Showing papers on "Channel allocation schemes published in 2014"

Evolution Towards 5G Multi-tier Cellular Wireless Networks: An Interference Management Perspective

Abstract: The evolving fifth generation (5G) cellular wireless networks are envisioned to overcome the fundamental challenges of existing cellular networks, for example, higher data rates, excellent end-to-end performance, and user-coverage in hot-spots and crowded areas with lower latency, energy consumption, and cost per information transfer. To address these challenges, 5G systems will adopt a multi-tier architecture consisting of macrocells, different types of licensed small cells, relays, and device-to-device (D2D) networks to serve users with different quality-of-service (QoS) requirements in a spectrum and energy-efficient manner. Starting with the visions and requirements of 5G multi-tier networks, this article outlines the challenges of interference management (e.g. power control, cell association) in these networks with shared spectrum access (i.e. when the different network tiers share the same licensed spectrum). It is argued that the existing interference management schemes will not be able to address the interference management problem in prioritized 5G multi-tier networks where users in different tiers have different priorities for channel access. In this context a survey and qualitative comparison of the existing cell association and power control schemes is provided to demonstrate their limitations for interference management in 5G networks. Open challenges are highlighted and guidelines are provided to modify the existing schemes in order to overcome these limitations and make them suitable for the emerging 5G systems.

Modulation Formats and Waveforms for 5G Networks: Who Will Be the Heir of OFDM?: An overview of alternative modulation schemes for improved spectral efficiency

Abstract: Fifth-generation (5G) cellular communications promise to deliver the gigabit experience to mobile users, with a capacity increase of up to three orders of magnitude with respect to current long-term evolution (LTE) systems There is widespread agreement that such an ambitious goal will be realized through a combination of innovative techniques involving different network layers At the physical layer, the orthogonal frequency division multiplexing (OFDM) modulation format, along with its multiple-access strategy orthogonal frequency division multiple access (OFDMA), is not taken for granted, and several alternatives promising larger values of spectral efficiency are being considered This article provides a review of some modulation formats suited for 5G, enriched by a comparative analysis of their performance in a cellular environment, and by a discussion on their interactions with specific 5G ingredients The interaction with a massive multiple-input, multiple-output (MIMO) system is also discussed by employing real channel measurements

Joint Subchannel Assignment and Power Allocation for OFDMA Femtocell Networks

Abstract: In this paper, we propose a joint subchannel and power allocation algorithm for the downlink of an orthogonal frequency-division multiple access (OFDMA) mixed femtocell/macrocell network deployment. Specifically, the total throughput of all femtocell user equipments (FUEs) is maximized while the network capacity of an existing macrocell is always protected. Towards this end, we employ an iterative approach in which OFDM subchannels and transmit powers of base stations (BS) are alternatively assigned and optimized at every step. For a fixed power allocation, we prove that the optimal policy in each cell is to give each subchannel to the user with the highest signal-to-interference-plus-noise ratio (SINR) on that subchannel. For a given subchannel assignment, we adopt the successive convex approximation (SCA) approach and transform the highly nonconvex power allocation problem into a sequence of convex subproblems. In the arithmetic-geometric mean (AGM) approximation, we apply geometric programming to find optimal solutions after condensing a posynomial into a monomial. On the other hand, logarithmic and \underline{d}ifference-of-two-\underline{c}oncave-functions (D.C.) approximations lead us to solving a series of convex relaxation programs. With the three proposed SCA-based power optimization solutions, we show that the overall joint subchannel and power allocation algorithm converges to some local maximum of the original design problem. While a central processing unit is required to implement the AGM approximation-based solution, each BS locally computes the optimal subchannel and power allocation for its own servicing cell in the logarithmic and D.C. approximation-based solutions. Numerical examples confirm the merits of the proposed algorithm.

