Optimal resource allocation techniques for MIMO-OFDMA based cognitive radio networks using integer linear programming

In this paper, we present an analytical study for the performance of a two-tier primary-secondary network based on IEEE 802.11 DCF mechanism. It is assumed that multiple primary users (PUs) and secondary users (SUs) coexist in the radio environment and share a single band such that the SUs are allowed to contend only if they sense the channel idle for a certain period of time. First, we derive the PUs' medium access delay in the presence of SUs. Then, assuming an exponential packet inter-arrival time for the primary network, and using M/G/1 queue modeling, we determine the collision probability and throughput for the primary and secondary networks, as well as the PUs' average total delay including the queuing delay. Numerical along with simulation results show how the performance in the primary network, with given packet arrival rate, can be affected as functions of the sensing time, packet payload size and population size of the SUs. The findings of this work can be used effectively to study the performance of any IEEE 802.11 based primary-secondary network, such as cognitive radio networks where SUs may exploit a primary channel opportunistically based on a predefined policy, as well as to optimize the performance of distributed cognitive radio MAC protocols.

A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.

In this study, the problem of robust adaptive power control (PC) in an underlay cognitive radio network with multiple secondary users (SUs) and primary users (PUs) is considered. Due to the effects of uncertainties (i.e. estimation errors, delays), the optimal PC (resource allocation) cannot guarantee the quality of service of SUs and PUs under imperfect channel state information and interference power of PUs. A robust resource allocation problem is formulated to maximise sum throughput of SUs under individual power constraints and signal-to-interference-and-noise ratio constraints of SUs as well as interference temperature constraints of PUs, whereas channel uncertainties and interference uncertainties induced into the secondary system are modelled by multiplicative uncertainties. Under the worst-case approach, the problem is transformed into a geometric programming problem solved by Lagrange dual methods. The performance of the different algorithms and the impact of uncertainties are discussed according to several simulation results.

In cognitive radio networks, secondary users need to access the licensed spectrum while neither disturbing the transmissions of the primary users nor introducing much interference to other secondary users. Hence, proper power controlling is of paramount importance to ensure efficient system operation. The previous work on power control in cognitive networks mainly focuses on the interactions among a set of secondary users. The primary users' influence is rarely considered. In this paper, a new power control game model is formulated, by including the primary users into the player set of the game. The primary users are rewarded for sharing their licensed spectrum with secondary users by setting a reasonable interference cap for the secondary transmissions. However, we ensure a minimum required Quality of Service (QoS) for primary users by severely penalizing their utility if their required transmission quality is not met. Simultaneously, the secondary users achieve energy efficient transmissions while not introducing much interference to both the primary and other secondary users. We prove that the proposed cognitive power control game has a unique Nash equilibrium (NE). The numerical examples show that the proposed game theoretical power control algorithm can provide the secondary users with energy efficient transmissions and the primary users with reasonable monetary rewards, while not compromising their required QoS.

We consider a distributed random channel access policy in a cognitive radio network where multiple secondary users (SUs) contend for spectrum usage over available multiple primary user (PU) channels. In the random channel access policy each SU stochastically determines whether to access a channel based on the access probability (AP) which is adapted to the sensing results. It is then important to obtain the optimal AP values from the SUs' throughput performance perspective. In our analysis, we assume that all SUs acquire a common channel state information that is potentially erroneous. We analyze the throughput of an arbitrary SU and the packet collision probability of a PU due to the interference by SUs. By considering a requirement on the packet collision probability of a PU, we rigorously derive an explicit expression on the optimal AP values that maximize the throughput of an SU under the requirement for PUs. In the derivation we show that the multi-dimensional optimization problem with AP values can be converted into a one-dimensional optimization problem, so that the optimal AP values can be easily found. Numerical examples are provided to validate our analysis and to investigate the performance behavior of the optimal channel access policy.

