Optimal Transmission Strategy Research Articles

A Hybrid Underlay/Overlay Transmission Mode for Cognitive Radio Networks with Statistical Quality-of-Service Provisioning

In order to achieve better statistical Quality-of-Service (QoS) provisioning for cognitive radio networks (CRN), in this paper, we develop a hybrid underlay/overlay transmission mode for CRNs. Specifically, by applying the theory of effective capacity and taking PN's activity statistics into consideration, we first analyze the maximum achievable throughput of the CRN under two dominant transmission modes, namely underlay and overlay, respectively, and provide efficient algorithms to derive optimal transmission strategies for the two modes. Following the analyses, we then propose a hybrid underlay/overlay transmission mode, through which the cognitive users' QoS requirements can be better guaranteed and network throughput can be further improved. Moreover, we analyze the optimal transmission strategies for both underlay and overlay modes under two limiting cases. Analyses indicate that 1) for the loose QoS requirement, optimal transmission strategies for both underlay and overlay modes become the water-filling algorithm; and 2) for the stringent QoS requirement, the cognitive user will transmit with constant rate. Furthermore, the impact of imperfect channel estimations on our proposed transmission mode is discussed. Simulation results are provided to demonstrate the impacts of delay QoS requirements and PN's activity statistics on maximizing the delay-constrained throughput for both underlay and overlay modes and verify the effectiveness of our proposed transmission mode. Moreover, for the overlay mode, we observe that 1) a unique optimal sensing time exists under the given QoS constraint; and 2) the optimal sensing time surprisingly increases as the QoS constraint gets more stringent.

Coded Cooperative Data Exchange in Multihop Networks

Consider a connected network of n nodes that all wish to recover k desired packets. Each node begins with a subset of the desired packets and exchanges coded packets with its neighbors. This paper provides necessary and sufficient conditions that characterize the set of all transmission strategies that permit every node to ultimately learn (recover) all k packets. When the network satisfies certain regularity conditions and packets are randomly distributed, this paper provides tight concentration results on the number of transmissions required to achieve universal recovery. For the case of a fully connected network, a polynomial-time algorithm for computing an optimal transmission strategy is derived. An application to secrecy generation is discussed.

Maximizing Lifetime of Wireless Sensor Networks with Mobile Sink Nodes

In order to maximize network lifetime and balance energy consumption when sink nodes can move, maximizing lifetime of wireless sensor networks with mobile sink nodes (MLMS) is researched. The movement path selection method of sink nodes is proposed. Modified subtractive clustering method, k-means method, and nearest neighbor interpolation method are used to obtain the movement paths. The lifetime optimization model is established under flow constraint, energy consumption constraint, link transmission constraint, and other constraints. The model is solved from the perspective of static and mobile data gathering of sink nodes. Subgradient method is used to solve the lifetime optimization model when one sink node stays at one anchor location. Geometric method is used to evaluate the amount of gathering data when sink nodes are moving. Finally, all sensor nodes transmit data according to the optimal data transmission scheme. Sink nodes gather the data along the shortest movement paths. Simulation results show that MLMS can prolong network lifetime, balance node energy consumption, and reduce data gathering latency under appropriate parameters. Under certain conditions, it outperforms Ratio_w, TPGF, RCC, and GRND.

An Optimal Transmission Strategy for Kalman Filtering Over Packet Dropping Links With Imperfect Acknowledgements

This paper presents a novel design methodology for optimal transmission policies at a smart sensor to remotely estimate the state of a stable linear stochastic dynamical system. The sensor makes measurements of the process and forms estimates of the state using a local Kalman filter. The sensor transmits quantized information over a packet dropping link to the remote receiver. The receiver sends packet receipt acknowledgments back to the sensor via an erroneous feedback communication channel which is itself packet dropping. The key novelty of this formulation is that the smart sensor decides, at each discrete time instant, whether to transmit a quantized version of either its local state estimate or its local innovation. The objective is to design optimal transmission policies in order to minimize a long term average cost function as a convex combination of the receiver's expected estimation error covariance and the energy needed to transmit the packets. The optimal transmission policy is obtained by the use of dynamic programming techniques. Using the concept of submodularity, the optimality of a threshold policy in the case of scalar systems with perfect packet receipt acknowledgments is proved. Suboptimal solutions and their structural results are also discussed. Numerical results are presented illustrating the performance of the optimal and suboptimal transmission policies.

