Dynamic spectrum access analysis in a multi-user cognitive radio network using Markov chains

Dynamic spectrum access has been proposed to address the spectrum scarcity. Based on this access mode, secondary users are allowed to opportunistically operate when primary users are absent. Secondary Users are, however, required to relinquish the spectrum upon return of the incumbent spectrum holder. Although it improves spectrum utility, opportunistic spectrum access limits secondary users' access to exogenous spectrum holes vacated by primary users. We argue that spectrum holes should be endogenous, enabled by incentives to incumbent users to increase spectrum availability, while mitigating risk. To this end, we take a different perspective on dynamic spectrum sharing and propose a coopetive framework, whereby primary and secondary users engage in a cooperative, yet competitive, auction-driven spectrum sharing to enhance spectrum usage. In this framework, the primary users'spectrum is organized in three bands, namely exclusive usage, usage right and management right bands. Based on this structure, spectrum is traded as financial options to increase trading flexibility and capture the value primary and secondary users attach to spectrum access and usage. Spectrum trading between primary and secondary users is formalized as a utility maximization problem. Approximate solutions to this problem are derived and their performance is analyzed. The results show that spectrum trading, which combines revocable and exclusive leasing, provides higher utilities for both primary and secondary users.

As the novel and effective approach to improving the utilization of the precious radio spectrum, cognitive radio technology is the key to realize the dynamic spectrum access (DSA) networks, where the secondary (unlicensed) users can opportunistically utilize the unused licensed spectrum in a way that confines the level of interference to the range the primary (licensed) users can tolerate. However, there are many new challenges associated with cognitive-radio-based DSA networks, such as the multi-channel hidden terminal problem and the fact that the time-varying channel availability differs for different secondary users, in the medium access control (MAC) layer. To overcome these challenges, we propose an efficient Cognitive Radio-EnAbled Multi-channel MAC (CREAM-MAC) protocol, which integrates the cooperative sequential spectrum sensing at physical layer and the packet scheduling at MAC layer, over the wireless DSA networks. Under the proposed CREAM-MAC protocol, each secondary user is equipped with a cognitive radio-enabled transceiver and multiple channel sensors. Our cooperative sequential spectrum sensing scheme improves the accuracy of spectrum sensing and further protects the primary users. The proposed CREAM-MAC enables the secondary users to best utilize the unused frequency spectrum while avoiding the collisions among secondary users and between secondary users and primary users. We develop the Markov chain model and M/GY/1 queueing model to rigorously study our proposed CREAM-MAC protocol for both the saturation networks and the non-saturation networks. We also conduct extensive simulations to validate our developed protocol and analytical models.

Cognitive Radio (CR) technique has been proposed for improving spectrum efficiency by dynamic spectrum access. In Cognitive Radio Networks (CRNs), unlicensed Secondary Users (SUs) with CR can utilize licensed spectrum without interfering licensed Primary Users (PUs). For effectively avoiding interference with licensed PUs and malicious attacks from jammers, a two-stage Learning-based Optimal Channel Selection (LOCS) algorithm for unlicensed SUs in distributed heterogeneous CRNs is proposed in this paper. The LOCS algorithm enables SUs to obtain real states of the licensed channels without knowing their information. Hence, SUs using LOCS algorithm can efficiently avoid collision and attack with PUs and jammers. Besides, the LOCS algorithm considers hardware limitation of the SUs, i.e., SUs can only sense and access parts of the license spectrum during any given time. SUs can select the optimal channels for spectrum sensing and data transmission by using the LOCS algorithm. Simulation results show the efficiency of our proposed algorithm in terms of collision and attack avoidance.