This paper investigates the secure communication issue for both static and dynamic cognitive radio networks (CRNs), where multiple nonaltruistic primary users (PUs), secondary users (SUs), and eavesdroppers exist. The design objective is to provide secure communications for PUs and, meanwhile, to ease the starvation of transmission opportunities for SUs. To achieve this goal, we propose a barter-like trading model to incentivize the cooperation among nonaltruistic users. Specifically, PUs leverage the assistance of SUs in the form of cooperative relaying or friendly jamming and, in return, yield certain licensed spectrum accessing time to the aided SUs. We propose a truthful nonmonetary double auction framework (FONDA) toward secure communications for static CRN where PUs and SUs interact in a single round. Then, we extend our framework to d-FONDA for dynamic CRN, where SUs who have patience (tolerant of traffic delay) arrive and leave the network dynamically. We prove that both FONDA and d-FONDA preserve nice economic properties, including truthfulness, individual rationality, and budget balance. Simulation results reveal that the proposed frameworks provide substantial performance gains compared with the baseline scheme and suffer acceptable performance degradation over ideal schemes.

Cognitive Radio Network is a form of communication where licensed frequency band of the Primary users (PUs) are made available to the Secondary user (SU) with constraint interference to the PUs. In this work, we have investigated a novel model considering interweave approach for spectrum access, with multiple primary users and single secondary user (SU). Multiple antennas have been considered at both the primary users as well as the secondary users. The activity of primary users are modeled as Poisson process. In addition, we also propose a new approach of sensing for a MIMO cognitive radio network using energy detection. The proposed method provides a closed form expression for probability of detection (P d ) and probability of false alarm (P f ) in a Multiple Input Multiple Output (MIMO) channel. Further, the throughput of secondary user as well as interference on primary users caused due to secondary user transmissions, has been computed for the proposed model.

In this paper, the spectrum leasing in Cognitive Radio Networks (CRN) based on property-right model is addressed when a single Primary User (PU) coexists with multiple Secondary Users (SU). In this model, the PU as the spectrum owner allows the SU to use a portion of the spectrum band in exchange for providing cooperative relaying service. We propose a novel game theoretic model, where the PU is able to monitor the behavior of the SUs in terms of cooperation. Using the proposed credit-based mechanism, the PU will be notified about any dishonest action of the SUs after they utilized the spectrum access, therefore the PU may decide not to select the fraudulent SUs in the next time slots. The proposed accumulative cooperative credit keeps the record of the portion of power is used by each SU to perform the relaying service. In each round, the PU observes the SUs' credit and selects a reliable one with a relatively good channel condition.

We investigate the interactive behavior and strategic decision-making between multiple secondary users (SUs) and primary users (PUs), both of which are end-to-end performance aware in cognitive networks. A Stackelberg game is utilized to formulate the spectrum utilization maximization problem after the complex interference situation is analyzed. Especially, an interference power cap (IPC) function predefined by PUs as a pricing function is introduced into the utility function design of SUs to guarantee QoS of PUs, as well as to decouple constraints. Further, an asymmetric information situation can be formed by considering PUs as leaders who employ the optimal water-filling algorithm, and the closed-form power strategy of SUs can be derived. And accordingly, SUs as followers can observe the available information to do more foresighted decision by learning. What is more, we prove the optimality and existence of the deceived solutions. Numerical results demonstrate that the proposed distributed algorithm provides more spectrum revenue and better QoS guarantees to PUs with limited iterations.

Spectrum sharing and power allocation are interesting issues in cognitive radio networks. In our paper, we introduce a pricing scheme to demonstrate the spectrum sharing scenario between primary users and secondary users. We demonstrate the joint optimal allocation scheme of both spectrum and power for secondary users buying spectrum from multiple primary users. The joint optimal allocation scheme maximizes secondary users' utilities while primary users' utilities meet certain requirements. Moreover, we illustrate a distributed resource allocation scheme to jointly optimize usage of power and spectrum when each secondary user does not know others' buying strategies. Our simulation results show that under some utility requirements, the joint optimized resource allocation scheme has 3dB gain over that under even resource allocation.

In a cognitive radio network (CRN), a primary (licensed) user (PU) can share its frequency spectrum by renting this spectrum to a secondary (unlicensed) user (SU). In this paper, we propose an auction-based CRN model whose novelty is considering multiple PUs along with multiple SUs. These PUs compete with each other so that they can earn more profit while the SUs pay less and earn higher payoffs. This situation is formulated as an oligopoly market. For the SUs, we adopt Hackner utility function to obtain the demands for the frequency spectrum and according to their payoff functions the best scenario in each stage of the dynamic spectrum sharing game is selected in the case in which there is a limit on PUs to supply SUs' demands. Simulation results show that our proposed model satisfies both groups of PUs and SUs.

