Information Exchange Overhead Research Articles

Distributed Joint Power and Bandwidth Allocation for Multiagent Cooperative Localization

Traditional cooperative localization schemes often rely on centralized information sharing to implement the joint power and bandwidth allocation for localization error minimization. In order to minimize the sharing overhead and reduce the implementation complexity, we propose a low complexity distributed power and bandwidth allocation scheme for high accurate collaborative localization. By decomposing the expected global squared position error bound (SPEB) into individual ones, our proposed scheme is able to determine the power and bandwidth distributively with limited information exchange overhead. Through analytical and numerical examples, we show that the proposed collaborative scheme can achieve about 11% mean square localization error improvement and offer over 71% complexity reduction for six agents, if compared with the traditional iterative searching schemes.

Semantic Communication Meets Edge Intelligence

The development of emerging applications, such as autonomous transportation systems, is expected to result in an explosive growth in mobile data traffic. As the available spectrum resource becomes more and more scarce, there is a growing need for a paradigm shift from Shannon's Classical Information Theory (CIT) to semantic communication (SemCom). Specifically, the former adopts a “transmit-before-understanding” approach while the latter leverages artificial intelligence (AI) techniques to “understand-before-transmit,” thereby alleviating bandwidth pressure by reducing the amount of data to be exchanged without negating the semantic effectiveness of the transmitted symbols. However, the semantic extraction (SE) procedure incurs costly computation and storage overheads. In this article, we introduce an edge-driven training, maintenance, and execution of SE. We further investigate how edge intelligence can be enhanced with SemCom through improving the generalization capabilities of intelligent agents at lower computation overheads and reducing the communication overhead of information exchange. Finally, we present a case study involving semantic-aware resource optimization for the wireless powered Internet of Things (IoT).

Energy-Efficient Secure Short-Packet Transmission in NOMA-Assisted mMTC Networks With Relaying

Massive Machine-Type Communication (mMTC) is proposed by ITU to provide a large scale of connectivity services for billions of machine-type communications devices (MTCDs) via cellular networks. However, MTCDs are generally energy-constrained, security capability limited and short-packet transmission dominated. It has great challenges for massive MTCDs to perform short-packet transmission simultaneously while guaranteeing the confidentiality and energy efficiency of the information transmission. In this paper, we investigate the energy-efficient secure short-packet transmission of Non-Orthogonal Multiple Access (NOMA) assisted mMTC networks, in which MTCDs aim to transmit secrecy messages to the destination base station via trusted relays with a passive eavesdropper present. To realize secure, reliable and energy-efficient transmission, we maximize the secrecy energy efficiency (SEE) by implementing joint relay and power level selection. Furthermore, we formulate a decentralized stateless Q-learning-based multi-agent reinforcement learning (MARL) algorithm for the dynamic and complex mMTC systems. The simulation results show that adopting the proposed decentralized MARL algorithm and relaying with NOMA can improve the performance of SEE while reducing information exchange overhead.

Contextual User-Centric Task Offloading for Mobile Edge Computing in Ultra-Dense Network

Integrating mobile edge computing (MEC) in the ultra-dense network (UDN) is a key enabler to meet the service demand by allowing smart devices to perform uninterrupted task offloading via densely deployed MEC servers. In most cases, the smart devices randomly move around the whole network. Consequently, the popular ‘`MEC-centralized decision’' offloading approach could be inapplicable, as joint decision-making among multiple MEC servers becomes difficult due to time synchronization and information exchange overhead. In this paper, we take a user-centric approach to minimize a long-term delay for a given task duration under a price budget constraint. To address this problem, we develop a novel contextual sleeping bandit learning (CSBL) algorithm, which integrates contextual information and sleeping characteristic to accelerate the learning convergence and leverage Lyapunov optimization to deal with the price budget constraint. Furthermore, we extend to a multiple offloading scenario where multiple MEC servers can be selected in each offloading round and propose a CSBL-multiple (CSBL-M) algorithm to address the exponential increase of the offloading selections. For both CSBL and CSBL-M, we derive the upper bounds of learning regret and provide rigorous proofs that they asymptotically approach the Oracle algorithm within bounded deviations for finite task duration.