Power Minimization Based Resource Allocation for Interference Mitigation in OFDMA Femtocell Networks

Abstract: With the introduction of femtocells, cellular networks are moving from the conventional centralized network architecture to a distributed one, where each network cell should make its own radio resource allocation decisions, while providing inter-cell interference mitigation. However, realizing such distributed network architecture is not a trivial task. In this paper, we first introduce a simple self-organization rule, based on minimizing cell transmit power, following which a distributed cellular network is able to converge into an efficient resource reuse pattern. Based on such self-organization rule and taking realistic resource allocation constraints into account, we also propose two novel resource allocation algorithms, being autonomous and coordinated, respectively. Performance of the proposed self-organization rule and resource allocation algorithms are evaluated using system-level simulations, and show that power efficiency is not necessarily in conflict with capacity improvements at the network level. The proposed resource allocation algorithms provide significant performance improvements in terms of user outages and network capacity over cutting-edge resource allocation algorithms proposed in the literature.

Clustering and Resource Allocation for Dense Femtocells in a Two-Tier Cellular OFDMA Network

Abstract: Small cells such as femtocells overlaying the macrocells can enhance the coverage and capacity of cellular wireless networks and increase the spectrum efficiency by reusing the frequency spectrum assigned to the macrocells in a universal frequency reuse fashion. However, management of both the cross-tier and co-tier interferences is one of the most critical issues for such a two-tier cellular network. Centralized solutions for interference management in a two-tier cellular network with orthogonal frequency-division multiple access (OFDMA), which yield optimal/near-optimal performance, are impractical due to the computational complexity. Distributed solutions, on the other hand, lack the superiority of centralized schemes. In this paper, we propose a semi-distributed (hierarchical) interference management scheme based on joint clustering and resource allocation for femtocells. The problem is formulated as a mixed integer non-linear program (MINLP). The solution is obtained by dividing the problem into two sub-problems, where the related tasks are shared between the femto gateway (FGW) and femtocells. The FGW is responsible for clustering, where correlation clustering is used as a method for femtocell grouping. In this context, a low-complexity approach for solving the clustering problem is used based on semi-definite programming (SDP). In addition, an algorithm is proposed to reduce the search range for the best cluster configuration. For a given cluster configuration, within each cluster, one femto access point (FAP) is elected as a cluster head (CH) that is responsible for resource allocation among the femtocells in that cluster. The CH performs sub-channel and power allocation in two steps iteratively, where a low-complexity heuristic is proposed for the sub-channel allocation phase. Numerical results show the performance gains due to clustering in comparison to other related schemes. Also, the proposed correlation clustering scheme offers performance, which is close to that of the optimal clustering, with a lower complexity.

Optimal Power Allocation for Outage Probability Minimization in Fading Channels with Energy Harvesting Constraints

Abstract: This paper studies the optimal power allocation for outage probability minimization in point-to-point fading channels with the energy-harvesting constraints and channel distribution information (CDI) at the transmitter. Both the cases with non-causal and causal energy state information (ESI) are considered, which correspond to the energy-harvesting (EH) rates being known and unknown prior to the transmissions, respectively. For the non-causal ESI case, the average outage probability minimization problem over a finite horizon of N EH periods is shown to be non-convex for a large class of practical fading channels. However, the globally optimal "offline" power allocation is obtained by a forward search algorithm with at most N one-dimensional searches, and the optimal power profile is shown to be non-decreasing over time and have an interesting "save-then-transmit" structure. In particular, for the special case of N=1, our result revisits the classic outage capacity for fading channels with uniform power allocation. Moreover, for the case with causal ESI, we propose both the optimal and suboptimal "online" power allocation algorithms, by applying the technique of dynamic programming and exploring the structure of optimal offline solutions, respectively.

