Discrete Rate Adaptation Research Articles

Power and Discrete Rate Adaptation in Wideband NOMA in Frequency-Selective Channels

Power and Discrete Rate Adaptation in Wideband NOMA in Frequency-Selective Channels

Discrete Data Rate Adaptation for Wireless Body Area Networks

eHealth services require continuous data streaming and a stable level of quality of service. However, wireless network connections can be characterized by variable bandwidths. This requires continuous adaptation of systems, including adapting the bit rates of data streamed by sensors. Assigning appropriate rates to the data represents a main task in congestion control. Most of the current methods look for proper sensor data rates within continuous domains. We examine the case when sensors can generate data streams with several different qualities (e.g., sampling rates, sampling accuracies, etc.). For this reason, the domain of the data rate values can be restricted to the discrete values representing the data rates of the possible quality variations. This paper examines the optimization of the utility of the delivered data under resource constraints by selecting an appropriate variation of the provided data from a discrete set. We provide a formal model for delivering data streams in WBANs and recommend an optimization algorithm to solve the problem. Our recommended solutions are related to the multiple-choice multidimensional knapsack problem. By comparing the proposed algorithms, we found that the greedy method closely approximates the optimum in a short running time.

Power and discrete rate adaptation in BER constrained wireless powered communication networks

Optimal system throughput is a crucial design issue in wireless powered communication networks. Unlike related literature, this study considers the problem of maximising throughput (MTP) for a practical scenario, in which each user node (UN) selects its own rate from a discrete rate set and controls its transmission power according to its own channel state. The MTP with a bit-error-rate constraint is investigated based on two general uplink access methods. The formulation size is exponentially large with respect to the problem input. A novel greedy algorithm based on the column generation method (GA-CGM) is proposed to solve the problem efficiently. The GA-CGM decomposes the problem into a master problem and a subproblem. The master problem is solved by the simplex method. The subproblem is solved by a greedy algorithm due to its non-linearity programming model. The max-min throughput problem (MMTP) is also considered, because of the `doubly near-far' phenomenon which leads to the unfair throughput among different UNs. Experimental results demonstrate that the proposed solutions are close to the optimal solutions for MTP and MMTP problems. They also show increasing the transmission power of a base station or decreasing the path-loss exponent improves the throughput performance.

Base Station-Side Rate Estimation for Threshold-Based Feedback, and Design Implications in Multi-User OFDM Systems

Rate adaptation and scheduling are essential in ensuring that contemporary orthogonal frequency division multiplexing systems achieve high downlink spectral efficiencies. They depend upon reduced feedback schemes to efficiently feedback channel state information from the users to the base station (BS). In the popular threshold-based quantized feedback scheme, a user feeds back to the BS the quantized value of the signal-to-noise ratio for each subchannel. For this scheme, we derive a novel, throughput-optimal discrete rate adaptation (TORA) policy, which enables a system designer to reduce the feedback overhead. We present it in closed form for different multi-antenna diversity modes for the exponentially correlated subchannel gains model. We also develop a computationally simpler suboptimal variant of it. We derive an insightful lower bound for the fading- and user location-averaged throughput gain achieved by TORA over conventional rate adaptation for 1-bit feedback. We present extensive results to benchmark the system-level performance of TORA for different numbers of feedback bits and modulation and coding schemes available at the BS, and various schedulers, quantizers, and multi-antenna modes.

New transmission scheme to enhance throughput of DF relay network using rate and power adaptation

ABSTRACTThis paper presents a new transmission scheme for a decode and forward (DF) relay network using continuous power adaptation while independent average power constraints are provisioned for each node. To have analytical insight, the achievable throughputs are analysed using continuous adaptation of the rates and the powers. As shown by numerical evaluations, a considerable outperformance is seen by continuous power adaptation compared to the case where constant powers are utilised. Also for practical systems, a new throughput maximised transmission scheme is developed using discrete rate adaptation (adaptive modulation and coding) and continuous transmission power adaptation. First a 2-hop relay network is considered and then the scheme is extended for an N-hop network. Numerical evaluations show the efficiency of the designed schemes.

