Showing papers in "IEEE Journal on Selected Areas in Communications in 2014"
[...]
TL;DR: This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.
Abstract: What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.
7,139 citations
TL;DR: Detailed spatial statistical models of the channels are derived and it is found that, even in highly non-line-of-sight environments, strong signals can be detected 100-200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing.
Abstract: With the severe spectrum shortage in conventional cellular bands, millimeter wave (mmW) frequencies between 30 and 300 GHz have been attracting growing attention as a possible candidate for next-generation micro- and picocellular wireless networks. The mmW bands offer orders of magnitude greater spectrum than current cellular allocations and enable very high-dimensional antenna arrays for further gains via beamforming and spatial multiplexing. This paper uses recent real-world measurements at 28 and 73 GHz in New York, NY, USA, to derive detailed spatial statistical models of the channels and uses these models to provide a realistic assessment of mmW micro- and picocellular networks in a dense urban deployment. Statistical models are derived for key channel parameters, including the path loss, number of spatial clusters, angular dispersion, and outage. It is found that, even in highly non-line-of-sight environments, strong signals can be detected 100-200 m from potential cell sites, potentially with multiple clusters to support spatial multiplexing. Moreover, a system simulation based on the models predicts that mmW systems can offer an order of magnitude increase in capacity over current state-of-the-art 4G cellular networks with no increase in cell density from current urban deployments.
2,102 citations
TL;DR: In this article, the authors present a survey of self-interference mitigation techniques for in-band full-duplex (IBFD) wireless systems and discuss the challenges and opportunities in the design and analysis of IBFD wireless systems.
Abstract: In-band full-duplex (IBFD) operation has emerged as an attractive solution for increasing the throughput of wireless communication systems and networks. With IBFD, a wireless terminal is allowed to transmit and receive simultaneously in the same frequency band. This tutorial paper reviews the main concepts of IBFD wireless. One of the biggest practical impediments to IBFD operation is the presence of self-interference, i.e., the interference that the modem's transmitter causes to its own receiver. This tutorial surveys a wide range of IBFD self-interference mitigation techniques. Also discussed are numerous other research challenges and opportunities in the design and analysis of IBFD wireless systems.
1,752 citations
TL;DR: A case is made for using mmWave for a fifth generation (5G) wireless system for ultradense networks by presenting an overview of enhanced local area (eLA) technology at mmWave with emphasis on 5G requirements, spectrum considerations, propagation and channel modeling, air-interface and multiantenna design, and network architecture solutions.
Abstract: Wireless data traffic is projected to skyrocket 10 000 fold within the next 20 years. To tackle this incredible increase in wireless data traffic, a first approach is to further improve spectrally efficient systems such as 4G LTE in bands below 6 GHz by using more advanced spectral efficiency techniques. However, the required substantial increase in system complexity along with fundamental limits on hardware implementation and channel conditions may limit the viability of this approach. Furthermore, the end result would be an extremely spectrally efficient system with little room for future improvement to meet the ever-growing wireless data usage. The second approach is to move up in frequency, into an unused nontraditional spectrum where enormous bandwidths are available, such as at millimeter wave (mmWave). The mmWave option enables the use of simple air interfaces since large bandwidths can be exploited (e.g., 2 GHz) to achieve high data rates rather than relying on highly complex techniques originally aimed at achieving a high spectral efficiency with smaller bandwidths. In addition, mmWave systems will easily evolve to even higher system capacities, because there will be plenty of margin to improve the spectral efficiency as data demands further increase. In this paper, a case is made for using mmWave for a fifth generation (5G) wireless system for ultradense networks by presenting an overview of enhanced local area (eLA) technology at mmWave with emphasis on 5G requirements, spectrum considerations, propagation and channel modeling, air-interface and multiantenna design, and network architecture solutions.
793 citations
TL;DR: It is argued that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation, which is akin, but also orthogonal to the transport-layer TCP congestion control, and PANDA - a client-side rate adaptation algorithm for HAS is presented.