Resource Efficiency: A New Paradigm on Energy Efficiency and Spectral Efficiency Tradeoff

Abstract: Spectral efficiency (SE) and energy efficiency (EE) are the main metrics for designing wireless networks. Rather than focusing on either SE or EE separately, recent works have focused on the relationship between EE and SE and provided good insight into the joint EE-SE tradeoff. However, such works have assumed that the bandwidth was fully occupied regardless of the transmission requirements and therefore are only valid for this type of scenario. In this paper, we propose a new paradigm for EE-SE tradeoff, namely the resource efficiency (RE) for orthogonal frequency division multiple access (OFDMA) cellular network in which we take into consideration different transmission-bandwidth requirements. We analyse the properties of the proposed RE and prove that it is capable of exploiting the tradeoff between EE and SE by balancing consumption power and occupied bandwidth; hence simultaneously optimizing both EE and SE. We then formulate the generalized RE optimization problem with guaranteed quality of service (QoS) and provide a gradient based optimal power adaptation scheme to solve it. We also provide an upper bound near optimal method to jointly solve the optimization problem. Furthermore, a low-complexity suboptimal algorithm based on a uniform power allocation scheme is proposed to reduce the complexity. Numerical results confirm the analytical findings and demonstrate the effectiveness of the proposed resource allocation schemes for efficient resource usage.

Compressive Sensing Based Channel Estimation for OFDM Systems Under Long Delay Channels

Abstract: Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) has advantages in spectral efficiency and synchronization. However, its iterative interference cancellation algorithm will suffer from performance loss especially under severely fading channels with long delays and has difficulty supporting high-order modulations like 256 QAM, which may not accommodate the emerging ultra-high definition television service. To solve this problem, a channel estimation method for OFDM under the framework of compressive sensing (CS) is proposed in this paper. Firstly, by exploiting the signal structure of recently proposed time-frequency training OFDM scheme, the auxiliary channel information is obtained. Secondly, we propose the auxiliary information based subspace pursuit (A-SP) algorithm to utilize a very small amount of frequency-domain pilots embedded in the OFDM block for the exact channel estimation. Moreover, the obtained auxiliary channel information is adopted to reduce the complexity of the classical SP algorithm. Simulation results demonstrate that the CS-based OFDM outperforms the conventional dual pseudo noise padded OFDM and CS-based TDS-OFDM schemes in both static and mobile environments, especially when the channel length is close to or even larger than the guard interval length, where the conventional schemes fail to work completely.

Performance Analysis of Group-Synchronized DCF for Dense IEEE 802.11 Networks

Abstract: In dense IEEE 802.11 networks, improving the efficiency of contention-based media access control is an important and challenging issue. Recently, the IEEE802.11ah Task Group has discussed a group-synchronized distributed coordination function (GS-DCF) for densely deployed wireless networks with a large number of stations. By using the restricted access window (RAW) and RAW slots, the GS-DCF is anticipated to improve the throughput substantially, primarily due to relieving the channel contention. However, optimizing the MAC configurations for the RAW, i.e., the number and duration of RAW slots, is still an open issue. In this paper, we first build an analytical model to track the performance of the GS-DCF in saturated 802.11 networks. Then, we study and compare the GS-DCF throughput using both centralized and decentralized grouping schemes. The accuracy of our model has been validated with simulation results. It is observed that the GS-DCF obtains a throughput gain of seven times or more over DCF in a network of 512 or more stations. Moreover, it is demonstrated that the decentralized grouping scheme can be implemented with a small throughput loss when compared with the centralized grouping scheme.

Joint Optimization of Power and Channel Allocation with Non-Orthogonal Multiple Access for 5G Cellular Systems

Abstract: Non-orthogonal multiple access (NOMA) with successive interference cancellation (SIC), is considered as a candidate multi-user access scheme for 5G cellular systems. In this paper, we provide theoretical insights and solution algorithm for optimizing multi- user power and channel allocation in NOMA systems. We mathematically formulate the NOMA resource allocation problem and prove its NP-hardness. For solving the problem, we propose an algorithm combining Lagrangian duality and dynamic programming to deliver a competitive suboptimal solution. Numerical results demonstrate that the proposed algorithmic solution can significantly improve the system performance over orthogonal frequency division multiple access (OFDMA) as well as over other existing NOMA resource allocation scheme.

