding and energy harvesting. Two scenarios are investigated considering different power splitting abilities of the receivers. In the first scenario, we assume receivers which can split the received power into a continuous set of power streams with arbitrary power splitting ratios. In the second scenario, we examine receivers which can split the received power only into a discrete set of power streams with fixed power splitting ratios. For both scenarios, we formulate the corresponding algorithm design as a non-convex optimization problem which takes into account the circuit power consumption, the minimum data rate requirements of delay constrained services, the minimum required system data rate, and the minimum amount of power that has to be delivered to the receivers. By exploiting fractional programming and dual decomposition, suboptimal iterative resource allocation algorithms are developed to solve the non-convex problems. Simulation results illustrate that the proposed iterative resource allocation algorithms approach the optimal solution within a small number of iterations and unveil the trade-off between energy efficiency, system capacity, and wireless power transfer: (1) wireless power transfer enhances the system energy efficiency by harvesting energy in the radio frequency, especially in the interference limited regime; (2) the presence of multiple receivers is beneficial for the system capacity, but not necessarily for the system energy efficiency.

Analytical Modeling of Uplink Cellular Networks

Abstract: Cellular uplink analysis has typically been undertaken by either a simple approach that lumps all interference into a single deterministic or random parameter in a Wyner-type model, or via complex system level simulations that often do not provide insight into why various trends are observed. This paper proposes a novel middle way using point processes that is both accurate and also results in easy-to-evaluate integral expressions based on the Laplace transform of the interference. We assume mobiles and base stations are randomly placed in the network with each mobile pairing up to its closest base station. Compared to related recent work on downlink analysis, the proposed uplink model differs in two key features. First, dependence is considered between user and base station point processes to make sure each base station serves a single mobile in the given resource block. Second, per-mobile power control is included, which further couples the transmission of mobiles due to location-dependent channel inversion. Nevertheless, we succeed in deriving the coverage (equivalently outage) probability of a typical link in the network. This model can be used to address a wide variety of system design questions in the future. In this paper we focus on the implications for power control and show that partial channel inversion should be used at low signal-to-interference-plus-noise ratio (SINR), while full power transmission is optimal at higher SINR.

Dynamic Base Station Switching-On/Off Strategies for Green Cellular Networks

Abstract: In this paper, we investigate dynamic base station (BS) switching to reduce energy consumption in wireless cellular networks. Specifically, we formulate a general energy minimization problem pertaining to BS switching that is known to be a difficult combinatorial problem and requires high computational complexity as well as large signaling overhead. We propose a practically implementable switching-on/off based energy saving (SWES) algorithm that can be operated in a distributed manner with low computational complexity. A key design principle of the proposed algorithm is to turn off a BS one by one that will minimally affect the network by using a newly introduced notion of network-impact, which takes into account the additional load increments brought to its neighboring BSs. In order to further reduce the signaling and implementation overhead over the air and backhaul, we propose three other heuristic versions of SWES that use the approximate values of network-impact as their decision metrics. We describe how the proposed algorithms can be implemented in practice at the protocol-level and also estimate the amount of energy savings through a first-order analysis in a simple setting. Extensive simulations demonstrate that the SWES algorithms can significantly reduce the total energy consumption, e.g., we estimate up to 50-80% potential savings based on a real traffic profile from a metropolitan urban area.

Joint Resource Allocation and User Association for Heterogeneous Wireless Cellular Networks

Abstract: We propose a unified static framework to study the interplay of user association and resource allocation in heterogeneous cellular networks. This framework allows us to compare the performance of three channel allocation strategies: Orthogonal deployment, Co-channel deployment, and Partially Shared deployment. We have formulated joint optimization problems that are non-convex integer programs, are NP-hard, and hence it is difficult to efficiently obtain exact solutions. We have, therefore, developed techniques to obtain upper bounds on the system's performance. We show that these upper bounds are tight by comparing them to feasible solutions. We have used these upper bounds as benchmarks to quantify how well different user association rules and resource allocation schemes perform. Our numerical results indicate that significant gains in throughput are achievable for heterogeneous networks if the right combination of user association and resource allocation is used. Noting the significant impact of the association rule on the performance, we propose a simple association rule that performs much better than all existing user association rules.