Abstract: Today, the technology for video streaming over the Internet is converging towards a paradigm named HTTP-based adaptive streaming (HAS), which brings two new features. First, by using HTTP/TCP, it leverages network-friendly TCP to achieve both firewall/NAT traversal and bandwidth sharing. Second, by pre-encoding and storing the video in a number of discrete rate levels, it introduces video bitrate adaptivity in a scalable way so that the video encoding is excluded from the closed-loop adaptation. A conventional wisdom in HAS design is that since the TCP throughput observed by a client would indicate the available network bandwidth, it could be used as a reliable reference for video bitrate selection. We argue that this is no longer true when HAS becomes a substantial fraction of the total network traffic. We show that when multiple HAS clients compete at a network bottleneck, the discrete nature of the video bitrates results in difficulty for a client to correctly perceive its fair-share bandwidth. Through analysis and test bed experiments, we demonstrate that this fundamental limitation leads to video bitrate oscillation and other undesirable behaviors that negatively impact the video viewing experience. We therefore argue that it is necessary to design at the application layer using a "probe and adapt" principle for video bitrate adaptation (where "probe" refers to trial increment of the data rate, instead of sending auxiliary piggybacking traffic), which is akin, but also orthogonal to the transport-layer TCP congestion control. We present PANDA - a client-side rate adaptation algorithm for HAS - as a practical embodiment of this principle. Our test bed results show that compared to conventional algorithms, PANDA is able to reduce the instability of video bitrate selection by over 75% without increasing the risk of buffer underrun.
545 citations
TL;DR: In this article, the authors considered a multi-user decode-and-forward relay channel, where multiple sources transmit simultaneously their signals to multiple destinations with the help of a full-duplex relay station and derived an exact achievable rate expression in closed-form for MRC/MRT processing and an analytical approximation of the achievable rate for ZF processing.
Abstract: We consider a multipair decode-and-forward relay channel, where multiple sources transmit simultaneously their signals to multiple destinations with the help of a full-duplex relay station. We assume that the relay station is equipped with massive arrays, while all sources and destinations have a single antenna. The relay station uses channel estimates obtained from received pilots and zero-forcing (ZF) or maximum-ratio combining/maximum-ratio transmission (MRC/MRT) to process the signals. To significantly reduce the loop interference effect, we propose two techniques: i) using a massive receive antenna array; or ii) using a massive transmit antenna array together with very low transmit power at the relay station. We derive an exact achievable rate expression in closed-form for MRC/MRT processing and an analytical approximation of the achievable rate for ZF processing. This approximation is very tight, particularly for a large number of relay station antennas. These closed-form expressions enable us to determine the regions where the full-duplex mode outperforms the half-duplex mode, as well as to design an optimal power allocation scheme. This optimal power allocation scheme aims to maximize the energy efficiency for a given sum spectral efficiency and under peak power constraints at the relay station and sources. Numerical results verify the effectiveness of the optimal power allocation scheme. Furthermore, we show that, by doubling the number of transmit/receive antennas at the relay station, the transmit power of each source and of the relay station can be reduced by 1.5 dB if the pilot power is equal to the signal power, and by 3 dB if the pilot power is kept fixed, while maintaining a given quality of service.
415 citations
TL;DR: This work aims to increase the throughput of polar decoding hardware by an order of magnitude relative to successive-cancellation decoders and is more than 8 times faster than the current fastest polar decoder.
Abstract: Polar codes provably achieve the symmetric capacity of a memoryless channel while having an explicit construction. The adoption of polar codes however, has been hampered by the low throughput of their decoding algorithm. This work aims to increase the throughput of polar decoding hardware by an order of magnitude relative to successive-cancellation decoders and is more than 8 times faster than the current fastest polar decoder. We present an algorithm, architecture, and FPGA implementation of a flexible, gigabit-per-second polar decoder.
391 citations
TL;DR: In this paper, the problem of user grouping for two different objectives, namely, maximizing spatial multiplexing and maximizing total received power, was formulated in a graph-theoretic framework.
Abstract: Massive MIMO systems are well-suited for mm-Wave communications, as large arrays can be built with reasonable form factors, and the high array gains enable reasonable coverage even for outdoor communications. One of the main obstacles for using such systems in frequency-division duplex mode, namely, the high overhead for the feedback of channel state information (CSI) to the transmitter, can be mitigated by the recently proposed joint spatial division and multiplexing (JSDM) algorithm. In this paper, we analyze the performance of this algorithm in some realistic propagation channels that take into account the partial overlap of the angular spectra from different users, as well as the sparsity of mm-Wave channels. We formulate the problem of user grouping for two different objectives, namely, maximizing spatial multiplexing and maximizing total received power in a graph-theoretic framework. As the resulting problems are numerically difficult, we proposed (sub optimum) greedy algorithms as efficient solution methods. Numerical examples show that the different algorithms may be superior in different settings. We furthermore develop a new, “degenerate” version of JSDM that only requires average CSI at the transmitter and thus greatly reduces the computational burden. Evaluations in propagation channels obtained from ray tracing results, as well as in measured outdoor channels, show that this low-complexity version performs surprisingly well in mm-Wave channels.
380 citations
TL;DR: This paper addresses the fundamental tradeoffs for maximizing energy efficiency (EE) versus spectrum efficiency (SE) for a point-to-point additive white Gaussian noise (AWGN) channel with the transmitter powered solely via energy harvesting from the environment and proposes a new online algorithm under the practical setup with only the past and present energy state information (ESI) known at the transmitter.