Wireless Information and Power Transfer: Architecture Design and Rate-Energy Tradeoff

Simultaneous information and power transfer over the wireless channels potentially offers great convenience to mobile users. Yet practical receiver designs impose technical constraints on its hardware realization, as practical circuits for harvesting energy from radio signals are not yet able to decode the carried information directly. To make theoretical progress, we propose a general receiver operation, namely, dynamic power splitting (DPS), which splits the received signal with adjustable power ratio for energy harvesting and information decoding, separately. Three special cases of DPS, namely, time switching (TS), static power splitting (SPS) and on-off power splitting (OPS) are investigated. The TS and SPS schemes can be treated as special cases of OPS. Moreover, we propose two types of practical receiver architectures, namely, separated versus integrated information and energy receivers. The integrated receiver integrates the front-end components of the separated receiver, thus achieving a smaller form factor. The rate-energy tradeoff for the two architectures are characterized by a so-called rate-energy (R-E) region. The optimal transmission strategy is derived to achieve different rate-energy tradeoffs. With receiver circuit power consumption taken into account, it is shown that the OPS scheme is optimal for both receivers. For the ideal case when the receiver circuit does not consume power, the SPS scheme is optimal for both receivers. In addition, we study the performance for the two types of receivers under a realistic system setup that employs practical modulation. Our results provide useful insights to the optimal practical receiver design for simultaneous wireless information and power transfer (SWIPT).

Backhaul-Aware Interference Management in the Uplink of Wireless Small Cell Networks

The design of distributed mechanisms for interference management is one of the key challenges in emerging wireless small cell networks whose backhaul is capacity limited and heterogeneous (wired, wireless and a mix thereof). In this paper, a novel, backhaul-aware approach to interference management in wireless small cell networks is proposed. The proposed approach enables macrocell user equipments (MUEs) to optimize their uplink performance, by exploiting the presence of neighboring small cell base stations. The problem is formulated as a noncooperative game among the MUEs that seek to optimize their delay-rate tradeoff, given the conditions of both the radio access network and the -- possibly heterogeneous -- backhaul. To solve this game, a novel, distributed learning algorithm is proposed using which the MUEs autonomously choose their optimal uplink transmission strategies, given a limited amount of available information. The convergence of the proposed algorithm is shown and its properties are studied. Simulation results show that, under various types of backhauls, the proposed approach yields significant performance gains, in terms of both average throughput and delay for the MUEs, when compared to existing benchmark algorithms.

Stochastic Power Adaptation with Multiagent Reinforcement Learning for Cognitive Wireless Mesh Networks

As the scarce spectrum resource is becoming overcrowded, cognitive radio indicates great flexibility to improve the spectrum efficiency by opportunistically accessing the authorized frequency bands. One of the critical challenges for operating such radios in a network is how to efficiently allocate transmission powers and frequency resource among the secondary users (SUs) while satisfying the quality-of-service constraints of the primary users. In this paper, we focus on the noncooperative power allocation problem in cognitive wireless mesh networks formed by a number of clusters with the consideration of energy efficiency. Due to the SUs' dynamic and spontaneous properties, the problem is modeled as a stochastic learning process. We first extend the single-agent Q-learning to a multiuser context, and then propose a conjecture-based multiagent Q-learning algorithm to achieve the optimal transmission strategies with only private and incomplete information. An intelligent SU performs Q-function updates based on the conjecture over the other SUs' stochastic behaviors. This learning algorithm provably converges given certain restrictions that arise during the learning procedure. Simulation experiments are used to verify the performance of our algorithm and demonstrate its effectiveness of improving the energy efficiency.