Adapting Transmitter Power and Modulation Format to Improve Optical Network Performance Utilizing the Gaussian Noise Model of Nonlinear Impairments

Abstract: This paper serves to highlight the gains in SNR margin and/or data capacity that can be achieved through a proper optimization of the transceiver parameters, for example, launch power, modulation format, and channel allocation. A simple quality of transmission estimator is described that allows a rapid estimation of the signal quality based on ASE noise and nonlinear interference utilizing the Gaussian noise model. The quality of transmission estimator was used to optimize the SNR and maximise the data throughput of transmission signals in a point-to-point link by adjusting the launch power and modulation format. In a three-node network, the launch power and channel allocation were adjusted to minimise the overall effect of nonlinear interference. This paper goes on to show that by optimizing the transceiver modulation format as part of the channel allocation and routing problem gains in network data throughput can be achieved for the 14-node NSF mesh network.

Energy-Efficient Resource Allocation for OFDMA-Based Multi-RAT Networks

Abstract: To support the heterogeneous demands for the network and mobile user equipment (UE), multiple radio access technologies (RATs), operating with different system configurations and resources, have evolved and now coexist. Recently, with data traffic exponentially increasing, there has been a significant expansion in wireless network infrastructure, raising a justifiable concern over the concomitant drastic increase in energy consumption. Thus, energy-efficient design in multi-RAT networks is becoming increasingly important. In this paper, we consider resource allocation that maximizes the energy efficiency (EE) for orthogonal frequency division multiple access (OFDMA) in multi-RAT networks. To this end, we present the optimal resource allocation problem for parallel transmission utilizing multiple RATs. Since the formulated problem is NP-hard, a modified problem is proposed, for which a near-optimal resource allocation algorithm is developed by exploiting the intrinsic quasiconcavity of the problem. To reduce the computational complexity, we develop a low-complexity suboptimal allocation strategy based on joint iterative subcarrier and waterfilling power allocation over multiple RATs. Simulation results show that the proposed algorithms can achieve a higher EE compared to a conventional spectral-efficiency-based approach and can obtain performance comparable with the optimal solution, but with much less complexity.

Application of Full-Duplex Wireless Technique into Secure MIMO Communication: Achievable Secrecy Rate based Optimization

Abstract: This letter considers the secure MIMO transmission in a wireless environment, in which one transmitter (Alice), one receiver (Bob) and one eavesdropper (Eve) are involved. Apart from the artificial noise (AN) generated by Alice, Bob can also exploit his remaining antenna resources to emit AN to further impair Eve's channel. Such kind of AN can be cancelled by Bob himself by applying the Full-Duplex wireless communication technique. A computable secrecy rate is obtained for this model over a Rayleigh-fading eavesdropping channel. In order to maximize the secrecy rate, a joint optimization scheme is proposed to assign the TX/RX antennas for Bob and to design the beamforming and power allocation for Alice's information and AN signal. Simulation is carried out to illustrate the process of the proposed optimization scheme.

CAD-MAC: A Channel-Aggregation Diversity Based MAC Protocol for Spectrum and Energy Efficient Cognitive Ad Hoc Networks

Abstract: In cognitive Ad Hoc networks (CAHN), because the contentions and mutual interferences among secondary nodes are inevitable as well as secondary nodes usually have limited power budget, spectrum efficiency and energy efficiency are critically important to the CAHN, especially for the medium access control (MAC) protocol design. Aiming at improving both spectrum and energy efficiencies, we in this paper propose a diversity technology called Channel-Aggregation Diversity (CAD), through which each node can utilize multiple channels simultaneously and efficiently allocate the upper-bounded power resource with only one data radio. Based on the proposed CAD technology, we further develop a CAD-based MAC (CAD-MAC) protocol, which enables the secondary nodes to sufficiently use available channel resources under the upper-bounded power and transmit multiple data packets in one transmission process subject to the transmission-time fairness constraint. In order to improve the performance of CAHNs, we propose two joint power-channel allocation schemes. In the first scheme, we aim at maximizing the data transmission rate. By converting the joint power-channel allocation to the Multiple-Choice Knapsack Problem, we derive the optimal allocation policy through dynamic programming. In the second scheme, our objective is to optimize the energy efficiency and we obtain the corresponding allocation policy through fractional programming. Simulation results show that our proposed CAD-MAC protocol can efficiently increase the spectrum and energy efficiencies as well as the throughput of the CAHN compared with existing protocols. Moreover, the energy efficiency of the CAHN can be further improved by adopting the energy efficiency optimization based resource allocation scheme.