Abstract: Characterizing the fundamental tradeoffs for maximizing energy efficiency (EE) versus spectrum efficiency (SE) is a key problem in wireless communication. In this paper, we address this problem for a point-to-point additive white Gaussian noise (AWGN) channel with the transmitter powered solely via energy harvesting from the environment. In addition, we assume a practical on-off transmitter model with non-ideal circuit power, i.e., when the transmitter is on, its consumed power is the sum of the transmit power and a constant circuit power. Under this setup, we study the optimal transmit power allocation to maximize the average throughput over a finite horizon, subject to the time-varying energy constraint and the non-ideal circuit power consumption. First, we consider the off-line optimization under the assumption that the energy arrival time and amount are a priori known at the transmitter. Although this problem is non-convex due to the non-ideal circuit power, we show an efficient optimal solution that in general corresponds to a two-phase transmission: the first phase with an EE-maximizing on-off power allocation, and the second phase with a SE-maximizing power allocation that is non-decreasing over time, thus revealing an interesting result that both the EE and SE optimizations are unified in an energy harvesting communication system. We then extend the optimal off-line algorithm to the case with multiple parallel AWGN channels, based on the principle of nested optimization. Finally, inspired by the off-line optimal solution, we propose a new online algorithm under the practical setup with only the past and present energy state information (ESI) known at the transmitter.
324 citations
TL;DR: The analysis shows that after realistic antenna isolation and RF cancellation, the dominant self-interference waveform at the receiver digital baseband can be modeled through a widely linear transformation of the original transmit data, opposed to classical purely linear models.
Abstract: This paper addresses the modeling and cancellation of self-interference in full-duplex direct-conversion radio transceivers, operating under practical imperfect radio frequency (RF) components. First, detailed self-interference signal modeling is carried out, taking into account the most important RF imperfections, namely, transmitter power amplifier nonlinear distortion as well as transmitter and receiver IQ mixer amplitude and phase imbalances. The analysis shows that after realistic antenna isolation and RF cancellation, the dominant self-interference waveform at the receiver digital baseband can be modeled through a widely linear transformation of the original transmit data, opposed to classical purely linear models. Such widely linear self-interference waveform is physically stemming from the transmitter and receiver IQ imaging and cannot be efficiently suppressed by classical linear digital cancellation. Motivated by this, novel widely linear digital self-interference cancellation processing is then proposed and formulated, combined with efficient parameter estimation methods. Extensive simulation results demonstrate that the proposed widely linear cancellation processing clearly outperforms the existing linear solutions, hence enabling the use of practical low-cost RF front ends utilizing IQ mixing in full-duplex transceivers.
277 citations
TL;DR: Two design ideas are proposed, which provide attractive analog/RF-isolation and allow integration in compact radios and combines a dual-port polarized antenna with a self-tunable cancellation circuit.
Abstract: In-band full-duplex sets challenging requirements for wireless communication radios, in particular their capability to prevent receiver sensitivity degradation due to self-interference (transmit signals leaking into its own receiver). Previously published self-interference rejection designs require bulky components and/or antenna structures. This paper addresses this form-factor issue. First, compact radio transceiver feasibility bottlenecks are identified analytically, and tradeoff equations in function of link budget parameters are presented. These derivations indicate that the main bottlenecks can be resolved by increasing the isolation in analog/RF. Therefore, two design ideas are proposed, which provide attractive analog/RF-isolation and allow integration in compact radios. The first design proposal targets compact radio devices, such as small-cell base stations and tablet computers, and combines a dual-port polarized antenna with a self-tunable cancellation circuit. The second design proposal targets even more compact radio devices such as smartphones and sensor network nodes. This design builds on a tunable electrical balance isolator/duplexer in combination with a single-port miniature antenna. The electrical balance circuit can be implemented for scaled CMOS technology, facilitating low cost and dense integration.
TL;DR: This paper considers optimization of the user and base-station (BS) association in a wireless downlink heterogeneous cellular network under the proportional fairness criterion and proposes an iterative dual coordinate descent and the power optimization algorithm that significantly outperforms existing approaches.