Spatial Stochastic Models and Metrics for the Structure of Base Stations in Cellular Networks

Abstract: The spatial structure of base stations (BSs) in cellular networks plays a key role in evaluating the downlink performance. In this paper, different spatial stochastic models (the Poisson point process (PPP), the Poisson hard-core process (PHCP), the Strauss process (SP), and the perturbed triangular lattice) are used to model the structure by fitting them to the locations of BSs in real cellular networks obtained from a public database. We provide two general approaches for fitting. One is fitting by the method of maximum pseudolikelihood. As for the fitted models, it is not sufficient to distinguish them conclusively by some classical statistics. We propose the coverage probability as the criterion for the goodness-of-fit. In terms of coverage, the SP provides a better fit than the PPP and the PHCP. The other approach is fitting by the method of minimum contrast that minimizes the average squared error of the coverage probability. This way, fitted models are obtained whose coverage performance matches that of the given data set very accurately. Furthermore, we introduce a novel metric, the deployment gain, and we demonstrate how it can be used to estimate the coverage performance and average rate achieved by a data set.

A Learning Theoretic Approach to Energy Harvesting Communication System Optimization

Abstract: A point-to-point wireless communication system in which the transmitter is equipped with an energy harvesting device and a rechargeable battery, is studied. Both the energy and the data arrivals at the transmitter are modeled as Markov processes. Delay-limited communication is considered assuming that the underlying channel is block fading with memory, and the instantaneous channel state information is available at both the transmitter and the receiver. The expected total transmitted data during the transmitter's activation time is maximized under three different sets of assumptions regarding the information available at the transmitter about the underlying stochastic processes. A learning theoretic approach is introduced, which does not assume any a priori information on the Markov processes governing the communication system. In addition, online and offline optimization problems are studied for the same setting. Full statistical knowledge and causal information on the realizations of the underlying stochastic processes are assumed in the online optimization problem, while the offline optimization problem assumes non-causal knowledge of the realizations in advance. Comparing the optimal solutions in all three frameworks, the performance loss due to the lack of the transmitter's information regarding the behaviors of the underlying Markov processes is quantified.

Downlink MIMO HetNets: Modeling, Ordering Results and Performance Analysis

Abstract: We develop a general downlink model for multi-antenna heterogeneous cellular networks (HetNets), where base stations (BSs) across tiers may differ in terms of transmit power, target signal-to-interference-ratio (SIR), deployment density, number of transmit antennas and the type of multi-antenna transmission. In particular, we consider and compare space division multiple access (SDMA), single user beamforming (SU-BF), and baseline single-input single-output (SISO) transmission. For this general model, the main contributions are: (i) ordering results for both coverage probability and per user rate in closed form for any BS distribution for the three considered techniques, using novel tools from stochastic orders, (ii) upper bounds on the coverage probability assuming a Poisson BS distribution, and (iii) a comparison of the area spectral efficiency (ASE). The analysis concretely demonstrates, for example, that for a given total number of transmit antennas in the network, it is preferable to spread them across many single-antenna BSs vs. fewer multi-antenna BSs. Another observation is that SU-BF provides higher coverage and per user data rate than SDMA, but SDMA is in some cases better in terms of ASE.

Joint Wireless Information and Energy Transfer in a Two-User MIMO Interference Channel

Abstract: This paper investigates joint wireless information and energy transfer in a two-user MIMO interference channel, in which each receiver either decodes the incoming information data (information decoding, ID) or harvests the RF energy (energy harvesting, EH) to operate with a potentially perpetual energy supply. In the two-user interference channel, we have four different scenarios according to the receiver mode - (ID1, ID2), (EH1, EH2), (EH1, ID2), and (ID1, EH2). While the maximum information bit rate is unknown and finding the optimal transmission strategy is still open for (ID1, ID2), we have derived the optimal transmission strategy achieving the maximum harvested energy for (EH1, EH2). For (EH1, ID2), and (ID1, EH2), we find a necessary condition of the optimal transmission strategy and, accordingly, identify the achievable rate-energy (R-E) tradeoff region for two transmission strategies that satisfy the necessary condition - maximum energy beamforming (MEB) and minimum leakage beamforming (MLB). Furthermore, a new transmission strategy satisfying the necessary condition - signal-to-leakage-and-energy ratio (SLER) maximization beamforming - is proposed and shown to exhibit a better R-E region than the MEB and the MLB strategies. Finally, we propose a mode scheduling method to switch between (EH1, ID2) and (ID1, EH2) based on the SLER.