Distributed Channel Allocation Protocols for Wireless Sensor Networks

Abstract: Interference between concurrent transmissions can cause severe performance degradation in wireless sensor networks (WSNs). While multiple channels available in WSN technology such as IEEE 802.15.4 can be exploited to mitigate interference, channel allocation can have a significant impact on the performance of multi-channel communication. This paper proposes a set of distributed protocols for channel allocation in WSNs with theoretical bounds. We first consider the problem of minimizing the number of channels needed to remove interference in a WSN, and propose both receiver-based and link-based distributed channel allocation protocols. Then, for WSNs with an insufficient number of channels, we formulate a fair channel allocation problem whose objective is to minimize the maximum interference (MinMax) experienced by any transmission link in the network. We prove that MinMax channel allocation is NP-hard, and propose a distributed link-based MinMax channel allocation protocol. Finally, we propose a distributed protocol for link scheduling based on MinMax channel allocation that creates a conflict-free schedule for transmissions. The proposed decentralized protocols are efficient, scalable, and adaptive to channel condition and network dynamics. Simulations based on the topologies and data traces collected from a WSN testbed of 74 TelosB motes have shown that our channel allocation protocols significantly outperform a state-of-the-art channel allocation protocol.

Energy-Efficient Spectrum Access in Cognitive Radios

Abstract: Cognitive radio (CR) and energy-efficient design have emerged as two promising techniques to achieve high spectrum efficiency (SE) and energy efficiency (EE), respectively. In this paper, we study energy-efficient opportunistic spectrum access strategies for an orthogonal frequency division multiplexing (OFDM)-based CR network with multiple secondary users (SUs). Both worst EE and average EE are considered and optimized for different emphases and application scenarios. Since the original optimization issues belong to nonconvex integer combinatorial fractional program and are essentially NP-hard for an optimal solution, we use continuous and convex relaxation to modify the problems for somewhat better mathematical tractability. For the relaxed worst-EE-based spectrum access problem, we first demonstrate the joint quasiconcavity of EE on subchannel and power allocation matrices and then develop a framework to find the optimal solution based on efficient root finding and convex optimization. We also develop a low-complexity alternative for suboptimal solution. The relaxed average-EE-based spectrum access problem is still NP-hard and may have many local optima. We first transform the problem into an equivalent form and introduce a general concave envelope based branch-and-bound (B&B) approach to find the global optimal solution. We then exploit the underlying properties of the energy-efficient transmission to speed up the convergence of the B&B approach. Besides, we develop a low-complexity heuristic approach to find a suboptimal solution. Simulation results show that the energy-efficient spectrum access strategies significantly boost EE compared with the conventional spectral-efficient spectrum access ones while the low-complexity suboptimal approaches can well balance the performance and complexity.

Joint mode selection, channel allocation and power assignment for green device-to-device communications

Abstract: Device-to-Device (D2D) communication has emerged as a promising technique for improving capacity and reducing power consumption in wireless networks. Most existing works on D2D communications either targeted CDMA-based single-channel networks or aimed to maximize network throughput. In this paper, we, however, aim at enabling green D2D communications in OFDMA-based wireless networks. We formally define an optimization problem based on a practical link data rate model, whose objective is to minimize power consumption while meeting user data rate requirements. We then present an effective algorithm to solve it in polynomial time, which jointly determines mode selection, channel allocation and power assignment. It has been shown by extensive simulation results that the proposed algorithm can achieve over 57% power savings, compared to several baseline methods.