Abstract: This paper considers optimization of the user and base-station (BS) association in a wireless downlink heterogeneous cellular network under the proportional fairness criterion. We first consider the case where each BS has a single antenna and transmits at fixed power and propose a distributed price update strategy for a pricing-based user association scheme, in which the users are assigned to the BS based on the value of a utility function minus a price. The proposed price update algorithm is based on a coordinate descent method for solving the dual of the network utility maximization problem and it has a rigorous performance guarantee. The main advantage of the proposed algorithm as compared to an existing subgradient method for price update is that the proposed algorithm is independent of parameter choices and can be implemented asynchronously. Further, this paper considers the joint user association and BS power control problem and proposes an iterative dual coordinate descent and the power optimization algorithm that significantly outperforms existing approaches. Finally, this paper considers the joint user association and BS beamforming problem for the case where the BSs are equipped with multiple antennas and spatially multiplex multiple users. We incorporate dual coordinate descent with the weighted minimum mean-squared error (WMMSE) algorithm and show that it achieves nearly the same performance as a computationally more complex benchmark algorithm (which applies the WMMSE algorithm on the entire network for BS association) while avoiding excessive BS handover.
TL;DR: This work uses a novel fork-join queueing framework to model multiple users requesting the content simultaneously, and derive bounds on the expected download time, demonstrating the fundamental trade-off between the expected downloading time and the amount of storage space.
Abstract: We study how coding in distributed storage reduces expected download time, in addition to providing reliability against disk failures. The expected download time is reduced because when a content file is encoded with redundancy and distributed across multiple disks, reading only a subset of the disks is sufficient for content reconstruction. For the same total storage used, coding exploits the diversity in storage better than simple replication, and hence gives faster download. We use a novel fork-join queueing framework to model multiple users requesting the content simultaneously, and derive bounds on the expected download time. Our system model and results are a novel generalization of the fork-join system that is studied in queueing theory literature. Our results demonstrate the fundamental trade-off between the expected download time and the amount of storage space. This trade-off can be used for design of the amount of redundancy required to meet the delay constraints on content delivery.
TL;DR: A simple self-organization rule is introduced, based on minimizing cell transmit power, following which a distributed cellular network is able to converge into an efficient resource reuse pattern, and two novel resource allocation algorithms are proposed, being autonomous and coordinated, respectively.
Abstract: With the introduction of femtocells, cellular networks are moving from the conventional centralized network architecture to a distributed one, where each network cell should make its own radio resource allocation decisions, while providing inter-cell interference mitigation. However, realizing such distributed network architecture is not a trivial task. In this paper, we first introduce a simple self-organization rule, based on minimizing cell transmit power, following which a distributed cellular network is able to converge into an efficient resource reuse pattern. Based on such self-organization rule and taking realistic resource allocation constraints into account, we also propose two novel resource allocation algorithms, being autonomous and coordinated, respectively. Performance of the proposed self-organization rule and resource allocation algorithms are evaluated using system-level simulations, and show that power efficiency is not necessarily in conflict with capacity improvements at the network level. The proposed resource allocation algorithms provide significant performance improvements in terms of user outages and network capacity over cutting-edge resource allocation algorithms proposed in the literature.
TL;DR: Numerical analysis shows that the proposed channel models are able to serve as a design framework for massive MIMO channel modeling.
Abstract: This paper proposes a novel theoretical non-stationary three dimensional (3-D) wideband twin-cluster channel model for massive multiple-input multiple-output (MIMO) communication systems with carrier frequencies on the order of gigahertz (GHz). As the dimension of antenna arrays cannot be ignored for massive MIMO, near field effects instead of far field effects are considered in the proposed model. These include the spherical wavefront assumption and a birth-death process to model non-stationary properties of clusters such as cluster appearance and disappearance on both the array and time axes. Their impacts on massive MIMO channels are investigated via statistical properties including correlation functions, condition numbers, and angular power spectra. Additionally, the impact of elevation angles on correlation functions is discussed. A corresponding simulation model for the theoretical model is also proposed. Finally, numerical analysis shows that the proposed channel models are able to serve as a design framework for massive MIMO channel modeling.
TL;DR: This paper shows that FD linear relaying systems with a suitable precoder can attain the same diversity function as their half-duplex (HD) counterparts, and shows that HD orthogonal AF using a superposition constellation is asymptotically optimal in terms of maximum coding gain.
Abstract: This paper investigates the error and diversity performances of full-duplex (FD) amplify-and-forward (AF) singlerelay systems under the effect of residual self-interference. The variance of this interference is assumed to be proportional to the λ-th power of the transmitted power (0 ≤ λ ≤ 1). The study considers the cooperative linear relaying protocol with direct source-destination link and the dual-hop scheme without direct link, both under uncoded and coded frameworks. At first, closed-form pairwise error probability expressions are derived for the uncoded systems, which are then used to obtain tight bounds to the bit error rate (BER) of the coded systems. To shed an insight on the diversity behavior, asymptotic expressions at high transmission powers are also presented. Different from previous works that treat the direct link as interference, this paper shows that FD linear relaying systems with a suitable precoder can attain the same diversity function as their half-duplex (HD) counterparts. However, further analysis shows that HD orthogonal AF using a superposition constellation is asymptotically optimal in terms of maximum coding gain. In addition, it is shown that the diversity of FD dual-hop systems is a decreasing function of λ and is equal to zero when λ = 1. Although HD relaying is asymptotically optimal under the considered protocols and interference model, illustrative results show that FD relaying is advantageous at practical BER levels when λ is sufficiently small.
