Rate Allocation Policy Research Articles

Information Freshness in Cache Updating Systems

We consider a cache updating system with a source, a cache and a user. There are $n$ files. The source keeps the freshest version of the files which are updated with known rates $\lambda _{i}$ . The cache downloads and keeps the freshest version of the files from the source with rates $c_{i}$ . The user gets updates from the cache with rates $u_{i}$ . When the user gets an update, it either gets a fresh update from the cache or the file at the cache becomes outdated by a file update at the source in which case the user gets an outdated update. We find an analytical expression for the average freshness of the files at the user. Next, we generalize our setting to the case where there are multiple caches in between the source and the user, and find the average freshness at the user. We provide an alternating maximization based method to find the update rates for the cache(s), $c_{i}$ , and for the user, $u_{i}$ , to maximize the freshness of the files at the user. We observe that for a given set of update rates for the user (resp. for the cache), the optimal rate allocation policy for the cache (resp. for the user) is a threshold policy , where the optimal update rates for rapidly changing files at the source may be equal to zero. Finally, we consider a system where multiple users are connected to a single cache and find update rates for the cache and the users to maximize the total freshness over all users.

Control Policies Approaching Hierarchical Greedy Ideal Performance in Heavy Traffic for Resource Sharing Networks

We consider resource sharing networks of the form introduced in work of Massoulié and Roberts as models for Internet flows. The goal is to study the open problem, formulated in Harrison et al. (2014) [Harrison JM, Mandayam C, Shah D, Yang Y (2014) Resource sharing networks: Overview and an open problem. Stochastic Systems 4(2):524–555.], of constructing simple form rate-allocation policies for broad families of resource sharing networks with associated costs converging to the hierarchical greedy ideal performance in the heavy traffic limit. We consider two types of cost criteria: an infinite horizon discounted cost and a long-time average cost per unit time. We introduce a sequence of rate-allocation control policies that are determined in terms of certain thresholds for the scaled queue-length processes and prove that, under conditions, both type of costs associated with these policies converge in the heavy traffic limit to the corresponding hierarchical greedy ideal (HGI) performance. The conditions needed for these results are satisfied by all the examples considered in the above cited paper of Harrison et al.

On Optimal Quantized Non-Bayesian Quickest Change Detection With Energy Harvesting

In this paper, we consider a problem of decentralized non-Bayesian quickest change detection using a wireless sensor network where the sensor nodes are powered by harvested energy from the environment. The underlying random process being monitored by the sensors is subject to change in its distribution at an unknown but deterministic time point, and the sensors take samples (sensing) periodically, compute the likelihood ratio based on the distributions before and after the change, quantize it and send it to a remote fusion centre (FC) over fading channels for performing a sequential test to detect the change. Due to the unpredictable and intermittent nature of harvested energy arrivals, the sensors need to decide whether they want to sense, and at what rate they want to quantize their information before sending them to the FC, since higher quantization rates result in higher accuracy and better detection performance, at the cost of higher energy consumption. We formulate an optimal sensing and quantization rate allocation problem (in order to minimize the expected detection delay subject to false alarm rate constraint) based on the availability (at the FC) of non-causal and causal information of sensors’ energy state information, and channel state information between the sensors and the FC. Motivated by the asymptotically inverse relationship between the expected detection delay (under a vanishingly small probability of false alarm) and the Kullback-Leibler (KL) divergence measure at the FC, we maximize an expected sum of the KL divergence measure over a finite horizon to obtain the optimal sensing and quantization rate allocation policy, subject to energy causality constraints at each sensor. The optimal solution is obtained using a typical dynamic programming based technique, and based on the optimal quantization rate, the optimal quantization thresholds are found by maximizing the KL information measure per slot. We also provide suboptimal threshold design policies using uniform quantization and an asymptotically optimal quantization policy for higher number of quantization bits. We provide an asymptotic approximation for the loss due to quantization of the KL measure, and also consider an alternative optimization problem with minimizing the expected sum of the inverse the KL divergence measure as the cost per time slot. Numerical results are provided comparing the various optimal and suboptimal quantization strategies for both optimization problem formulations, illustrating the comparative performance of these strategies at different regimes of quantization rates.