Cognitive Radio (CR) aims to provide efficient utilization of the scarcely available radio spectrum. Spectrum sensing is indispensable to identify spectrum holes. Inaccurate sensing results in interference to PU. Therefore, sensing accuracy is an important parameter. Cooperative Spectrum Sensing (CSS) is an efficient technique to improve the sensing accuracy. It makes use of cooperation between Secondary Users (SUs) to detect the presence of Primary User (PU). Game theoretical models help in understanding and modeling interaction among users. In this paper, a CR network with multiple PUs and SUs is considered. CSS is modeled as hedonic coalition formation game where SUs sensing the same channel form coalitions. The coalitions are formed on the basis of probability of detection. For each PU, the SUs which have maximum probability of detection form coalition and cooperatively make the final sensing decision by making use of OR rule. Channel estimation for ST-SR link is done using WARP testbed. The sensing accuracy and energy consumption for the coalitions when link is modeled using free space pathloss model and using WARP testbed are found out. Simulation results show that CSS modeled using hedonic coalition formation has better sensing accuracy than non-cooperative sensing.

This paper considers the spectrum sharing multiple-input multiple-output (MIMO) cognitive radio network, in which single primary user (PU) coexists with multiple secondary users (SUs). Joint beamforming and admission control with adaptive modulation technique is designed to maximize the number of the serviced SUs and the transmit rate of the secondary links while imposing upper limits on the interference temperature to the PU by using the least transmit power. In the proposed cross-layer design, the SINR target is dynamically adapted with channel condition and bit-error rate (BER) requirement, and works as admission control implicitly, that is, the cognitive link is dropped as long as its SINR requirement is equal to zero. With the pre-fixed receiver, the multi-objective optimization problem is NP-hard, which is further transformed into single problem by introducing a utility function. The optimum solutions are achieved via second-order cone programming (SOCP) with an iterative bisection search method. Simulation results show that the proposed scheme achieves desirable spectrum and power efficiency, and the maximum number of the total active SUs are intelligently selected.

We investigate opportunistic cooperation between secondary (unlicensed) users and primary (licensed) users in cognitive radio networks. We consider two models for such cooperation. In the first model, called the Cooperative Relay Model, a secondary user cannot transmit its own data concurrently with a primary user. However, it can employ cooperative relaying in order to improve the effective transmission rate of the primary user. In the second model, called the Interference Model, a secondary user is allowed to transmit its data concurrently with a primary user. However, the secondary user can “cooperate” by deferring its transmissions when the primary user is busy. In both models, the secondary users must make intelligent cooperation decisions as they seek to maximize their own throughput subject to average power constraints. The decision options are different during idle and busy periods of the primary user, and the decisions in turn influence the durations of these periods according to a controllable infinite state Markov chain. Such problems can be formulated as constrained Markov decision problems, and conventional solution techniques require either extensive knowledge of the system dynamics or learning based approaches that suffer from large convergence times. However, using a generalized Lyapunov optimization technique, we design a novel greedy and online control algorithm that overcomes these challenges. Remarkably, this algorithm does not require any knowledge of the network arrival rates and is provably optimal.

In this work it is considered a ultra wideband cognitive radio network where multiple secondary users (SUs) communicate with an access point (AP) in an opportunistic way. The SUs adopt a distributed medium access control (MAC) scheme based on time contention through random medium access. One or multiple primary users (PUs) may become active in the entire band or in part of it, which may impact in the communications of the SUs. Departing from a classical random access scheme for the SUs, it is derived a solution to estimate the level of activity of the PUs through the observation of different data available at the MAC layer of each SU. Differently from the majority of the works already proposed, no physical spectrum sensing is needed to estimate the activity of the PUs since the estimation only relies on data collected at the MAC layer. Based on the level of PUs' activity detected by each SU, it is proposed a solution to regulate the SU's medium access in order to guarantee a given level of protection to the PUs. The formal methodology is evaluated through simulation and several results confirm the effectiveness of our proposal.