Cognitive Radio Networks with Energy Harvesting

Abstract: We consider a cognitive radio network with an energy-harvesting secondary transmitter to improve both energy efficiency and spectral efficiency. The goal of this paper is to determine an optimal spectrum sensing policy that maximizes the expected total throughput subject to an energy causality constraint and a collision constraint. The energy causality constraint comes from the fact that the total consumed energy should be equal to or less than the total harvested energy, while the collision constraint is required to protect the primary user. We first show that the system can be divided into a spectrum-limited regime and an energy-limited regime depending on where the detection threshold for the spectrum sensor lies. Assuming infinite battery capacity, we derive the optimal detection threshold that maximizes the expected total throughput subject to the energy causality constraint and the collision constraint. Analytical and numerical results show that the system is energy-limited if the energy arrival rate is lower than the expected energy consumption for a single spectrum access. They also show that a decreasing probability of accessing the occupied spectrum does not always result in decreased probability of accessing the idle spectrum in the energy-limited regime.

Optimal Save-Then-Transmit Protocol for Energy Harvesting Wireless Transmitters

Abstract: In this paper, the design of a wireless communication device relying exclusively on energy harvesting is considered. Due to the inability of rechargeable energy sources to charge and discharge at the same time, a constraint we term the energy half-duplex constraint, two rechargeable energy storage devices (ESDs) are assumed so that at any given time, there is always one ESD being recharged. The energy harvesting rate is assumed to be a random variable that is constant over the time interval of interest. A save-then-transmit (ST) protocol is introduced, in which a fraction of time ρ (dubbed the save-ratio) is devoted exclusively to energy harvesting, with the remaining fraction 1-ρ used for data transmission. The ratio of the energy obtainable from an ESD to the energy harvested is termed the energy storage efficiency, η. We address the practical case of the secondary ESD being a battery with η <; 1, and the main ESD being a super-capacitor with η = 1. Important properties of the optimal save-ratio that minimizes outage probability are derived, from which useful design guidelines are drawn. In addition, we compare the outage performance of random power supply to that of constant power supply over the Rayleigh fading channel. The diversity order with random power is shown to be the same as that of constant power, but the performance gap can be large. Finally, we extend the proposed ST protocol to wireless networks with multiple transmitters. It is shown that the system-level outage performance is critically dependent on the number of transmitters and the optimal save-ratio for single-channel outage minimization.

Resource Allocation for Device-to-Device Communications Overlaying Two-Way Cellular Networks

Abstract: Device-to-device (D2D) communications has been proposed in the literature as an underlay approach to cellular networks to allow direct transmission between two cellular devices with local communication needs. In this paper, we consider a scenario of D2D communications overlaying a cellular network and propose a new spectrum sharing protocol, which allows the D2D users to communicate bi-directionally with each other while assisting the two-way communications between the cellular base station (BS) and the cellular user (CU). We derive the achievable rate region of the sum rate of the D2D transmissions versus that of the cellular transmissions. The Pareto boundary of the region is found by optimizing the transmit power at BS and CU as well as the power splitting factor at the relay D2D node. Since either of the two D2D users can be the relay and there can exist multiple pairs of D2D users, we also consider the relay selection from the potential D2D users. We find through numerical results that the proposed two-way protocol with power control at the BS and CU is effective to improve the sum rate for both the D2D and cellular users. In addition, relay selection can achieve further improvement in the sum rate of the cellular links.

Energy-Efficient Resource Allocation in OFDMA Systems with Hybrid Energy Harvesting Base Station

Abstract: We study resource allocation algorithm design for energy-efficient communication in an orthogonal frequency division multiple access (OFDMA) downlink network with hybrid energy harvesting base station (BS). Specifically, an energy harvester and a constant energy source driven by a non-renewable resource are used for supplying the energy required for system operation. We first consider a deterministic offline system setting. In particular, assuming availability of non-causal knowledge about energy arrivals and channel gains, an offline resource allocation problem is formulated as a non-convex optimization problem over a finite horizon taking into account the circuit energy consumption, a finite energy storage capacity, and a minimum required data rate. We transform this non-convex optimization problem into a convex optimization problem by applying time-sharing and exploiting the properties of non-linear fractional programming which results in an efficient asymptotically optimal offline iterative resource allocation algorithm for a sufficiently large number of subcarriers. In each iteration, the transformed problem is solved by using Lagrange dual decomposition. The obtained resource allocation policy maximizes the weighted energy efficiency of data transmission (weighted bit/Joule delivered to the receiver). Subsequently, we focus on online algorithm design. A conventional stochastic dynamic programming approach is employed to obtain the optimal online resource allocation algorithm which entails a prohibitively high complexity. To strike a balance between system performance and computational complexity, we propose a low complexity suboptimal online iterative algorithm which is motivated by the offline algorithm. Simulation results illustrate that the proposed suboptimal online iterative resource allocation algorithm does not only converge in a small number of iterations, but also achieves a close-to-optimal system energy efficiency by utilizing only causal channel state and energy arrival information.