Optimum Power Randomization for the Minimization of Outage Probability

The optimum power randomization problem is studied to minimize outage probability in flat block-fading Gaussian channels under an average transmit power constraint and in the presence of channel distribution information at the transmitter. When the probability density function of the channel power gain is continuously differentiable with a finite second moment, it is shown that the outage probability curve is a nonincreasing function of the normalized transmit power with at least one inflection point and the total number of inflection points is odd. Based on this result, it is proved that the optimum power transmission strategy involves randomization between at most two power levels. In the case of a single inflection point, the optimum strategy simplifies to on-off signaling for weak transmitters. Through analytical and numerical discussions, it is shown that the proposed framework can be adapted to a wide variety of scenarios including log-normal shadowing, diversity combining over Rayleigh fading channels, Nakagami-m fading, spectrum sharing, and jamming applications. We also show that power randomization does not necessarily improve the outage performance when the finite second moment assumption is violated by the power distribution of the fading.

Linear Precoder Design for MIMO Interference Channels with Finite-Alphabet Signaling

This paper investigates the linear precoder design for $K$-user interference channels of multiple-input multiple-output (MIMO) transceivers under finite alphabet inputs. We first obtain general explicit expressions of the achievable rate for users in the MIMO interference channel systems. We study optimal transmission strategies in both low and high signal-to-noise ratio (SNR) regions. Given finite alphabet inputs, we show that a simple power allocation design achieves optimal performance at high SNR whereas the well-known interference alignment technique for Gaussian inputs only utilizes a partial interference-free signal space for transmission and leads to a constant rate loss when applied naively to finite-alphabet inputs. Moreover, we establish necessary conditions for the linear precoder design to achieve weighted sum-rate maximization. We also present an efficient iterative algorithm for determining precoding matrices of all the users. Our numerical results demonstrate that the proposed iterative algorithm achieves considerably higher sum-rate under practical QAM inputs than other known methods.

Cognitive Radio Decision Engine Based on Binary Chaotic Particle Swarm Optimization

A novel Cognitive Radio (CR) decision engine based on Binary Chaotic Particle Swarm Optimization (BCPSO) is presented to enable robust and spectrally-efficient transmission in this paper. The BCPSO algorithm is used to adjust transmitter parameters so that the proposed CR decision engine can adjust the transmission scheme to adapt to the instantaneous channel condition. The obtained results in the Orthogonal Frequency Division Multiplexing (OFDM) system are compared with those of Genetic Algorithm (GA) and Binary Particle Swarm Optimization (BPSO) algorithm and the superiority of the proposed algorithm in convergence and accuracy over other algorithms is demonstrated. The proposed CR decision engine can select the optimal transmission scheme in real time and have the versatility for multiple applications with different Quality of Service (QoS) requirements.

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

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

Downlink Base Station Cooperative Transmission Under Limited-Capacity Backhaul

Coordinated multi-point transmission (CoMP) joint processing (CoMP-JP) is a promising technique for supporting high spectral efficiency in future cellular systems. However, this technique requires high-capacity backhaul links for connecting multiple base stations (BSs), which leads to high deployment costs. CoMP coordinated beamforming (CoMP-CB) needs less backhaul capacity, but with lower maximal multiplexing gain. In this paper, we study downlink CoMP strategies with limited-capacity backhaul. We start by investigating the optimal interference-free transmission strategy under backhaul rate constraints, where only a part of the data is shared among the BSs. We then study soft and hard switching between the CoMP-JP and CoMP-CB modes. Finally, a closed-form semi-dynamic distributed hard switching scheme is proposed, which depends on the local large-scale channel gains of the users. Simulation results show that both the soft and hard mode switching transmission perform closely to the optimal strategy and outperform the single-mode CoMP transmission especially when the backhaul capacity is very limited.

Secure Transmission in Downlink Cellular Network with a Cooperative Jammer

In this paper, a novel transmission scheme is proposed to improve the security of downlink cellular network. The confidential message intended to one of K mobile users (MUs) should be securely kept from the undesired recipients. In this work, the K-1 remaining users are regarded as potential eavesdroppers and called as internal eavesdroppers. For the security enhancement, we propose an adaptation of a single cooperative jammer (CJ) to increase the ambiguity at all malicious users by distracting them with artificial interference. With the help of CJ, we derive the optimal joint transmission scheme by beamforming solution to maximize the secrecy rate of the intended user. Numerical results show the performance improvement of the proposed scheme.