Robust secrecy energy efficiency optimisation in heterogeneous networks with simultaneous wireless information and power transfer: centralised and distributed design

With the ellipsoid-bounded channel state information (CSI) errors, this study investigates the robust secrecy energy efficiency (SEE) optimisation in a wireless powered heterogeneous network. Specifically, the authors consider the network where multiple femtocell base stations (FBSs) are deployed under the coverage of one macrocell base station (MBS). Meanwhile, the MBS serves multiple macrocell users in the presence of a malicious multiple-antenna eavesdropper (Eve) while each FBS serves a pair of information receiver (IR) and energy receiver (ER) with multiple antennas, where the ER attempts to wiretap the information of IR in the same femtocell. To promote the secrecy performance, artificial noise (AN) is aided into the downlink signal at the MBS and FBSs, and the problem of maximising SEE is formed in a cross-tier multi-cell AN-aided transmit beamforming design. This problem is non-convex while containing infinite constraints caused by CSI errors, which cannot be solved directly. In this regard, the authors resort to the successive convex approximation, semi-definite relaxation techniques and Lagrange duality theory to acquire its solvable form. Moreover, to reduce the overhead of information exchange among coordinated BSs, they further propose a distributed approach based on alternative direction multiplier method. Finally, simulation results validate the effectiveness of the proposed design.

Robust energy-efficient power allocation and relay selection for cooperative relay networks

One of the challenging issues in wireless relay networks is to choose the appropriate relays while keeping acceptable requirement of quality of service. In this paper, a joint optimal scheme of power allocation and relay selection is proposed for the decode-and-forward (DF) relay communication networks. The proposed scheme attempts to maximize the total energy-efficiency of the system. The cooperative relay and orthogonal-frequency-division-multiple-access (OFDMA) technologies are introduced to improve the spectral efficiency. In order to reduce the effect caused by channel uncertainty, a robust power control approach based on worst-case is applied. The formulated problem is a mixed-integer nonlinear problem, which is difficult to solve in the original form. Therefore, a novel relay selection scheme is formulated to transform it into a quasi-concave programming problem. The Dinkelbach method is used to solve the formulated optimization problem. Besides, a distributed iteration algorithm is developed to reduce the overhead of information exchange. Numerical results reveal the effectiveness of the proposed scheme and show better performance in aspect of outage reduction and energy efficiency improvement.

Distributed Multienergy Coordination of Multimicrogrids With Biogas-Solar-Wind Renewables

This paper proposes a distributed multienergy management framework for the coordinated operation of interconnected biogas-solar-wind microgrids. In this framework, each microgrid not only schedules its local hybrid biogas-solar-wind renewables for coupled multicarrier energy supplies based on the concept of energy hub but also exchanges energy with interconnected microgrids and via the transactive market. The multimicrogrid scheduling is a challenging optimization problem due to its severe constraints and strong couplings. A multimicrogrid multienergy coupling matrix is thus formulated to model and exploit the inherent biogas-solar-wind energy couplings among electricity, gas, and heat flows. Furthermore, a distributed stochastic optimal scheduling scheme with minimum information exchange overhead is proposed to dynamically optimize energy conversion and storage devices in the multimicrogrid system. The proposed method has been fully tested and benchmarked on the different scaled multimicrogrid system over a 24-h scheduling horizon. Comparative results demonstrated that the proposed approach can reduce the system operating cost and enhance the system energy-efficiency, and also confirm its scalability in solving large-scale multimicrogrid problems.

Robust Secrecy Energy Efficiency Optimization for Wireless Powered Heterogeneous Networks Using Distributed ADMM Algorithm

Under the case of imperfect channel state information (CSI), this paper investigates the robust secrecy energy efficiency (SEE) optimization for the heterogeneous networks (HeNets) supported by simultaneous wireless information and power transfer. Specifically, we first consider a two-tier HeNet composed of a macrocell base station (MBS) and several femtocell base stations (FBSs), where the MBS serves multiple macrocell users (MUs) while each FBS serves an information receiver (IR) and an multiple-antenna energy receiver (ER). Meanwhile, a malicious multiple-antenna eavesdropper (Eve) attempts to wiretap the downlink information of MUs and the ER acts as a potential Eve to eavesdrop the confidential information for IR in the same femtocell. To enhance the secrecy performance, artificial noise (AN) is injected into the downlink information beams of the MBS and FBSs. By considering the user fairness, the problem of SEE maximization of the whole network is formulated via a cross-tier multi-cell coordinated beamforming design. The resulting problem contains infinite constraints caused by CSI error, which is nonconvex and cannot be solved directly. To this regard, we resort to the successive convex approximation, S-procedure and semi-definite relaxation techniques to obtain a solvable form of it. Furthermore, to reduce the overhead of information exchange among coordinated BSs, we develop a distributed solution based on alternative direction multiplier method (ADMM) that can achieve a good approximation performance. Finally, simulation results verify the validity of the proposed AN-aided cross-tier multi-cell coordinated beamforming design and distributed ADMM-based design.