Performance of Multi-Cell Massive MIMO Systems With Interference Decoding

We consider a multi-cell massive MIMO system where a time-division duplex protocol is used to estimate the channel state information via uplink pilots. When maximum ratio combining (MRC) is used at the BSs, the re-use of pilots across cells causes the pilot contamination effect which yields interference components that do not vanish as the number of base-station (BS) antennas $M \rightarrow \infty$. When treating interference as noise (TIN), this phenomenon limits the performance of multi-cell massive MIMO systems. In this paper, we analyze more advanced schemes based on simultaneous unique decoding (SD) as well as simultaneous non-unique decoding (SND) of the interference that can provide unbounded rate as $M \rightarrow \infty$. We also establish a worst-case uncorrelated noise technique for multiple-access channels to derive achievable rate expressions for finite $M$. Furthermore, we study a much simpler subset of SND (called S-SND) which provides a lower bound to SND and achieves unbounded rate as $M \rightarrow \infty$, and also outperforms SD for finite $M$. For the special cases of two-cell and three-cell systems, using a maximum symmetric rate allocation policy we compare the performance of different interference decoding schemes with that of TIN. Finally, we numerically illustrate the improved performance of the proposed schemes.

A unified approach of energy and data cooperation in energy harvesting WSNs

Energy harvesting (EH) provisioned wireless sensor nodes are key enablers to increase network life time in modern wireless sensor networks (WSNs). However, the intermittent nature of the EH process necessitates management of nodes limited data and energy buffer capacity. In this paper, a unified mathematical model for a cooperative EHWSN with an opportunistic relay is presented. The energy and data causality constraints are expressed in terms of throughput, available energy, delay and transmission time. Considering finite energy buffers, data buffers and discrete transmission rates (as defined in the standard IEEE 802.15.4) at the nodes, different intuitive online power allocation policies at the relay are studied. The results show that a policy achieving high throughput is less fair and vice versa. Therefore, a joint rate and power allocation policy (JRPAP) is proposed in this study which provides a better trade off between fairness, throughput and energy over intuitive policies. Based on the JRPAP results, we propose to use data aggregation (DA) to achieve throughput gain at lower buffer sizes. In addition, the notion of energy aggregation (EA) is introduced to achieve throughput gain at higher buffer sizes. Combining both EA and DA further improves the overall throughput at all buffer sizes.

Energy and Data Cooperative Multiple Access Channel With Intermittent Data Arrivals

We consider an energy harvesting two user cooperative Gaussian multiple access channel, where both of the users harvest energy from nature. The users cooperate at the physical layer (data cooperation) by establishing common messages through overheard signals and then cooperatively sending them. We study two scenarios within this model. In the first scenario, the data packets arrive intermittently over time. We find the optimal offline transmit power and rate allocation policy that maximize the departure region. We first show that there exists an optimal policy, in which the single user rate constraints in each time slot are tight, yielding a one-to-one relation between the powers and rates. Then, we formulate the departure region maximization problem as a weighted sum departure maximization in terms of rates only. Next, we propose a sequential convex approximation method to approximate the problem at each step and show that it converges to the optimal solution. We solve the approximate problems using an inner–outer decomposition method. In the second scenario, the data packets are available at the beginning of the transmission, but the users now have the ability to cooperate at the battery level (energy cooperation), in addition to data cooperation. The energy cooperation is facilitated by wireless energy transfer and is bidirectional. For this scenario, we find the jointly optimal offline transmit power and rate allocation policy together with the energy transfer policy that maximize the departure region. We provide necessary conditions for energy transfer and prove some properties of the optimal transmit policy, thereby shedding some light on the interplay between energy and data cooperation.

Reconstruction of Correlated Sources With Energy Harvesting Constraints in Delay-Constrained and Delay-Tolerant Communication Scenarios

In this paper, we investigate the reconstruction of time-correlated sources in a point-to-point communications scenario comprising an energy-harvesting sensor and a Fusion Center (FC). Our goal is to minimize the average distortion in the reconstructed observations by using data from previously encoded sources as side information. First, we analyze a delay-constrained scenario, where the sources must be reconstructed before the next time slot. We formulate the problem in a convex optimization framework and derive the optimal transmission (i.e., power and rate allocation) policy. To solve this problem, we propose an iterative algorithm based on the subgradient method. Interestingly, the solution to the problem consists of a coupling between a 2-D directional water-filling algorithm (for power allocation) and a reverse water-filling algorithm (for rate allocation). Then, we find a more general solution to this problem in a delay-tolerant scenario where the time horizon for source reconstruction is extended to multiple time slots. Finally, we provide some numerical results that illustrate the impact of delay and correlation in the power and rate allocation policies, and in the resulting reconstruction distortion. We also discuss the performance gap exhibited by a heuristic online policy derived from the optimal (offline) one.