The spectrum scarcity problem can be tackled by promoting a more efficient use of the radio spectrum. Spectrum sharing techniques like TV White Spaces (TVWS), Licensed Shared Access (LSA), and Spectrum Access System (SAS), are adequate solutions for this problem and there exist already regulation and standardization efforts worldwide. To evaluate the coexistence between primary and secondary spectrum users, a Long Term Evolution (LTE) multi-cell simulator has been designed, which employs a generic spectrum sharing technique, Dynamic Spectrum Access (DSA), in order to obtain additional network capacity. As a way to bring attention and confidence of the telecommunications sector regarding the benefits that this technology may bring, the analysis is done in terms of interference suffered by incumbents (primary users) and capacity by secondary users. Results have confirmed the unfeasibility of co-channel coexistence for moderate distances between the cordless camera service (incumbent) and LTE network (secondary user) due to high interference. However, for adjacent bands, a gain of 40% in the secondary users’ capacity is possible following more restrictive interference protection requirements and 30% gain with the smaller frequency separation between primary and secondary operating frequencies.

A robust beamformer design is proposed for a spectrum sharing CR network consisting of multiple secondary users (SUs) in the presence of multiple primary users (PUs). The proposed algorithm aims to maximize the worst case signal-to-interference-plus-noise ratio (SINR) of the SUs while limiting the interference leakage to PUs. Since it may be difficult to obtain perfect information about the channels between the SU basestation and the PUs, we consider errors in the PU channel estimates and design beamformers to limit interference leakage to PU below a specific threshold with a certain probability. The simulation results demonstrate that the proposed beamformer design has the ability to maximize the worst case SINR (balanced SINR) while controlling the interference leakage to PUs.

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 cognitive radio networks (CRNs), primary users (PUs) have the highest priority in channel resource allocation. Secondary users (SUs) can generally only utilize temporarily unused channels of PUs, share channels with PUs, or cooperate with PUs to gain priority through the interweave, underlay, and overlay modes. Traditional optimization schemes for channel resource allocation often lead to structural wastage of channel resources, whereas approaches such as reinforcement learning—though effective—require high computational power and thus exhibit poor adaptability in industrial deployments. Moreover, existing works typically optimize a single performance metric with limited scenario scalability. To address these limitations, this paper proposes a CR network algorithm based on the hybrid users (HU) concept, which links the Interweave and Underlay modes through an adaptive threshold for mode switching. The algorithm employs the Hungarian method for SU channel allocation and applies a multi-level power adjustment strategy when PUs and SUs share the same channel to maximize channel resource utilization. Simulation results under various parameter settings show that the proposed algorithm improves the average signal to interference plus noise ratio (SINR) of SUs while ensuring PU service quality, significantly enhances network energy efficiency, and markedly improves Jain’s fairness among SUs in low-power scenarios.

Over the past few years there has been a growing demand for radio resources and at the same time these resources are under utilized due to static spectrum allocation techniques. Dynamic spectrum access (DSA) has been thought of as a solution that would satisfy both the growing demand for radio resources and to efficiently utilize the spectrum. The radio devices that have the capability to dynamically sense the spectrum and access the under utilized bands are called cognitive radios (CR). There are two broad classes of users in CR, the primary user is a licensed user of a particular radio frequency band and the secondary users are unlicensed users who cognitively operate without causing harmful interference to the primary user. In this paper we consider a denial attack on centralized DSA networks where a malicious secondary user masquerades as a primary user and effectively shuts off access to all other secondary users. Note that this problem is unique to CR due to the distinction between primary and secondary users. We propose a public key cryptography based primary user identification mechanism that prevents malicious secondary users from masquerading as primary users. We show that the proposed identification mechanism and the associated key management are computationally light weight. We also discuss some advantages and limitations of the proposed identification mechanism.