Delay-aware downlink beamforming with discrete rate adaptation for green cloud radio access network

With the popularity of variety delay-sensitive services, how to guarantee the delay requirements for mobile users (MUs) is a great challenge for downlink beamformer design in green cloud radio access networks (C-RANs). In this paper, we consider the problem of the delay-aware downlink beamforming with discrete rate adaptation to minimize the power consumption of C-RANs. We address the problem via a mixed integer nonlinear program (MINLP), and then reformulate the MINLP problem as a mixed integer second-order cone program (MI-SOCP), which is a convex program when the integer variables are relaxed as continuous ones. Based on this formulation, a deflation algorithm, whose computational complexity is polynomial, is proposed to derive the suboptimal solution. The simulation results are presented to validate the effectiveness of our proposed algorithm.

Optimal Power and Modulation Adaptation Policies with Receiver Diversity over Rayleigh Fading Channel

<p>Efficient bandwidth utilization is paramount in wireless communication systems, particulary in fading<br />environments, since fading is one of the major constraints that impair communication in wireless systems.<br />The bandwidth efficiency of a wireless communication system can be enhanced significantly by employing<br />power and modulation adaptation policies with diversity combining gain. In this work, first we examine an<br />analytically-derived solution for Maximum Combining Ratio (MRC) diversity technique for the capacity per<br />unit bandwidth. Then, we design an adaptive transmission system to utilize the diversity combining gain while<br />retaining the target BER by adapting power and constellation size using continuous power, channel inversion<br />with fixed rate and continuous power and disrecte-rate. By considering the effect of diversity combining<br />gain, the designed system yields a reasonable spectral efficiency with respect to target BER that grows as<br />the number of diversity levels increase. Furthermore, the presented results show continuous power and<br />discrete-rate adaptation policy reduces probability of outage unlike its achieved spectral efficiency is close<br />to other selected policies, which ratifies the optimized switching thresholds and makes it best candidate for<br />imperfect channel conditions.</p>

Optimizing the throughput‐lifetime tradeoff in wireless sensor networks with link scheduling, rate adaptation, and power control

AbstractThroughput and lifetime are usually conflicting objectives in designing wireless sensor networks; hence, the right balance needs to be found. With this aim in view, we address in this paper the problem of minimizing the frame length defined within a time division multiple access scheme and the problem of maximizing network lifetime subject to a maximum frame length. The pursued solution in either case leverages a wide range of parameters related to coverage, routing, transmission power, and data rate. Furthermore, it is consistent with the physical interference model. To this end, we rely on column generation technique to derive near‐optimal solutions even when the integrality constraints on coverage and flow variables are enforced. Moreover, we propose a polynomial‐time heuristic algorithm to solve efficiently the underlying NP‐hard problem of concurrent link selection with discrete power control and rate adaptation. Simulation results show that our heuristic algorithm leads to solutions within 3% of optimality while saving around 99% of computation time. Besides, the results illustrate the significant impact of power control and rate adaptation on throughput and lifetime improvement. Interestingly, we found that network lifetime can be significantly prolonged when traffic demands are sufficiently low at the affordable cost of small decrease in throughput. Copyright © 2015 John Wiley & Sons, Ltd.

Performance of OFDM Systems With Best- <inline-formula> <tex-math notation="TeX">$m$</tex-math></inline-formula> Feedback, Scheduling, and Delays for Uniformly Correlated Subchannels

Contemporary cellular standards, such as Long Term Evolution (LTE) and LTE-Advanced, employ orthogonal frequency-division multiplexing (OFDM) and use frequency-domain scheduling and rate adaptation. In conjunction with feedback reduction schemes, high downlink spectral efficiencies are achieved while limiting the uplink feedback overhead. One such important scheme that has been adopted by these standards is best- m feedback, in which every user feeds back its m largest subchannel (SC) power gains and their corresponding indices. We analyze the single cell average throughput of an OFDM system with uniformly correlated SC gains that employs best- m feedback and discrete rate adaptation. Our model incorporates three schedulers that cover a wide range of the throughput versus fairness tradeoff and feedback delay. We show that, for small m, correlation significantly reduces average throughput with best- m feedback. This result is pertinent as even in typical dispersive channels, correlation is high. We observe that the schedulers exhibit varied sensitivities to correlation and feedback delay. The analysis also leads to insightful expressions for the average throughput in the asymptotic regime of a large number of users.