Fair Virtualization of 802.11 Networks

We consider virtualization of network capacity in 802.11 WLANs and mesh networks. We show that allocating total airtime slices to ISPs is analogous to allocating a fraction of available time-slots in TDMA. We establish that the max-min fair flow rate allocation within an ISP airtime slice can be characterized independently of the rate allocation policy employed in other slices. Building on these observations, we present a lightweight, distributed algorithm for allocating airtime slices among ISP and max-min fair flow rates within each slice.

Providing Differentiated Services in Multiaccess Systems With and Without Queue State Information

In this paper, we consider one quality-of-service (QoS) criterion, average packet delay (queueing delay plus service time), in a multiaccess system and investigate the basic problem whether a multiaccess system can meet the different average packet delay requirements of all users by combining information theory with queueing theory. Two different cases of the central scheduler with and without queue state information (QSI) are discussed. If the QSI is not available to the central scheduler, we show that static rate allocation policies (SRAPs) can achieve better average packet delay performance than probabilistic rate allocation policies. Based on this conclusion, the delay feasibility checking process reduces to checking whether the required service rate vector lies in the multiaccess capacity region. We find that for users with equal transmit power, only N inequalities are necessary for the checking process, whereas for users with unequal transmit powers, we provide a polynomial-time algorithm for such a decision. Furthermore, if the system cannot satisfy the average packet delay requirements of all users, we prove that as long as the sum power is larger than a threshold, there is always an approach to adjust the transmit powers of different users to satisfy the average packet delay requirements. On the other hand, if the QSI is available to the central scheduler, we propose two dynamic scheduling algorithms to achieve proportional average packet delay and compare their performances with optimal SRAP by simulations.

Capacity Analysis of Adaptive Transmission with Space–Time Block Codes in Spatially Correlated MIMO Nakagami- $$m$$ m Fading Channels

In this paper we investigate the impacts of spatial fading correlation on channel capacity of orthogonal space---time block codes (STBCs) over multiple-input multiple-output Nakagami- $$m$$ m fading under different power and rate allocation policy. This paper presents the single-input single-output equivalency of STBC which is equivalent to scalar additive white Gaussian noise channel, with a channel gain proportional to the Frobenius norm of the matrix channel. We derive the characteristic function (c.f.) of the signal-to-noise ratio at the output of the STBC decoder, when spatial fading correlation is assumed. The c.f. turns out to be a linear combination of Laplace transforms weighted by the eigenvalues of the channel correlation matrix, where each element corresponds to an elementary Gamma probability density function. Based on this approach, and using the linearity of the Laplace transform, the capacity expressions are derived for STBC under different adaptive transmission policy. Numerical results illustrate the impact of fading correlation and fading severity on the channel capacity and outline the performance differences between the distinctive adaptation policies.

Rate Allocation and Adaptation for Incremental Redundancy Truncated HARQ

This paper considers incremental redundancy hybrid ARQ (HARQ) transmission over independent block-fading channels. The transmitter, having no knowledge of the instantaneous channel state information (CSI) can or - allocate the transmission rate knowing the statistics of the channel, or - adapt the transmissions rates using the outdated CSI, i.e., the one experienced by the receiver in the past transmissions that resulted in a packet decoding failure. Aiming at throughput maximization problems under constraint on the outage probability, we show how to optimize the rate-adaptation and rate-allocation policies using dynamic programming framework. Numerical examples obtained in a Rayleigh-fading channel show that rate adaptation provides notable gains over a rate allocation and non-adaptive HARQ, and, for high SNR, only a few transmissions are necessary to approach closely the ergodic capacity.

Multi-rate control policies for elastic traffic in CDMA networks

In this paper a rate control scheme for downlink packet transmission in CDMA networks is proposed based on both the queue lengths and the channel states of mobile users. We are interested in optimal rate allocation policies for throughput maximisation over time and thus we formulate the problem as a discrete stochastic dynamic program. This dynamic program is exponentially complex in the number of users, which renders it impractical and therefore we use an approximate dynamic programming (DP) algorithm to obtain sub-optimal rate allocation policies in real time. The numerical results reveal that the proposed algorithm significantly outperforms a number of different baseline greedy heuristics.