TL;DR: This work forms residential DR as an optimal power flow problem and proposes a distributed scheme where the load service entity and the households interactively communicate to compute an optimal demand schedule.
Abstract: Demand response (DR) enables customers to adjust their electricity usage to balance supply and demand. Most previous works on DR consider the supply-demand matching in an abstract way without taking into account the underlying power distribution network and the associated power flow and system operational constraints. As a result, the schemes proposed by those works may end up with electricity consumption/shedding decisions that violate those constraints and thus are not feasible. In this paper, we study residential DR with consideration of the power distribution network and the associated constraints. We formulate residential DR as an optimal power flow problem and propose a distributed scheme where the load service entity and the households interactively communicate to compute an optimal demand schedule. To complement our theoretical results, we also simulate an IEEE test distribution system. The simulation results demonstrate two interesting effects of DR. One is the location effect, meaning that the households far away from the feeder tend to reduce more demands in DR. The other is the rebound effect, meaning that DR may create a new peak after the DR event ends if the DR parameters are not chosen carefully. The two effects suggest certain rules we should follow when designing a DR program.
TL;DR: It is shown that multicell processing with optimized quantization noise levels across the BSs can significantly improve the performance of wireless cellular networks.
Abstract: This paper studies the uplink of a cloud radio access network (C-RAN) where the cell sites are connected to a cloud-computing-based central processor (CP) with noiseless backhaul links with finite capacities. We employ a simple compress-and-forward scheme in which the base stations (BSs) quantize the received signals and send the quantized signals to the CP using either distributed Wyner-Ziv coding or single-user compression. The CP first decodes the quantization codewords and then decodes the user messages as if the remote users and the cloud center form a virtual multiple-access channel (VMAC). This paper formulates the problem of optimizing the quantization noise levels for weighted sum rate maximization under a sum backhaul capacity constraint. We propose an alternating convex optimization approach to find a local optimum solution to the problem efficiently, and more importantly, to establish that setting the quantization noise levels to be proportional to the background noise levels is near optimal for sum-rate maximization when the signal-to-quantization-noise-ratio (SQNR) is high. In addition, with Wyner-Ziv coding, the approximate quantization noise level is shown to achieve the sum-capacity of the uplink C-RAN model to within a constant gap. With single-user compression, a similar constant-gap result is obtained under a diagonal dominant channel condition. These results lead to an efficient algorithm for allocating the backhaul capacities in C-RAN. The performance of the proposed scheme is evaluated for practical multicell and heterogeneous networks. It is shown that multicell processing with optimized quantization noise levels across the BSs can significantly improve the performance of wireless cellular networks.
TL;DR: The asymptotic Shapley value is shown to be in the core of the coalitional game such that no group of SESs and EUs has an incentive to abandon the coalition, which implies the stable operation of DT for the proposed pricing scheme.
Abstract: Integration of distributed generation based on renewable energy sources into the power system has gained popularity in recent years. Many small-scale electricity suppliers (SESs) have recently entered the electricity market, which has been traditionally dominated by a few large-scale electricity suppliers. The emergence of SESs enables direct trading (DT) of electricity between SESs and end-users (EUs), without going through retailers, and promotes the possibility of improving the benefits to both parties. In this paper, the cooperation between SESs and EUs in DT is analyzed based on coalitional game theory. In particular, an electricity pricing scheme that achieves a fair division of revenue between SESs and EUs is analytically derived by using the asymptotic Shapley value. The asymptotic Shapley value is shown to be in the core of the coalitional game such that no group of SESs and EUs has an incentive to abandon the coalition, which implies the stable operation of DT for the proposed pricing scheme. Unlike the existing pricing schemes that typically require multiple stages of calculations and real time information about each participant, the electricity price for the proposed scheme can be determined instantaneously based on the number of participants in DT and statistical information about electricity supply and demand. Therefore, the proposed pricing scheme is suitable for practical implementation. Using computer simulations, the price of electricity for the proposed DT scheme is examined in various environments, and the numerical results validate the asymptotic analysis. Moreover, the revenues of the SESs and EUs are evaluated for various types of SESs and different numbers of participants in DT. The optimal ratio of different types of SESs is also investigated.
TL;DR: A new framework is proposed to embrace the new opportunities brought by combining some special features of data centers with traffic engineering, and it is confirmed that, by using this framework, one can achieve up to 50 percent energy savings.