Novel Cross-Layer QoE-Aware Radio Resource Allocation Algorithms in Multiuser OFDMA Systems

Abstract: The assurance of quality of experience (QoE) and provisioning of high throughput of the system represent the main goals of future wireless and mobile networks. This paper presents novel and practical cross-layer QoE-aware radio resource allocation (RRA) algorithms for the downlink of a heterogeneous orthogonal frequency division multiple access (OFDMA) system. The objective of the proposed algorithms is to assure the appropriate level of QoE for each user of the system by incorporating application-layer parameters and subjective human perception of quality into the RRA process. We propose two user-oriented joint subcarrier and power allocation algorithms with low complexity for real-time and interactive services. The first algorithm dynamically allocates resources by assuring the same level of QoE to all users of the system, whereas the second algorithm introduces the efficient trade-off between the user's QoE and the spectral efficiency of the system. By considering application-layer parameters and user's perception of quality, high users' QoE and explicit control of data rates can be achieved. The numerical results show that the proposed algorithms achieve significant increase in the level of QoE compared to previous works, a fair distribution of capacity among users and near to optimal solution of QoE for the OFDMA system.

Performance Analysis of Chunk-Based Resource Allocation in Multi-Cell OFDMA Systems

Abstract: In the orthogonal frequency division multiple access (OFDMA) system, one of the efficient and low complex methods to allocate radio resources among multiple users is chunk-based resource allocation, which groups a number of adjacent subcarriers into a chunk and allocates resources chunk by chunk. In this paper, performance analysis of chunk-based resource allocation is studied in the multi-cell OFDMA environment. Fractional frequency reuse (FFR) is considered in the cellular OFDMA. Basically, FFR divides each cell into central and edge areas where two different values of the frequency reuse factor are assumed. This paper analytically evaluates how spectral efficiency performance is affected by system parameters, including radius ratio of the central area to the whole cell, transmit signal to noise ratio (SNR), number of users, number of subcarriers per chunk, and coherence bandwidth. The numerical results show that there exists an optimal radius ratio to achieve the highest spectral efficiency in the proposed research. The optimal radius ratio is about 0.7, which is almost irrespective of the SNR, number of users, and number of subcarriers per chunk. In other words, the sizes of the central area and the edge area of the whole cell are almost equal when achieving the optimal performance.

On Efficient Resource Allocation for Cognitive and Cooperative Communications

Abstract: Cooperative communication (CC) can offer high channel capacity and reliability in an efficient and low-cost way by forming a virtual antenna array among single-antenna nodes that cooperatively share their antennas. It has been well recognized that the selection of relay nodes plays a critical role in the performance of multiple source-destination pairs. Unfortunately, all prior work has made an unrealistic assumption that spectrum resources are unlimited and each source-destination pair can communicate over a dedicated channel with no mutual interference. In this paper, we study the problem of maximizing the minimum transmission rate among multiple source-destination pairs using CC in a cognitive radio network (CRN). We jointly consider the relay assignment and channel allocation under a finite set of available channels, where the interference must be considered. In order to improve the spectrum efficiency, we exploit the network coding opportunities existing in CC that can further increase the capacity. Such max-min rate problems for cognitive and cooperative communications are proved to be NP-hard and the corresponding MINLP (Mixed-Integer Nonlinear Programming) formulations are developed. Moreover, we apply the reformulation and linearization techniques to the original optimization problems with nonlinear and nonconvex objective functions such that our proposed algorithms can produce high competitive solutions in a timely manner. Extensive simulations are conducted to show that the proposed algorithms can achieve high spectrum efficiency in terms of providing a much improved max-min transmission rate under various network settings.