Sensing Based Joint Rate and Power Allocations for Cognitive Radio Systems

In this paper, we propose a spectrum sensing based joint rate and power allocation scheme for Cognitive Radio (CR) systems. By exploiting both its sensing result and sensing accuracy, the Secondary User (SU) adaptively determines its transmission policy to maximize its effective throughput which is achievable without requiring any Primary User's (PU's) true state information and thus is more practical than the performance metric of ergodic capacity used by . We propose an efficient algorithm to find the optimal SU rate and power allocation policy by exploiting the special structure of the problem. Based on the optimal policy, we further quantify sufficient conditions under which it is beneficial for the SU to use the PU channel even the SU sensing result indicates that the PU is busy by anticipating the happening of sensing error (but still with the PU interference constraint satisfied). Our results validate the advantages of exploiting both the information on sensing result and sensing accuracy in the overlay secondary spectrum access.

Optimal error considerate cross-layer scheduler for heterogeneous users

A cross-layer scheduling algorithm that combines packet scheduling and an efficient bandwidth allocation for orthogonal frequency division multiple access (OFDMA) systems with heterogeneous user's delay requirements are proposed. The proposed work employs channel fluctuation and queue states in their decision rules for packet scheduling, which determines the optimum subcarrier allocation policy to maximise the system throughput subject to individual user's delay constraints. The delay sensitive subcarrier allocation strategy has linear complexity with respect to the number of users and number of subcarriers. The cross-layer scheduling algorithm also use an adaptive modulation and coding (AMC) scheme, which adapt according to the channel condition and queue states in terms of signal-to-noise ratio (SNR) constraints to improve the bandwidth usage in an efficient manner. The simulation results show that substantial throughput gain and spectral efficiency improvement are obtained while satisfying the delay constraint when the delay sensitive jointly optimum rate and subcarrier allocation policy with AMC is adopted.

Secure Resource Allocation and Scheduling for OFDMA Decode-and-Forward Relay Networks

In this paper, we formulate an optimization problem for secure resource allocation and scheduling in orthogonal frequency division multiple access (OFDMA) half-duplex decode-and-forward (DF) relay assisted networks. Our problem formulation takes into account artificial noise generation to combat a passive multiple antenna eavesdropper and the effects of imperfect channel state information at the transmitter (CSIT) in slow fading. The optimization problem is solved by dual decomposition which results in a highly scalable distributed iterative resource allocation algorithm. The packet data rate, secrecy data rate, power, and subcarrier allocation policies are optimized to maximize the average secrecy outage capacity (bit/s/Hz securely and successfully delivered to the users via relays). Simulation results illustrate that our proposed distributed iterative algorithm converges to the optimal solution in a small number of iterations and guarantees a non-zero secrecy data rate for given target secrecy outage and channel outage probability requirements.

Keys Through ARQ: Theory and Practice

This paper develops a novel framework for sharing secret keys using the Automatic Repeat reQuest (ARQ) protocol. We first characterize the underlying information theoretic limits, under different assumptions on the channel spatial and temporal correlation function. Our analysis reveals a novel role of “dumb antennas” in overcoming the negative impact of spatial correlation on the achievable secrecy rates. We further develop an adaptive rate allocation policy, which achieves higher secrecy rates in temporally correlated channels, and explicit constructions for ARQ secrecy coding that enjoy low implementation complexity. Building on this theoretical foundation, we propose a unified framework for ARQ-based secrecy in Wi-Fi networks. By exploiting the existing ARQ mechanism in the IEEE 802.11 standard, we develop security overlays that offer strong security guarantees at the expense of only minor modifications in the medium access layer. Our numerical results establish the achievability of nonzero secrecy rates even when the eavesdropper channel is less noisy, on the average, than the legitimate channel, while our Linux-based prototype demonstrates the efficiency of our ARQ overlays in mitigating all known, passive and active, Wi-Fi attacks at the expense of a minimal increase in the link setup time and a small loss in throughput.