Abstract: The popularization of cloud computing has raised concerns over the energy consumption that takes place in data centers. In addition to the energy consumed by servers, the energy consumed by large numbers of network devices emerges as a significant problem. Existing work on energy-efficient data center networking primarily focuses on traffic engineering, which is usually adapted from traditional networks. We propose a new framework to embrace the new opportunities brought by combining some special features of data centers with traffic engineering. Based on this framework, we characterize the problem of achieving energy efficiency with a time-aware model, and we prove its NP-hardness with a solution that has two steps. First, we solve the problem of assigning virtual machines (VM) to servers to reduce the amount of traffic and to generate favorable conditions for traffic engineering. The solution reached for this problem is based on three essential principles that we propose. Second, we reduce the number of active switches and balance traffic flows, depending on the relation between power consumption and routing, to achieve energy conservation. Experimental results confirm that, by using this framework, we can achieve up to 50 percent energy savings. We also provide a comprehensive discussion on the scalability and practicability of the framework.
TL;DR: In this paper, a new concept of information-theoretic independent sets (ITISs) is defined, which indicates the sets of links for which simultaneous communication and treating the interference from each other as noise is information theoretically optimal (to within a constant gap).
Abstract: We consider the problem of spectrum sharing in device-to-device communication systems. Inspired by the recent optimality condition for treating interference as noise, we define a new concept of information-theoretic independent sets (ITISs), which indicates the sets of links for which simultaneous communication and treating the interference from each other as noise is information-theoretically optimal (to within a constant gap). Based on this concept, we develop a new spectrum sharing mechanism, called information-theoretic link scheduling (ITLinQ), which at each time schedules those links that form an ITIS. We first provide a performance guarantee for ITLinQ by characterizing the fraction of the capacity region that it can achieve in a network with sources and destinations randomly located within a fixed area. Furthermore, we demonstrate how ITLinQ can be implemented in a distributed manner, using an initial two-phase signaling mechanism that provides the required channel state information at all the links. Through numerical analysis, we show that distributed ITLinQ can outperform similar state-of-the-art spectrum sharing mechanisms, such as FlashLinQ, by more than 100% of sum-rate gain, while keeping the complexity at the same level. Finally, we discuss a variation of the distributed ITLinQ scheme, which can also guarantee fairness among the links in the network and numerically evaluate its performance.
TL;DR: Numerical secrecy outage results demonstrate that for both the coordinated and uncoordinated eavesdroppers, the optimal user scheduling achieves the best security performance and the round-robin scheduling performs the worst.
Abstract: In this paper, we investigate the physical-layer security of a multi-user multi-eavesdropper cognitive radio system, which is composed of multiple cognitive users (CUs) transmitting to a common cognitive base station (CBS), {while multiple eavesdroppers may collaborate with each other or perform independently in intercepting the CUs-CBS transmissions, which are called the coordinated and uncoordinated eavesdroppers, respectively}. Considering multiple CUs available, we propose the round-robin scheduling as well as the optimal and suboptimal user scheduling schemes for improving the security of CUs-CBS transmissions against eavesdropping attacks. Specifically, the optimal user scheduling is designed by assuming that the channel state information (CSI) of all links from CUs to CBS, to primary user (PU) and to eavesdroppers are available. By contrast, the suboptimal user scheduling only requires the CSI of CUs-CBS links without the PU's and eavesdroppers' CSI. We derive closed-form expressions of the secrecy outage probability of these three scheduling schemes in the presence of {the coordinated and uncoordinated eavesdroppers}. We also carry out the secrecy diversity analysis and show that the round-robin scheduling achieves the diversity order of only one, whereas the optimal and suboptimal scheduling schemes obtain the full secrecy diversity, {no matter whether the eavesdroppers collaborate or not. In addition, numerical secrecy outage results demonstrate that for both the coordinated and uncoordinated eavesdroppers, the optimal user scheduling achieves the best security performance and the round-robin scheduling performs the worst.} Finally, upon increasing the number of CUs, the secrecy outage probabilities of the optimal and suboptimal user scheduling schemes both improve significantly.
TL;DR: This paper investigates typical adaptation methods in the context of live video streaming and finds that the perceptual impact depends not only on adaptation method but also on the content itself.
Abstract: HTTP streaming has become a cost-effective means for multimedia delivery nowadays. For adaptivity to networks and terminals, a provider should generate multiple representations of an original video as well as the related metadata. Recently, there have been various adaptation methods to support adaptive HTTP streaming. In this paper, we investigate typical adaptation methods in the context of live video streaming. We first discuss the trade-off among typical adaptation methods. The evaluation and comparison are then carried out not only in terms of bitrate and buffer behaviors but also in terms of the perceptual impact on end users. It is found that the perceptual impact depends not only on adaptation method but also on the content itself. We also show that the preparation of representation sets may affect the behaviors of some adaptation methods.