Distributed beamforming algorithm based on game theory in device-to-device communications

In the device-to-device communication network, there is the interference problem when device-to-device users share the same spectrum with cellular users, a distributed beamforming scheme based on non-cooperative game is proposed to maximize weighted sum rate under the rated transmit power and the users’ quality of service. Since the optimization problem is non-concave, we first obtain the solution of the Karush-Kuhn-Tucher (KKT) condition for the downlink beamforming problem of each sender by dual decomposition. Second, a distributed beamforming algorithm based on the non-cooperative game is proposed, which can quickly converge to the Nash equilibrium point with a lower information exchange overhead. Finally, the simulation results show that the proposed algorithm has better performance in terms of system sum rate and fairness than the existing algorithms.

Incentivizing Energy Trading for Interconnected Microgrids

In this paper, we study the interactions among interconnected autonomous microgrids, and propose a joint energy trading and scheduling strategy. Each interconnected microgrid not only schedules its local power supply and demand, but also trades energy with other microgrids in a distribution network. Specifically, microgrids with excessive renewable generations can trade with other microgrids in deficit of power supplies for mutual benefits. Since interconnected microgrids operate autonomously, they aim to optimize their own performance and expect to gain benefits through energy trading. We design an incentive mechanism using Nash bargaining theory to encourage proactive energy trading and fair benefit sharing. We solve the bargaining problem by decomposing it into two sequential problems on social cost minimization and trading benefit sharing, respectively. For practical implementation, we propose a decentralized solution method with minimum information exchange overhead. Numerical studies based on realistic data demonstrate that the total cost of the interconnected-microgrids operation can be reduced by up to 13.2% through energy trading, and an individual participating microgrid can achieve up to 29.4% reduction in its cost through energy trading.

Secrecy Energy Efficiency in Wireless Powered Heterogeneous Networks: A Distributed ADMM Approach

This paper investigates the physical layer security in heterogeneous networks (HetNets) supported by simultaneous wireless information and power transfer (SWIPT). We first consider a two-tier HetNet composed of a macrocell and several femtocells, where the macrocell base station (BS) serves multiple users in the presence of a malicious eavesdropper, while each femtocell BS serves a couple of Internet-of-Things (IoT) users. With regard to the energy constraint of IoT users, SWIPT is performed at the femtocell BSs, and IoT users accomplish the reception of information and energy in a time-switching manner, where information secrecy is to be protected. To enhance the secrecy performance, we inject artificial noise (AN) into the transmit beam at both macrocell and femtocell BSs, and for the sake of achieving green communications, we formulate the problem of maximizing secrecy energy efficiency while considering the fairness in a cross-tier multi-cell coordinated beamforming (MCBF) design. To handle this resulting nonconvex max–min fractional program problem, we propose an iterative algorithm by applying successive convex approximation method. Then, we further develop a decentralized solution based on alternative direction multiplier method (ADMM), which reduces the overhead of information exchange among coordinated BSs and achieves good approximation performance. Finally, simulation results demonstrate the performance of the proposed AN-aided cross-tier MCBF design and verify the validity of distributed ADMM-based approach.

Traffic-Aware Cell Management for Green Ultradense Small-Cell Networks

To reduce the power consumption of fifth-generation ultradense small-cell networks, base stations can be switched to low-power sleep modes when local traffic levels are low. In this paper, a novel sleep mode control algorithm is proposed to control such sleep modes. The algorithm innovates a concept called traffic-aware cell management (TACM). It involves cell division, cell death, and cell migration to represent adaptations of networks, where the state transitions of base stations are controlled. Direction of arrival (DOA) is adopted for distributed decision making. The TACM algorithm aims at reducing the network power consumption while alleviating the impacts of applying sleep modes, such as mitigating system overheads and reducing user transmission power. The TACM algorithm is compared with a recent consolidated baseline scheme by simulation on networks with unbalanced traffic distributions and with base stations at random locations. In contrast, the TACM algorithm shows a significant improvement in mitigating system overheads due to the absence of load information exchange overhead and up to 72 times less switching frequency. Up to 81% network power consumption can be reduced compared with the baseline scheme if considering high energy consumption of switching transient states. In addition, at a low traffic level, average uplink transmission power is reduced by 79% comparatively. Furthermore, the impact of important performance-governing parameters of the TACM algorithm is analyzed. The insensitivity to the estimation accuracy of DOA is also demonstrated. The results show that the proposed TACM algorithm has a comprehensive advantage of power reduction and overhead mitigation over the baseline scheme.