Joint Discrete Rate Adaptation and Downlink Beamforming Using Mixed Integer Conic Programming

Multiuser downlink beamforming for sum-rate maximization has been intensively studied in the literature assuming that the achievable data rates of the mobile stations (MSs) are continuous and strictly increasing functions of the received signal-to-interference-plus-noise ratios (SINRs). However, in practical cellular networks that employ adaptive modulation and coding, the data rates of the MSs are determined by the specific modulation and coding schemes and thus attain discrete values. We consider in this paper discrete rate adaptation and downlink beamforming (RAB), where the discrete rate assignment is jointly optimized along with the beamformer design to achieve the maximum sum-rate with minimum total transmitted power of the base station. User admission control is embedded in the discrete rate assignment procedure. We address the RAB problem using a mixed integer second-order cone program (MI-SOCP) approach, proposing a standard big-M MI-SOCP formulation that supports the branch-and-cut (BnC) method. To reduce the complexity of the BnC algorithm, we further develop an improved extended MI-SOCP formulation. We analytically show that the extended formulation generally admits strictly tighter continuous relaxations (and thus less computational complexity) than that of the big-M formulation. Efficient strategies are proposed to customize the standard BnC method for the RAB problem. For applications in large-scale networks, we develop low-complexity SOCP based inflation and deflation procedures to find suboptimal solutions of the RAB problem. Simulations show that the inflation and deflation procedures yield sum-rates that are very close to that of the optimal solutions.

Discrete-Rate Adaptation and Selection in Energy Harvesting Wireless Systems

In a system with energy harvesting (EH) nodes, the design focus shifts from minimizing energy consumption by infrequently transmitting less information to making the best use of available energy to efficiently deliver data while adhering to the fundamental energy neutrality constraint. We address the problem of maximizing the throughput of a system consisting of rate-adaptive EH nodes that transmit to a destination. Unlike related literature, we focus on the practically important discrete-rate adaptation model. First, for a single EH node, we propose a discrete-rate adaptation rule and prove its optimality for a general class of stationary and ergodic EH and fading processes. We then study a general system with multiple EH nodes in which one is opportunistically selected to transmit. We first derive a novel and throughput-optimal joint selection and rate adaptation rule (TOJSRA) when the nodes are subject to a weaker average power constraint. We then propose a novel rule for a multi-EH node system that is based on TOJSRA, and we prove its optimality for stationary and ergodic EH and fading processes. We also model the various energy overheads of the EH nodes and characterize their effect on the adaptation policy and the system throughput.

Variable-Rate, Variable-Power Network-Coded-QAM/PSK for Bi-Directional Relaying Over Fading Channels

Network coded modulation (NCM) holds the promise of significantly improving the efficiency of two-way wireless relaying. In this contribution, we propose near instantaneously adaptive variable-rate, variable-power QAM/PSK for NC-aided decode-and-forward two-way relaying (DF-TWR) to maximize the average throughput. The proposed scheme is optimized subject to both average-power and bit-error-ratio (BER) constraints. Based on the BER bounds, we investigate a discrete-rate adaptation scheme, relying on a pair of solutions proposed for maximizing the spectral efficiency of the network. We then derive a closed-form solution based power adaptation policy for a continuous-rate scheme and quantify the signal-to-noise ratio (SNR) loss imposed by NC-QAM. Our simulation results demonstrate that the proposed discrete adaptive NC-QAM/PSK schemes are capable of attaining a higher spectral efficiency than their fixed-power counterparts.