TL;DR: Numerical results show that the asymptotic performance of the proposed scheme is close to the ideal case with perfect CSI, and a new pilot scheme that estimates the composite channel, which is a linear combination of the individual channels of multicast users in each cell.
Abstract: We study physical layer multicasting in cellular networks where each base station (BS) is equipped with a very large number of antennas and transmits a common message using a single beamformer to multiple mobile users. The messages sent by different BSs are independent, and the BSs do not cooperate. We first show that when each BS knows the perfect channel state information (CSI) of its own served users, the asymptotically optimal beamformer at each BS is a linear combination of the channel vectors of its multicast users. Moreover, the optimal and explicit combining coefficients are obtained. Then we consider the imperfect CSI scenario where the CSI is obtained through uplink channel estimation in time-division duplex systems. We propose a new pilot scheme that estimates the composite channel, which is a linear combination of the individual channels of multicast users in each cell. This scheme is able to completely eliminate pilot contamination. The pilot power control for optimizing the multicast beamformer at each BS is also derived. Numerical results show that the asymptotic performance of the proposed scheme is close to the ideal case with perfect CSI. Simulation also verifies the effectiveness of the proposed scheme with finite number of antennas at each BS.
TL;DR: A structure theorem is derived for general optimal (r, δ)a codes which helps illuminate some of their structure properties, and new constructive algorithms are proposed to cover more cases.
Abstract: Linear erasure codes with local repairability are desirable for distributed data storage systems. An [n,k,d] linear code having all-symbol (r,δ)-locality, denoted as (r,δ)_a, is considered optimal if it has the actual highest minimum distance of any code of the given parameters n,k,r and δ. A minimum distance bound is given in . The existing results on the existence and the construction of optimal (r, δ)_a linear codes are limited to only two small regions within this special case, namely, i) m=0 and ii) m≥ (v+δ-1)>(δ-1) and δ=2, where m=n mod(r+δ-1) and v=k mod r. This paper investigates the properties and existence conditions for optimal (r,δ)_a linear codes with general r and δ. First, a structure theorem is derived for general optimal (r,δ)_a codes which helps illuminate some of their structure properties. Next, the entire problem space with arbitrary n, k, r and δ is divided into eight different cases (regions) with regard to the specific relations of these parameters. For two cases, it is rigorously proved that no (r,δ)_a linear code can achieve the minimum distance bound in . For four other cases the optimal (r,δ)_a codes are shown to exist over a field of size q≥({n} {k-1}), deterministic constructions are proposed. Our new constructive algorithms not only cover more cases, but for the same cases where previous algorithms exist, the new constructions require a smaller field, which translates to potentially lower computational complexity. Our findings substantially enriches the knowledge on optimal (r,δ)_a linear codes, leaving only two cases in which the construction of optimal codes are not yet known.
TL;DR: This paper considers a network where the secondary user can perform channel access to transmit a packet or to harvest RF energy when the selected channel is idle or occupied by the primary user, respectively, and presents an optimization formulation to obtain the channel access policy.
Abstract: Radio frequency (RF) energy harvesting is a promising technique to sustain operations of wireless networks. In a cognitive radio network, a secondary user can be equipped with RF energy harvesting capability. In this paper, we consider such a network where the secondary user can perform channel access to transmit a packet or to harvest RF energy when the selected channel is idle or occupied by the primary user, respectively. We present an optimization formulation to obtain the channel access policy for the secondary user to maximize its throughput. Both the case that the secondary user knows the current state of the channels and the case that the secondary knows the idle channel probabilities of channels in advance are considered. However, the optimization requires model parameters (e.g., the probability of successful packet transmission, the probability of successful RF energy harvesting, and the probability of channel to be idle) to obtain the policy. To obviate such a requirement, we apply an online learning algorithm that can observe the environment and adapt the channel access action accordingly without any a prior knowledge about the model parameters. We evaluate both the efficiency and convergence of the learning algorithm. The numerical results show that the policy obtained from the learning algorithm can achieve the performance in terms of throughput close to that obtained from the optimization.
TL;DR: In this article, the authors proposed an EM-lens enabled MIMO system, which integrates an EM lens with the large antenna array to focus the power of an incident wave to a small area of the antenna array, whereas the location of the focal area varies with the angle of arrival of the wave.