Cooperative Precoding for Cognitive Transmission in Two-Tier Networks

In this paper, we study cooperative precoder design in two-tier networks, consisting of a macro-cell (MC) and several small-cells (SCs). By exploiting multiuser Vandermonde-subspace frequency division multiplexing (VFDM) transmission, an MC downlink can co-exist with cognitive SCs. In this paper, we first propose a cooperative cross-tier precoder (CTP) among the transmitters in the SCs to increase the transmitted dimension. The cooperative CTP allows us to use more efficient intra-tier precoder (ITP) in SCs to handle intracell interference and improve the throughput of the cognitive system. And then, three ITPs, a block-diagonal zero-forcing (BD-ZF) ITP, a capacity-achieving (CA) ITP, and a generalized MMSE channel inversion (GMI) ITP, are developed. Complexities of all CTPs and ITPs are discussed and compared. The overhead of channel state information (CSI) exchange is analyzed. Numerical results are presented to demonstrate the throughput improvement of the proposed schemes and to discover the impact of the imperfect CSI. From the complexity comparison and the numerical results, the GMI ITP offers a good tradeoff between complexity and throughput.

On Throughput Maximization of Time Division Multiple Access With Energy Harvesting Users

In this paper, we consider a multiple-access channel, where multiple users equipped with energy harvesting batteries communicate to an access point. To avoid consuming extra energy on competition for the channel, the users are supposed to share the channel via time division multiple access (TDMA). In many existing works, it is commonly assumed that the users' energy harvesting processes and storage status are known to all the users before transmissions. In practice, such knowledge may not be readily available. To avoid excessive overhead for real-time information exchange, we consider the scenario where the users schedule their individual transmissions according to the users' statistical energy harvesting profiles. We first study the optimal transmission scheme in the case where each node has an infinite-capacity battery. By optimization theory, we show that, to maximize the average system throughput, all the users should transmit at an identical optimal power, which solely depends on the energy harvesting rate per time slot. We then study the equal-power TDMA scheme in the case where each node is equipped with a battery of finite capacity. The system is formulated as a polling system consisting of multiple energy queues and one server. By the Markov chain modeling method, we derive the performance of equal-power TDMA in this case, in terms of the energy loss ratio and average system throughput. In addition, we develop an algorithm to efficiently compute the optimal transmission power in the finite-capacity battery case. We also consider an equal-time TDMA scheme, which assigns equal-length subslots to each user, and analyze its system performance. It is found that equal-power TDMA always outperforms equal-time TDMA in the infinite-capacity battery case, whereas equal-time TDMA exhibits compatible or even slightly better performance in some scenarios when the batteries have finite capacities.

Weighted Sum Energy Efficiency Maximization in Ad Hoc Networks

In this letter, we propose a distributed adaptive-pricing algorithm aimed at solving the weighted sum energy efficiency (EE) maximization problem in ad hoc networks. It is theoretically proven that the proposed distributed algorithm strictly converges to the Karush-Kuhn-Tucker (KKT) point of the problem. Significant performance enhancement is observed by numerical results with fast convergence. Moreover, it is shown that the proposed algorithm degrades gracefully when decreasing overhead of information exchange.

Multiuser Precoding With Limited Cooperation for Large-Scale MIMO Multicell Downlink

Multiple base stations (BSs) equipped with large antenna arrays can perform cooperative downlink transmissions using beamforming for cell-edge users to provide high data rate services. However, due to an excessive overhead for the channel state information (CSI) exchange between BSs, the cooperation with full CSI may not be practical for large antenna arrays. Thus, based on an existing precoding approach, two different precoding approaches with partial cooperation, where no CSI exchange is required, are proposed. One of them (called Type III precoding) has a better performance, and its performance can be further improved by user allocation, which becomes possible by deriving a closed-form expression for transmission power. For low-complexity implementations, an algorithm is derived for the user allocation based on the greedy algorithm. Under certain channel conditions, closed-form expressions for average transmission powers are also derived to see asymptotic performance. Based on them, we can show that the average transmission power becomes $Q$ times higher than that with full cooperation if precoding is carried out with local CSI only, where $Q$ is the number of BSs in cooperation, which would be considered as a penalty due to partial cooperation. Through simulations, it is shown that this penalty (i.e., the increase of transmission power) can be reduced by user allocation.