Spectral efficiency optimisation in amplify and forward relay network with diversity using adaptive rate and adaptive power transmission

In this study, new transmission schemes are designed for the spectral efficiency optimisation in the amplify and forward relay network with diversity. The proposed schemes utilise both adaptive rate and adaptive power transmission with individual average power constraints for the relay and the transmitter. To obtain an analytical insight into the network performance, schemes with continuous rate adaptation are designed at first. Next, transmission schemes with discrete rate and power adaptation are developed for practical systems. An amplify–forward relaying scenario with diversity is considered in which signals at the destination are received from both the transmitter and the relay. Albeit, the schemes could be simplified for the network without diversity in which there is no path for the signal from the transmitter to the receiver. Schemes with constant power transmission are also designed and analysed. Numerical evaluations show that there is a considerable performance gain in using adaptive power transmission compared to the constant power scheme at the cost of a reasonable complexity increase.

Performance analysis of spectrally efficient amplify-and-forward opportunistic relaying scheme for adaptive cooperative wireless systems

In this paper, we propose an adaptive amplify-and-forward AF relaying scheme that selects the best relay among the available relay nodes opportunistically to cooperate with a source node for improvement of the spectral efficiency. This improvement can be achieved by introducing a policy that gives the useful cooperative regions and defines a switching threshold signal-to-noise ratio that guarantees the bit error rate BER of cooperative transmission is below the target. We model all links as independent non-identically distributed Rayleigh fading channels. We then derive closed-form expressions for the average spectral efficiency, average BER, and outage probability when an upper bound for the signal-to-noise ratio of the end-to-end relay path is applied and adaptive discrete rate is considered. Numerical and simulation results show that the proposed scheme, compared with the outage-based AF incremental relaying, AF fixed relaying, and the conventional direct transmission, can achieve the maximum average spectral efficiency while maintaining the average BER and outage probability. Copyright © 2013 John Wiley & Sons, Ltd.

Interplay Between Optimal Selection Scheme, Selection Criterion, and Discrete Rate Adaptation in Opportunistic Wireless Systems

An opportunistic, rate-adaptive system exploits multi-user diversity by selecting the best node, which has the highest channel power gain, and adapting the data rate to selected node's channel gain. Since channel knowledge is local to a node, we propose using a distributed, low-feedback timer backoff scheme to select the best node. It uses a mapping that maps the channel gain, or, in general, a real-valued metric, to a timer value. The mapping is such that timers of nodes with higher metrics expire earlier. Our goal is to maximize the system throughput when rate adaptation is discrete, as is the case in practice. To improve throughput, we use a pragmatic selection policy, in which even a node other than the best node can be selected. We derive several novel, insightful results about the optimal mapping and develop an algorithm to compute it. These results bring out the inter-relationship between the discrete rate adaptation rule, optimal mapping, and selection policy. We also extensively benchmark the performance of the optimal mapping with several timer and opportunistic multiple access schemes considered in the literature, and demonstrate that the developed scheme is effective in many regimes of interest.

Variable-Rate Variable-Power MQAM for Energy Efficient Transmission over Fading Channels

Wireless communication devices are often battery-powered. Reducing energy consumption during transmission has become a major challenge. We propose an optimal adaptation scheme and a suboptimal scheme based on the continuous-power discrete-rate adaptation (CPDR) for energy efficient transmission over fading channels. In our proposed schemes, we optimize the average transmit power according to data rate of the source node and buffer level, and then we adapt the transmit rate and power according to the CPDR scheme. Simulation results show that our proposed schemes have outstanding performance on energy-saving.

Joint Rate Adaptation and Best-Relay Selection Using Limited Feedback

This paper presents a novel joint rate adaptation and relay selection scheme for multi-relay networks adopting half-duplex best-relay decode-and-forward protocol. The proposed scheme aims to maximize the overall transmission rate when relays are allowed to forward messages using different rates from the source. It is shown that the proposed scheme outperforms the conventional adaptive scheme in terms of the spectral efficiency (e.g. by 10.1% improvement for SNR=10 dB). Furthermore, in order to reduce signaling overhead of the proposed scheme, a number of joint discrete-rate adaptation and relay selection approaches are proposed for both non-reciprocal and reciprocal channels. The relay selection is basically a two-stage scheme. At the rm first stage, a set of relays are selected based on mixed channel quality information (CQI), i.e., the knowledge of CQI varies for different links; at the second stage, the best relay within the set is selected based on instantaneous CQI, which is obtained through carefully designed signaling protocols. It is shown that the proposed discrete-rate adaptation schemes can offer comparable spectral efficiency to the conventional adaptive scheme with significantly reduced signaling overhead.