Channel Assembling with Priority-Based Queues in Cognitive Radio Networks: Strategies and Performance Evaluation

Abstract: With the implementation of channel assembling (CA) techniques, higher data rate can be achieved for secondary users in multi-channel cognitive radio networks. Recent studies which are based on loss systems show that maximal capacity can be achieved using dynamic CA strategies. However the channel allocation schemes suffer from high blocking and forced termination when primary users become active. In this paper, we propose to introduce queues for secondary users so that those flows that would otherwise be blocked or forcibly terminated could be buffered and possibly served later. More specifically, in a multi-channel network with heterogeneous traffic, two queues are separately allocated to real-time and elastic users and channel access opportunities are distributed between these two queues in a way that real-time services receive higher priority. Two queuing schemes are introduced based on the delay tolerance of interrupted elastic services. Furthermore, continuous time Markov chain models are developed to evaluate the performance of the proposed CA strategy with queues, and the correctness as well as the preciseness of the derived theoretical models are verified through extensive simulations. Numerical results demonstrate that the integration of queues can further increase the capacity of the secondary network and spectrum utilization while decreasing blocking probability and forced termination probability.

Distributed Channel Selection for Interference Mitigation in Dynamic Environment: A Game-Theoretic Stochastic Learning Solution

Abstract: In this paper, we investigate the problem of distributed chan- nel selection for interference mitigation in a canonical communication network. The channel is assumed time-varying, and the active user set is considered dynamically variable due to the specific service requirement. This problem is formulated as an exact potential game, and the optimality property of the solution to this problem is first analyzed. Then, we design a low-complexity fully distributed no-regret learning algorithm for chan- nel adaptation in a dynamic environment, where each active player can independently and automatically update its action with no information exchange. The proposed algorithm is proven to converge to a set of correlated equilibria with a probability of 1. Finally, we conduct simula- tions to demonstrate that the proposed algorithm achieves near-optimal performance for interference mitigation in dynamic environments. Index Terms—Distributed channel allocation, dynamic environment, interference mitigation, no-regret learning, potential game.

Dynamic resource allocations based on Q-learning for D2D communication in cellular networks

Abstract: In order to solve the problem of spectrum and power allocation for D2D communication in cellular networks when the prior knowledge is not available, a method based on machine learning is proposed in this paper. Q-learning, which is one of the most important algorithms in machine learning, is proposed to solve the radio resource management in underlay mode to find the optimal strategy in time series. In this mode, Q-learning is used to finish the channel assignment and the power allocation at the same time. And the simulation results show that greater system capacity can be achieved through the method proposed in this paper.

Radio resource allocation for full-duplex OFDMA networks using matching theory

Abstract: Wireless radio links are generally half-duplex, i.e., they can either transmit or receive in a single channel but not both simultaneously. To achieve single band simultaneous bidirectional communication, full-duplex radio links are recently utilized in wireless networks. Adoption of OFDMA scheme in the full-duplex radio links has also stimulated new research interest to improve the wireless networks' transmission rate even further [1]. Typically, a full-duplex OFDMA network consists of a common base station (BS), and multiple users as transmitters (TXs) and receivers (RXs). To achieve a satisfying sum-rate in the network, the TXs and RXs need to be properly paired into separate transceiver units, and a suitable subcarrier should be assigned to each transceiver unit. The BS also allocates proper power level to each transceiver unit such that the rate performance of the whole network is maximized. Due to the combinatorial nature of pairing multiple TXs, RXs, and subcarriers, and also the complexity of optimal power allocation to each subcarrier-transceiver pair, resource allocation in such a full-duplex OFDMA network can be very challenging.