Transfer learning and cooperation management: balancing the quality of service and information exchange overhead in cognitive radio networks

AbstractThis paper introduces and studies a novel solution of transfer learning applied to spectrum management in cognitive radio networks in order to improve the Quality of Service (QoS) and convergence performance of conventional full distributed learning. Cooperation management has been investigated to enhance transfer learning as a novel way of reducing the need for control information exchange between distributed cognitive agents while providing the same effective QoS as that achieved in a fully coordinated network. A structured approach is taken to transfer learning, including a source agent selection function defining how the agents exchange learning information, and a target agent training function reinforcing the knowledgebase. It is demonstrated in simulation and analysis that transfer learning achieves a significantly higher QoS and throughput than distributed reinforcement learning. The cooperation management algorithm is shown to effectively reduce the need for information exchange by 90 per cent whilst still providing adequate QoS compared with a fully coordinated network. Copyright © 2014 John Wiley & Sons, Ltd.

Null‐space cognitive precoding for heterogeneous networks

Small-cells are considered as an effective solution to increase capacity and offload traffic from, the current macro-cell cellular system. Owing to the difficulty and costs involved in acquiring new spectrum licenses, small-cells are expected to coexist with their respective macro-cells, in the same spectrum. This leads to considerable interference between the two systems. Optimum performance, at the macro-cell, is achieved when the small-cell terminals transmit their information over the null-space of the macro-cell link. However, availability, at the small-cell terminals, of the macro-cell channel null-space information requires full-cooperation and thus a high overhead of information exchange. In this study, the cognitive precoding schemes are designed under a limited inter-system information exchange and the constraint that the performance of the macro-cell link is kept close to the case where no small-cell network does exist. Two techniques are considered: a two-bit quantisation precoded and a dual space-frequency coding precoded approach. It is demonstrated that the first achieves a performance close to the full cooperation approach recently proposed, but with very low information exchange requirements. For the second, it is show that both the systems are able to coexist without any inter-system cooperation and with a performance close to the non-coexistence scenario.

King's Graph-Based Neighbor-Vehicle Mapping Framework

Vehicle localization algorithms aim to provide an accurate location estimation of neighbor vehicles for critical applications in intelligent vehicles. For the initial location estimation, localization algorithms use either Global Positioning System (GPS), radio-based lateration techniques, or both. These techniques suffer from three major issues, namely, flip ambiguities, location information exchange (beacon) overhead, and forged relative location information. The accuracy of these algorithms at the early iterations is primarily affected by flip ambiguities, which in turn result in erroneous initial location estimates. The errors from flip ambiguities are a monotonically increasing function of time and propagated to the subsequent iterations to build an erroneous neighbor-vehicle map. In this paper, we propose a novel GPS-free neighbor-vehicle mapping framework that provides reliable initial relative position estimates of neighbor vehicles and mitigates aforementioned issues. This framework uses presence/absence status information of neighbor vehicles in binary form from a vision-based environment sensor system to associate each vehicle's cardinal location with its identification information, such as media access control (MAC)/Internet Protocol addresses. We represent a vehicle's neighborhood region and neighborhood topology using the Moore neighborhood (MN) and King's graph (KG), respectively, by analyzing a typical vehicle formation in a multilane roadway. We also introduce a KG-based neighborhood information overlap measure (IOM) algorithm for neighbor mapping by exploiting the perspective symmetric properties of the MN. Performance analysis and simulation results show that the proposed algorithm builds an accurate relative neighbor-vehicle map and outperforms trilateration- and multilateration-based methods in mitigating flip ambiguities and location information exchange overhead.

Regularized Zero-Forcing Interference Alignment for the Two-Cell MIMO Interfering Broadcast Channel

We present linear transmit-receive filter design strategies for the two-cell multiple-input multiple-output (MIMO) interfering broadcast channel (IBC) where inter-cell interference (ICI) exists in addition to inter-user interference (IUI) within the cell. We first formulate a generalized zero-forcing interference alignment (ZF-IA) method capable of handling multiple data streams for each user. We proceed to further adjust the transmit-receive filters based on the minimum weighted-mean-square-error (WMSE) principle using the weights obtained from the initial generalized ZF-IA design, in an effort to to enhance the sum-rate performance. In contrast to the existing weighted-sum-rate-maximizing scheme, our method does not require an iterative calculation of the weights. Because of this, the proposed scheme, while not designed specifically to maximize the sum-rate, is computationally efficient and delivers favorable tradeoffs between performance and information exchange overhead.