Energy-Efficient Link Adaptation on Rayleigh Fading Channel for OSTBC MIMO System With Imperfect CSIT

We propose an energy-efficient adaptive transmission scheme for multiple-input–multiple-output (MIMO) beamforming systems with orthogonal space–time block coding (OSTBC) based on imperfect channel state information at the transmitter (CSIT). The transmission rate and transmit power are adapted to maximize the energy efficiency (EE) subject to an instantaneous bit-error-rate (I-BER) constraint. We solve this nonconcave problem based on the generalized convexity. Closed-form expressions of the energy-efficient transmission rate and transmit power for both modulations with constant power amplifier (PA) inefficiency and $M$ -ary quadratic-amplitude modulation (MQAM) with nonconstant PA inefficiency are obtained. Based on these results, we also analyze four special cases: The circuit power dissipation is negligible, the CSIT is perfect, the CSIT is only channel distribution information (CDI), and the discrete rate adaptation policy is employed. Numerical results are also given to further illustrate the impact of CSIT imperfection to the transmitter EE.

Cross‐layer resource allocation in orthogonal frequency multiple access systems based on channel distribution information

In this study, the authors propose a cross-layer resource allocation scheme for orthogonal frequency multiple access (OFDM) systems based on the subcarrier channel distribution information (CDI). Here, in contrast to most of the previous works, the authors consider practical discrete rate adaptation in which rate is adapted using a predefined set of modulation levels corresponding to fading regions. The OFDM system supports 'streaming traffic' that requires a minimum guaranteed average delay, as well as 'elastic traffic' with flexible rate requirements. The main objective is to maximise the total average transmission rate of elastic users, whereas the average delay is also guaranteed for streaming traffic, all subject to the maximum average transmission power constraint. The authors then propose an algorithm which finds suboptimal fading regions, as well as suboptimal subcarrier allocation based on dual decomposition method. In the proposed method, the solution of optimisation problem remains valid, until the channel distributions and/or the packet arrival and packet length distribution of the users are changed. This significantly reduces the computational complexity since there is no need to solve the optimisation problem for each channel side information (CSI) feedback. The proposed scheme also reduces the signalling overhead since corresponding to each user the CSI is required only for the assigned subcarriers. Particularly, the proposed method is suitable for cases in which wireless channel distributions is not changed, or can be simply estimated, for example, cases where a parametric distribution is fixed and only statistics such as average and variance is changed. Using simulations, the authors study the impact of number of fading regions and packet arrival rate on the achievable rate of elastic traffic.

Optimal and efficient adaptation in distributed real-time systems with discrete rates

Many distributed real-time systems face the challenge of dynamically maximizing system utility and meeting stringent resource constraints in response to fluctuations in system workload. Thus, online adaptation must be adopted in face of workload changes in such systems. We present the MultiParametric Rate Adaptation (MPRA) algorithm for discrete rate adaptation in distributed real-time systems with end-to-end tasks. The key novelty and advantage of MPRA is that it can efficiently produce optimal solutions in response to workload variations caused by dynamic task arrivals and departures. Through offline preprocessing MPRA transforms an NP-hard utility optimization problem to the evaluation of a piecewise linear function of the CPU utilization. At run time MPRA produces optimal solutions by evaluating the function based on the CPU utilization. Analysis and simulation results show that MPRA maximizes system utility in the presence of varying workloads, while reducing the online computation complexity to polynomial time. The advantages of MPRA have been validated through the implementation in a real-time middleware system and experiments on a physical testbed.