Spatial-Temporal Opportunity Detection for Spectrum-Heterogeneous Cognitive Radio Networks: Two-Dimensional Sensing

Abstract: This paper investigates the issue of spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks, where at a given time secondary users (SUs) at different locations may experience different spectrum access opportunities. Most prior studies address either spatial or temporal sensing in isolation and explicitly or implicitly assume that all SUs share the same spectrum opportunity. However, this assumption is not realistic and the traditional non-cooperative sensing (NCS) and cooperative sensing (CS) schemes are not very effective in a more realistic setting considering the heterogeneous spectrum availability among SUs. We define new performance metrics to guide the spatial-temporal opportunity detection and propose a two-dimensional sensing (TDS) framework to improve the opportunity detection performance, which exploits correlations in time and space simultaneously by effectively fusing sensing results in a spatial-temporal sensing window. Furthermore, in terms of maximum interference constrained transmission power (MICTP), we classify the spatial opportunities for SUs into three groups: black, grey, and white, and propose a TDS-based distributed power control scheme to further improve the spectrum utilization by exploiting both grey and white spectrum opportunities. The effectiveness of the proposed scheme is demonstrated through in-depth numerical simulations under a variety of scenarios.

On Optimizing Green Energy Utilization for Cellular Networks with Hybrid Energy Supplies

Abstract: Green communications has received much attention recently. For cellular networks, the base stations (BSs) account for more than 50 percent of the energy consumption of the networks. Therefore, reducing the power consumption of BSs is crucial to achieve green cellular networks. With the development of green energy technologies, BSs are able to be powered by green energy in order to reduce the on-grid energy consumption, thus reducing the CO2 footprints. In this paper, we envision that the BSs of future cellular networks are powered by both on-grid energy and green energy. We optimize the energy utilization in such networks by maximizing the utilization of green energy, and thus saving on-grid energy. The optimal usage of green energy depends on the characteristics of the energy generation and the mobile traffic, which exhibit both temporal and spatial diversities. We decompose the problem into two sub-problems: the multi-stage energy allocation problem and the multi-BSs energy balancing problem. We propose algorithms to solve these sub-problems, and subsequently solve the green energy optimization problem. Simulation results demonstrate that the proposed solution achieves significant on-grid energy savings.

Coordinated Multipoint Transmission with Limited Backhaul Data Transfer

Abstract: When the joint processing technique is applied in the coordinated multipoint (CoMP) downlink transmission, the user data for each mobile station needs to be shared among multiple base stations (BSs) via backhaul. If the number of users is large, this data exchange can lead to a huge backhaul signaling overhead. In this paper, we consider a multi-cell CoMP network with multi-antenna BSs and single antenna users. The problem that involves the joint design of transmit beamformers and user data allocation at BSs to minimize the backhaul user data transfer is addressed, which is subject to given quality-of-service and per-BS power constraints. We show that this problem can be cast into an l0-norm minimization problem, which is NP-hard. Inspired by recent results in compressive sensing, we propose two algorithms to tackle it. The first algorithm is based on reweighted l1-norm minimization, which solves a series of convex l0-norm minimization problems. In the second algorithm, we first solve the l2-norm relaxation of the joint clustering and beamforming problem and then iteratively remove the links that correspond to the smallest transmit power. The second algorithm enjoys a faster solution speed and can also be implemented in a semi-distributed manner under certain assumptions. Simulations show that both algorithms can significantly reduce the user data transfer in the backhaul.

Load-Aware Modeling and Analysis of Heterogeneous Cellular Networks

Abstract: Random spatial models are attractive for modeling heterogeneous cellular networks (HCNs) due to their realism, tractability, and scalability. A major limitation of such models to date in the context of HCNs is the neglect of network traffic and load: all base stations (BSs) have typically been assumed to always be transmitting. Small cells in particular will have a lighter load than macrocells, and so their contribution to the network interference may be significantly overstated in a fully loaded model. This paper incorporates a flexible notion of BS load by introducing a new idea of conditionally thinning the interference field. For a K-tier HCN where BSs across tiers differ in terms of transmit power, supported data rate, deployment density, and now load, we derive the coverage probability for a typical mobile, which connects to the strongest BS signal. Conditioned on this connection, the interfering BSs of the i^{th} tier are assumed to transmit independently with probability p_i, which models the load. Assuming — reasonably — that smaller cells are more lightly loaded than macrocells, the analysis shows that adding such access points to the network always increases the coverage probability. We also observe that fully loaded models are quite pessimistic in terms of coverage.