Abstract: Massive multiple-input-multiple-output (MIMO) techniques have been recently advanced to tremendously improve the performance of wireless communication networks. However, the use of very large antenna arrays at the base stations brings new issues, such as the significantly increased hardware and signal processing costs. In order to reap the performance gains of massive MIMO and yet reduce its cost, this paper proposes a novel system design by integrating an electromagnetic (EM) lens with the large antenna array, termed the EM-lens enabled MIMO. The EM lens has the capability of focusing the power of an incident wave to a small area of the antenna array, whereas the location of the focal area varies with the angle of arrival (AoA) of the wave. Hence, in scenarios where the arriving signals from geographically separated users have different AoAs, the EM-lens enabled receiver provides two new benefits, namely, energy focusing and spatial interference rejection. By taking into account the effects of imperfect channel estimation via pilot-assisted training, in this paper, we analytically show that the average received signal-to-noise ratio in both the single-user and multiuser uplink transmissions can be improved by the EM-lens enabled system. Furthermore, we demonstrate that the proposed design makes it possible to considerably reduce the hardware and signal processing costs with only slight degradations in performance. To this end, two complexity/cost reduction schemes are proposed, which are small-MIMO processing with parallel receiver filtering applied over subgroups of antennas to reduce the computational complexity, and channel covariance based antenna selection to reduce the required number of radio frequency chains. Numerical results are provided to corroborate our analysis and show the great potential advantages of our proposed EM-lens enabled MIMO system for next generation cellular networks.
TL;DR: This paper model and analyze the interaction among one MNO and multiple APOs (for the amount of MNO's offloading data and the respective APOs' compensations) by using thew Nash bargaining theory and sheds light on the economic aspects and the possible outcomes of the MNO/APOs interactions and can be used as a roadmap for designing policies for this promising data offloading solution.
Abstract: The unprecedented growth of mobile data traffic challenges the performance and economic viability of today's cellular networks and calls for novel network architectures and communication solutions. Data offloading through third-party WiFi or femtocell access points (APs) can effectively alleviate the cellular network congestion in low operational and capital expenditure. This solution requires the cooperation and agreement of mobile cellular network operators (MNOs) and AP owners (APOs). In this paper, we model and analyze the interaction among one MNO and multiple APOs (for the amount of MNO's offloading data and the respective APOs' compensations) by using thew Nash bargaining theory. Specifically, we introduce a one-to-many bargaining game among the MNO and APOs and analyze the bargaining solution (game equilibrium) systematically under two different bargaining protocols: 1) sequential bargaining, where the MNO bargains with APOs sequentially, with one APO at a time, in a given order; and 2) concurrent bargaining, where the MNO bargains with all APOs concurrently. We quantify the benefits for APOs when bargaining sequentially and earlier with the MNO, and the losses for APOs when bargaining concurrently with the MNO. We further study the group bargaining scenario where multiple APOs form a group bargaining with the MNO jointly and quantify the benefits for APOs when forming such a group. Interestingly, our analysis indicates that grouping of APOs not only benefits the APOs in the group but may also benefit some APOs not in the group. Our results shed light on the economic aspects and the possible outcomes of the MNO/APOs interactions and can be used as a roadmap for designing policies for this promising data offloading solution.
TL;DR: A low-complexity alternative for soft-output decoding of polar codes that offers better performance but with significantly reduced processing and storage requirements is proposed.
Abstract: The state-of-the-art soft-output decoder for polar codes is a message-passing algorithm based on belief propagation, which performs well at the cost of high processing and storage requirements. In this paper, we propose a low-complexity alternative for soft-output decoding of polar codes that offers better performance but with significantly reduced processing and storage requirements. In particular we show that the complexity of the proposed decoder is only 4% of the total complexity of the belief propagation decoder for a rate one-half polar code of dimension 4096 in the dicode channel, while achieving comparable error-rate performance. Furthermore, we show that the proposed decoder requires about 39% of the memory required by the belief propagation decoder for a block length of 32768.
TL;DR: A new radio resource management algorithm which aims at minimizing the base station supply power consumption for multi-user MIMO-OFDM and optimizes the trade-off between three basic power-saving mechanisms: antenna adaptation, power control and discontinuous transmission.
Abstract: We propose a new radio resource management algorithm which aims at minimizing the base station supply power consumption for multi-user MIMO-OFDM. Given a base station power model that establishes a relation between the RF transmit power and the supply power consumption, the algorithm optimizes the trade-off between three basic power-saving mechanisms: antenna adaptation, power control and discontinuous transmission. The algorithm comprises two steps: a) the first step estimates sleep mode duration, resource shares and antenna configuration based on average channel conditions and b) the second step exploits instantaneous channel knowledge at the transmitter for frequency selective time-variant channels. The proposed algorithm finds the number of transmit antennas, the RF transmission power per resource unit and spatial channel, the number of discontinuous transmission time slots, and the multi-user resource allocation, such that supply power consumption is minimized. Simulation results indicate that the proposed algorithm is capable of reducing the supply power consumption by between 25% and 40%, dependend on the system load.