Cross-Layer Scheduling for OFDMA Amplify-and-Forward Relay Networks

In this paper, we consider cross-layer scheduling for the downlink of amplify-and-forward (AF) relay-assisted orthogonal frequency-division multiple-access (OFDMA) networks. The proposed cross-layer design takes into account the effects of imperfect channel-state information (CSI) at the transmitter (CSIT) in slow fading. The rate, power, and subcarrier allocation policies are optimized to maximize the system goodput (in bits per second per hertz successfully received by the users). The optimization problem is solved by using dual decomposition, resulting in a highly scalable distributed iterative resource-allocation algorithm. We also investigate the asymptotic performance of the proposed scheduler with respect to (w.r.t.) the numbers of users and relays. We find that the number of relays should grow faster than the number of users to fully exploit the multiuser diversity (MUD) gain. On the other hand, diversity from multiple relays can be exploited to enhance system performance when the MUD gain is saturated due to noise amplification at the AF relays. Furthermore, we introduce a feedback-reduction scheme to reduce the computational burden and the required amount of CSI feedback from the users to the relays. Simulation results confirm the derived analytical results for the growth of the system goodput and illustrate that the proposed distributed cross-layer scheduler only requires a small number of iterations to achieve practically the same performance as the optimal centralized scheduler, even if the information exchanged between the base station (BS) and the relays in each iteration is quantized, and the proposed CSI feedback reduction scheme is employed.

Distributed Rate Allocation Policies for Multihomed Video Streaming Over Heterogeneous Access Networks

We consider the problem of rate allocation among multiple simultaneous video streams sharing multiple heterogeneous access networks. We develop and evaluate an analytical framework for optimal rate allocation based on observed available bit rate (ABR) and round-trip time (RTT) over each access network and video distortion-rate (DR) characteristics. The rate allocation is formulated as a convex optimization problem that minimizes the total expected distortion of all video streams. We present a distributed approximation of its solution and compare its performance against Hinfin-optimal control and two heuristic schemes based on TCP-style additive-increase-multiplicative-decrease (AIMD) principles. The various rate allocation schemes are evaluated in simulations of multiple high-definition (HD) video streams sharing multiple access networks. Our results demonstrate that, in comparison with heuristic AIMD-based schemes, both media-aware allocation and Hinfin-optimal control benefit from proactive congestion avoidance and reduce the average packet loss rate from 45% to below 2%. Improvement in average received video quality ranges between 1.5 to 10.7 dB in PSNR for various background traffic loads and video playout deadlines. Media-aware allocation further exploits its knowledge of the video DR characteristics to achieve a more balanced video quality among all streams.

Closed-loop cross-layer SDMA designs with outdated CSIT

In this paper, we propose a novel closed-loop approach for robust downlink multi-antenna cross-layer design with outdated channel state information at the transmitter (CSIT). Based on the ACK/NAK feedbacks from the mobiles, the proposed cross-layer design does not require the knowledge of CSIT error statistics. We formulate the cross-layer design as a mixed combinatorial search and Markov decision process (MDP). While it is well-known that general solutions for MDP are very complex, one important contribution of this paper is that we obtain simple closed-form power, rate and user allocation policies which are asymptotically optimal for small target frame error rate (FER). Simulation results illustrate that the performance of the proposed closed-loop cross-layer design is very robust with respect to the outdated CSIT, CSIT error model mismatch as well as channel variations due to Doppler.

On Resource Allocation in Fading Multiple-Access Channels–An Efficient Approximate Projection Approach

We consider the problem of rate and power allocation in a multiple-access channel. Our objective is to obtain rate and power allocation policies that maximize a general concave utility function of average transmission rates on the information theoretic capacity region of the multiple-access channel. Our policies does not require queue-length information. We consider several different scenarios. First, we address the utility maximization problem in a nonfading channel to obtain the optimal operating rates, and present an iterative gradient projection algorithm that uses approximate projection. By exploiting the polymatroid structure of the capacity region, we show that the approximate projection can be implemented in time polynomial in the number of users. Second, we consider resource allocation in a fading channel. Optimal rate and power allocation policies are presented for the case that power control is possible and channel statistics are available. For the case that transmission power is fixed and channel statistics are unknown, we propose a greedy rate allocation policy and provide bounds on the performance difference of this policy and the optimal policy in terms of channel variations and structure of the utility function. We present numerical results that demonstrate superior convergence rate performance for the greedy policy compared to queue-length based policies. In order to reduce the computational complexity of the greedy policy, we present approximate rate allocation policies which track the greedy policy within a certain neighborhood that is characterized in terms of the speed of fading.