Stochastic Modeling of THz Indoor Radio Channels

Abstract: Providing the basis for fast system simulations and the adequate design of upcoming THz communication systems, a novel stochastic 300 GHz indoor channel model is introduced. It combines both the modeling in time as well as in frequency domain in order to account for the significant frequency dispersion of ultra broadband THz channels. Not only amplitude, phase and temporal, but also spatial channel information is considered. That way, MIMO systems as well as novel antenna concepts can be simulated. Verified and calibrated frequency domain ray tracing simulations in an office scenario provide the data basis for the derivation of model parameters. Model channel realizations are tested against ray tracing predictions and channel measurements. A complete scenario-specific parameter set is given for the considered environment, so that the model can be implemented for further use and future THz communication links can be designed under consideration of realistic propagation conditions.

Joint Spectrum Sensing and Access Evolutionary Game in Cognitive Radio Networks

Abstract: Many spectrum sensing methods and dynamic access algorithms have been proposed to improve the secondary users' opportunities of utilizing the primary users' spectrum resources. However, few of them have considered to integrate the design of spectrum sensing and access algorithms together by taking into account the mutual influence between them. In this paper, we propose to jointly analyze the spectrum sensing and access problem by studying two scenarios: synchronous scenario where the primary network is slotted and non-slotted asynchronous scenario. Due to selfish nature, secondary users tend to act selfishly to access the channel without contribution to the spectrum sensing. Moreover, they may take out-of-equilibrium strategies because of the uncertainty of others' strategies. To model the complicated interactions among secondary users, we formulate the joint spectrum sensing and access problem as an evolutionary game and derive the evolutionarily stable strategy (ESS) that no one will deviate from. Furthermore, we design a distributed learning algorithm for the secondary users to converge to the ESS. With the proposed algorithm, each secondary user senses and accesses the primary channel with the probabilities learned purely from its own past utility history, and finally achieves the desired ESS. Simulation results shows that our system can quickly converge to the ESS and such an ESS is robust to the sudden unfavorable deviations of the selfish secondary users.

Energy Efficiency Optimization for MIMO Broadcast Channels

Abstract: Characterizing the fundamental energy efficiency (EE) limits of MIMO broadcast channels (BC) is significant for the development of green wireless communications. We address the EE optimization problem for MIMO-BC in this paper and consider a practical power model, i.e., taking into account a transmit independent power which is related to the number of active transmit antennas. Under this setup, we propose a new optimization approach, in which the transmit covariance is optimized under fixed active transmit antenna sets, and then active transmit antenna selection (ATAS) is utilized. During the transmit covariance optimization, we propose a globally optimal energy efficient iterative water-filling scheme through solving a series of concave-convex fractional programs based on the block-coordinate ascent algorithm. After that, ATAS is employed to determine the active transmit antenna set. Since activating more transmit antennas can achieve higher sum-rate but at the cost of larger transmit independent power consumption, there exists a tradeoff between the sum-rate gain and the power consumption. Here ATAS can explore the optimal tradeoff curve and thus further improve the EE. Optimal exhaustive search and low-complexity norm based ATAS schemes are developed. Through simulations, we discuss the effect of different parameters on the EE of the MIMO-BC.

Self-Organization in Small Cell Networks: A Reinforcement Learning Approach

Abstract: In this paper, a decentralized and self-organizing mechanism for small cell networks (such as micro-, femto- and picocells) is proposed. In particular, an application to the case in which small cell networks aim to mitigate the interference caused to the macrocell network, while maximizing their own spectral efficiencies, is presented. The proposed mechanism is based on new notions of reinforcement learning (RL) through which small cells jointly estimate their time-average performance and optimize their probability distributions with which they judiciously choose their transmit configurations. Here, a minimum signal to interference plus noise ratio (SINR) is guaranteed at the macrocell user equipment (UE), while the small cells maximize their individual performances. The proposed RL procedure is fully distributed as every small cell base station requires only an observation of its instantaneous performance which can be obtained from its UE. Furthermore, it is shown that the proposed mechanism always converges to an epsilon Nash equilibrium when all small cells share the same interest. In addition, this mechanism is shown to possess better convergence properties and incur less overhead than existing techniques such as best response dynamics, fictitious play or classical RL. Finally, numerical results are given to validate the theoretical findings, highlighting the inherent tradeoffs facing small cells, namely exploration/exploitation, myopic/foresighted behavior and complete/incomplete information.