On Cooperative and Malicious Behaviors in Multirelay Fading Channels

Multirelay networks exploit spatial diversity by transmitting user's messages through multiple relay paths. Most works in the literature on cooperative or relay networks assume that all terminals are fully cooperative and neglect the effect of possibly existing malicious relay behaviors. In this work, we consider a multirelay network that consists of both cooperative and malicious relays, and aims to obtain an improved understanding on the optimal behaviors of these two groups of relays via information-theoretic mutual information games. By modeling the set of cooperative relays and the set of malicious relays as two players in a zero-sum game with the maximum achievable rate as the utility, the optimal transmission strategies of both types of relays are derived by identifying the Nash equilibrium of the proposed game. Our main contributions are twofold. First, a generalization to previous works is obtained by allowing malicious relays to either listen or attack in Phase 1 (source-relay transmission phase). This is in contrast to previous works that only allow the malicious relays to listen in Phase 1 and to attack in Phase 2 (relay-destination transmission phase). The latter is shown to be suboptimal in our problem. Second, the impact of CSI knowledge at the destination on the optimal attack strategy that can be adopted by the malicious relays is identified. In particular, for the more practical scenario where the interrelay CSI is unknown at the destination, the constant attack is shown to be optimal as opposed to the commonly considered Gaussian attack.

MIMO Broadcasting for Simultaneous Wireless Information and Power Transfer

Wireless power transfer (WPT) is a promising new solution to provide convenient and perpetual energy supplies to wireless networks. In practice, WPT is implementable by various technologies such as inductive coupling, magnetic resonate coupling, and electromagnetic (EM) radiation, for short-/mid-/long-range applications, respectively. In this paper, we consider the EM or radio signal enabled WPT in particular. Since radio signals can carry energy as well as information at the same time, a unified study on simultaneous wireless information and power transfer (SWIPT) is pursued. Specifically, this paper studies a multiple-input multiple-output (MIMO) wireless broadcast system consisting of three nodes, where one receiver harvests energy and another receiver decodes information separately from the signals sent by a common transmitter, and all the transmitter and receivers may be equipped with multiple antennas. Two scenarios are examined, in which the information receiver and energy receiver are separated and see different MIMO channels from the transmitter, or co-located and see the identical MIMO channel from the transmitter. For the case of separated receivers, we derive the optimal transmission strategy to achieve different tradeoffs for maximal information rate versus energy transfer, which are characterized by the boundary of a so-called rate-energy (R-E) region. For the case of co-located receivers, we show an outer bound for the achievable R-E region due to the potential limitation that practical energy harvesting receivers are not yet able to decode information directly. Under this constraint, we investigate two practical designs for the co-located receiver case, namely time switching and power splitting, and characterize their achievable R-E regions in comparison to the outer bound.

Robust {MISO Transmit} Optimization under Outage-Based QoS Constraints in Two-Tier Heterogeneous Networks

To improve wireless heterogeneous network service via macrocell and femtocells that share certain spectral resources, {this paper studies} the transmit beamforming design for femtocell base station (FBS), {equipped with multiple antennas}, under an outage-based quality-of-service (QoS) constraint at the {single-antenna} femtocell user equipment characterized by its signal-to-interference-plus-noise ratio. {Specifically}, we {focus on} the practical case of {imperfect} downlink {multiple-input single-output (MISO)} channel state information (CSI) at the FBS due to limited CSI feedback or CSI estimation errors. By characterizing the CSI uncertainty probabilistically, we formulate an outage-based robust beamforming design. This nonconvex optimization problem can be relaxed into a convex semidefinite programming problem, which reduces to a power control problem when all CSI vectors are independent {and} identically distributed. We also investigate the performance gap between the optimal transmission strategy (that allows maximum transmission degrees of freedom (DoF) equal to the number of transmit antennas) and the proposed optimal beamforming design (with the DoF equal to one) and provide some feasibility conditions, followed by their performance evaluation and trade-off through simulation results.