Sequential Learning for Multi-Channel Wireless Network Monitoring With Channel Switching Costs

Abstract: We consider the problem of optimally assigning $p$ sniffers to $K$ channels to monitor the transmission activities in a multichannel wireless network with switching costs. The activity of users is initially unknown to the sniffers and is to be learned along with channel assignment decisions to maximize the benefits of this assignment, resulting in the fundamental tradeoff between exploration and exploitation. Switching costs are incurred when sniffers change their channel assignments. As a result, frequent changes are undesirable. We formulate the sniffer-channel assignment with switching costs as a linear partial monitoring problem, a superclass of multiarmed bandits. As the number of arms (sniffer-channel assignments) is exponential, novel techniques are called for, to allow efficient learning. We use the linear bandit model to capture the dependency amongst the arms and develop a policy that takes advantage of this dependency. We prove that the proposed Upper Confident Bound-based (UCB) policy enjoys a logarithmic regret bound in time $t$ that depends sublinearly on the number of arms, while its total switching cost grows in the order of $O(\log\log(t))$ .

An Experimental Comparison of Different Bit-and-Power-Allocation Algorithms for DCO-OFDM

Abstract: This research comprises an end-to-end experimental comparison of the bit-rates and bit error-rates of four major adaptive bit-and-power allocation algorithms for dc biased optical orthogonal frequency-division multiplexing. The comparison includes studying different channel conditions and different numbers of subcarriers. The experimental results show that all the methods compared display similar performance without significant superiority of a single method, even though in theory some difference is expected. Our analysis of the experimental data showed that the main reason for the uniformity in performance is the high variance in the channel estimation, especially at higher frequency subcarriers.

Smart spectrum allocation for interference mitigation in Wireless Body Area Networks

Abstract: In this paper, a dynamic resource allocation scheme is proposed to avoid interference amongst coexisting Wireless Body Area Networks (WBAN). In the proposed scheme, each WBAN generates a table consisting of interfering nodes from coexisting WBANs in its vicinity. Then each WBAN broadcasts this table to its neighbors, which allows for efficient interpretation of an Interference Region (IR) between each pair of WBANs. The nodes in the IR are later allocated orthogonal sub-channels; whilst nodes that do not exist in the IR can potentially transmit in the same time interval. We further demonstrate a precise tradeoff between the minimum interference level and spatial reuse. Simulation results show that our proposed scheme has far better spectral efficiency compared to the conventional orthogonal schemes, whilst maintaining an acceptable interference level. We also provide mathematical analysis on the proposed scheme to validate its efficiency for increasing spectral efficiency and avoiding interference. To further reduce the interference level, we propose a probabilistic approach, and analytically show that the outage probability can be effectively reduced at the cost of very small change in the spatial reuse factor.

Fully Distributed Optimal Channel Assignment for Open Spectrum Access

Abstract: In this paper, we address the problem of fully distributed assignment of users to sub-bands such that the sum-rate of the system is maximized. We introduce a modified auction algorithm that can be applied in a fully distributed way using an opportunistic CSMA assignment scheme and is e optimal. We analyze the expected time complexity of the algorithm and suggest a variant to the algorithm that has lower expected complexity. We then show that, in the case of i.i.d. Rayleigh channels, a simple greedy scheme is asymptotically optimal as SNR increases or as the number of users is increased to infinity. We conclude by providing simulated results of the suggested algorithms.

Exploiting mobility in proportional fair cellular scheduling: Measurements and algorithms

Abstract: Proportional Fair (PF) scheduling algorithms are the de facto standard in cellular networks. They exploit the users' channel state diversity (induced by fast-fading) and are optimal for stationary channel state distributions and an infinite time-horizon. However, mobile users experience a nonstationary channel, due to slow-fading (on the order of seconds), and are associated with base stations for short periods. Hence, we develop the Predictive Finite-horizon PF Scheduling ((PF)2S) Framework that exploits mobility. We present extensive channel measurement results from a 3G network and characterize mobility-induced channel state trends. We show that a user's channel state is highly reproducible and leverage that to develop a data rate prediction mechanism. We then present a few channel allocation estimation algorithms that exploit the prediction mechanism. Our trace-based simulations consider instances of the ((PF)2S) Framework composed of combinations of prediction and channel allocation estimation algorithms. They indicate that the framework can increase the throughput by 15%-55% compared to traditional PF schedulers, while improving fairness.