Spectrum Sharing Scheme Between Cellular Users and Ad-hoc Device-to-Device Users

Abstract: In an attempt to utilize spectrum resources more efficiently, protocols sharing licensed spectrum with unlicensed users are receiving increased attention. From the perspective of cellular networks, spectrum underutilization makes spatial reuse a feasible complement to existing standards. Interference management is a major component in designing these schemes as it is critical that licensed users maintain their expected quality of service. We develop a distributed dynamic spectrum protocol in which ad-hoc device-to-device users opportunistically access the spectrum actively in use by cellular users. First, channel gain estimates are used to set feasible transmit powers for device-to-device users that keeps the interference they cause within the allowed interference temperature. Then network information is distributed by route discovery packets in a random access manner to help establish either a single-hop or multi-hop route between two device-to-device users. We show that network information in the discovery packet can decrease the failure rate of the route discovery and reduce the number of necessary transmissions to find a route. Using the found route, we show that two device-to-device users can communicate with a low probability of outage while only minimally affecting the cellular network, and can achieve significant power savings when communicating directly with each other instead of utilizing the cellular base station.

Simultaneous Transmission and Reception: Algorithm, Design and System Level Performance

Abstract: Full Duplex or Simultaneous transmission and reception (STR) in the same frequency at the same time can potentially double the physical layer capacity. However, high power transmit signal will appear at receive chain as echoes with powers much higher than the desired received signal. Therefore, in order to achieve the potential gain, it is imperative to cancel these echoes. As these high power echoes can saturate low noise amplifier (LNA) and also digital domain echo cancellation requires unrealistically high resolution analog-to-digital converter (ADC), the echoes should be cancelled or suppressed sufficiently before LNA. In this paper we present a closed-loop echo cancellation technique which can be implemented purely in analogue domain. The advantages of our method are multiple-fold: it is robust to phase noise, does not require additional set of antennas, can be applied to wideband signals and the performance is irrelevant to radio frequency (RF) impairments in transmit chain. Next, we study a few protocols for STR systems in carrier sense multiple access (CSMA) network and investigate MAC level throughput with realistic assumptions in both single cell and multiple cells. We show that STR can reduce hidden node problem in CSMA network and produce gains of up to 279% in maximum throughput in such networks. Moreover, at high traffic load, the gain of STR system can be tremendously large since the throughput of non-STR system is close to zero at heavy traffic due to severe collisions. Finally, we investigate the application of STR in cellular systems and study two new unique interferences introduced to the system due to STR, namely BS-BS interference and UE-UE interference. We show that these two new interferences will hugely degrade system performance if not treated appropriately. We propose novel methods to reduce both interferences and investigate the performances in system level. We show that BS-BS interference can be suppressed sufficiently enough to be less than thermal noise power, and with favorable UE-UE channel model, capacities close to double are observed both in downlink (DL) and uplink (UL). When UE-UE interference is larger than DL co-channel interferences, we propose a simple and "non-cooperative" technique in order to reduce UE-UE interference.

Towards Understanding the Fundamentals of Mobility in Cellular Networks

Abstract: Despite the central role of mobility in wireless networks, analytical study on its impact on network performance is notoriously difficult. This paper aims to address this gap by proposing a random waypoint (RWP) mobility model defined on the entire plane and applying it to analyze two key cellular network parameters: handover rate and sojourn time. We first analyze the stochastic properties of the proposed model and compare it to two other models: the classical RWP mobility model and a synthetic truncated Levy walk model which is constructed from real mobility trajectories. The comparison shows that the proposed RWP mobility model is more appropriate for the mobility simulation in emerging cellular networks, which have ever-smaller cells. Then we apply the proposed model to cellular networks under both deterministic (hexagonal) and random (Poisson) base station (BS) models. We present analytic expressions for both handover rate and sojourn time, which have the expected property that the handover rate is proportional to the square root of BS density. Compared to an actual BS distribution, we find that the Poisson-Voronoi model is about as accurate in terms of mobility evaluation as hexagonal model, though being more pessimistic in that it predicts a higher handover rate and lower sojourn time.