Partial Third-Party Information Exchange with Network Coding

In this paper, we consider the problem of exchanging channel state information in a wireless network such that a subset of the clients can obtain the complete channel state information of all the links in the network. We first derive the minimum number of required transmissions for such partial third-party information exchange problem. We then design an optimal transmission scheme by determining the number of packets that each client should send, and designing a deterministic encoding strategy such that the subset of clients can acquire complete channel state information of the network with minimal number of transmissions. Numerical results show that network coding can efficiently reduce the number of transmissions, even with only pairwise encoding.

Joint Optimization of Throughput and Packet Drop Rate for Delay Sensitive Applications in TDD Satellite Network Coded Systems

In this paper, we consider the issue of throughput and packet drop rate (PDR) optimization as two performance metrics for delay sensitive applications in network coded time division duplex (TDD) satellite systems with large round trip times (RTTs). We adopt random linear network coding (RLNC) and our purpose is to obtain the optimum RLNC-based transmission strategy. We start with a single-user case and propose a systematic framework to investigate the advantage of using feedback by comparing feedback-free and feedback schemes. Showing analytically that the feedback-free scheme gives better performance for our system of interest, we extend it to multi-user broadcast case. To this end, we consider a number of different broadcast scenarios and optimize the system parameters such that the best overall performance is achieved. Furthermore, the complicated interplay of the mean throughputs and PDRs of different users with different packet erasure conditions is discussed. Finally, it is shown that the optimized feedback-free RLNC broadcast scheme works close enough to an idealistic RLNC scheme, where the complete and immediate knowledge about the reception status of all users is assumed to be available at the sender.

Improvement and Performance Evaluation for Multimedia Files Transmission in Vehicle-Based DTNs

In recent years, P2P file sharing has been widely embraced and becomes the largest application of the Internet traffic. And the development of automobile industry has promoted a trend of deploying Peer-to-Peer (P2P) networks over vehicle ad hoc networks (VANETs) for mobile content distribution. Due to the high mobility of nodes, nodes' limited radio transmission range and sparse distribution, VANETs are divided and links are interrupted intermittently. At this moment, VANETs may become Vehicle-based Delay Tolerant Network (VDTNs). Therefore, this work proposes an Optimal Fragmentation-based Multimedia Transmission scheme (OFMT) based on P2P lookup protocol in VDTNs, which can enable multimedia files to be sent to the receiver fast and reliably in wireless mobile P2P networks over VDTNs. In addition, a method of calculating the most suitable size of the fragment is provided, which is tested and verified in the simulation. And we also show that OFMT can defend a certain degree of DoS attack and senders can freely join and leave the wireless mobile P2P network. Simulation results demonstrate that the proposed scheme can significantly improve the performance of the file delivery rate and shorten the file delivery delay compared with the existing schemes.

Analysis of Estimation Parameter for Energy Efficient in Wireless Sensor Network

In wireless sensor network energy fully depend on battery lifetime. Energy saving by making intelligent importance driven decision about message transmission in self organized method. The selective forwarder is a method which makes intelligent importance driven decision for saving energy. The development of a selective forwarder method based on decision theory which was not stable for network as well as before failure the route, how can recognized the failure route no one knows the idea, for that proposed an approach to develop optimal energy-aware transmission schemes by adaptive selective forwarder for quality of information as well as for stable network. However, in the work include stochastic process and routing protocol depends on parameters like available energy on a node, retransmission a message and message importance. By using these three parameters find out the best performance in network with analysis and comparison of adaptive selective forwarder and local-global forwarder method. The constant threshold values apply for saving energy of each node from source node to sink node for optimal solution in the network. The main focus of work is an analysis the estimated parameter which are used for getting efficient energy according to changing the level of energy of sensor node for increasing the lifetime of battery as well as increasing the lifetime of the network. Also calculate battery lifetime by using Markov decision process rule with proper network design.