A prototype Dynamic Spectrum Access (DSA) system is implemented, for ease of implementation, using the audio spectrum. In this setup, laptop PCs behave as two software defined radios (SDR), one a primary user (PU) and the other a secondary user (SU). The radios communicate digital information over the air, using the audio capabilities of standard soundcards. The PU, which is emulating the licensed user of an audio channel centered at 4 kHz, communicates using binary phase shift keying (BPSK) modulated data in periodic bursts. A cognitive radio (CR) based SU wishes to opportunistically occupy the PU channel during the inter-burst times, i.e. during the temporal ‘spectrum holes,’ while not interfering with the PU traffic. The PU link is implemented in MATLAB, while the SU is defined in GNU Radio with a separate Python control module defining its algorithm for spectrum sensing and hopping. The implementation of both communication links is described, as well as the initial performance evaluation of the preliminary system in terms of bit error rate performance of the PU with and without the presence of an active SU. The measured BER achieved for the best of the PU bursts with DSA was on the order of a few percent, just slightly higher than without DSA.

Spectrum sensing is a critical function for enabling dynamic spectrum access (DSA) in wireless networks that utilize cognitive radio (CR). In DSA networks, unlicensed secondary users can gain access to a licensed spectrum band as long as they do not interfere with primary users. Spectrum sensing is subject to errors in the form of false alarms and missed detections. False alarms cause spectrum under-use by secondary users, and missed detections cause interference to primary users. Although existing research has demonstrated the utility of a Markov chain for modeling the spectrum access pattern of primary users over time, little effort has been directed toward spectrum sensing based upon such models. In this paper, we develop soft-input sequence detection algorithms of Markov sources in noise for spectrum sensing in DSA networks. We assign different Bayesian cost factors for missed detections and false alarms, and we show that a suitably modified Forward-Backward sequence detection algorithm is optimal in minimizing the detection risk. Along the way, we observe new fundamental limitations that we call "Risk Floor" and "Limiting ROC" for energy detection and coherent detection due to the PU's spectrum access pattern.

We consider a cooperation scenario between primary users and untrusted secondary users in cognitive radio networks. The secondary users are willing to help the primary users to relay the primary users' messages in reward for being allowed to share the primary users' spectrum bands. However, the primary users might be reluctant to accept this help, since the secondary users are untrustworthy and may try unauthorized decoding of the primary users' messages. This paper will answer the question: when is this cooperation mutually beneficial for primary and secondary users? Taking an approach of information-theoretic secrecy, such as coding techniques for wiretap channels, the primary users can allow the secondary users to sense and relay the message, while ensuring that the secondary users are ignorant of the primary users' messages. We characterize an achievable secrecy rate of primary users with a rate of the secondary users' communication. From the derived rate pairs, an optimization problem is formulated such that the secondary transmitter distributes its transmit power to maximize its data rate while providing a higher secrecy rate to the primary users. We demonstrate that this cooperation can provide a positive secrecy rate, even when a non-cooperative scheme achieves a zero secrecy rate.

This paper studies the case of an overlay cognitive radio network where the primary user leases spectral resources to the secondary user in exchange for cooperation, considering that both type of users have specific quality of service requirements. We investigate the problem of joint power and time allocation for the secondary access during the cooperative phase, with a view to optimizing the effective capacity of the primary user given an average energy constraint for the secondary user. Afterwards, the optimal power allocation of the secondary user for its own transmission phase is investigated in order to maximize the effective capacity of the secondary link. The proposed joint power and time allocation mechanism is compared with an optimal time/constant power allocation scheme and a less sophisticated baseline allocation scheme, i.e. power allocation under constant time and its superiority is proven for various network parameters. The reference model of one primary---one secondary user is extended to a general multi user cognitive radio network through the proposed pairing mechanism based on matching theory. Particularly, considering the remarks of the reference scenario, we propose two different matching schemes (with/without consideration of primary users' quality of service requirements) and we confirm their superiority compared to other matching mechanisms.

Cognitive radio methodologies have the potential to dramatically increase the throughput of wireless systems. Herein, control strategies which enable the superposition in time and frequency of primary and secondary user transmissions are explored in contrast to more traditional sensing approaches which only allow the secondary user to transmit when the primary user is idle. In this paper, the optimal transmission policy for the secondary user when the primary user adopts a retransmission-based error control scheme is investigated. The policy aims to maximize the secondary users' throughput, with a constraint on the throughput loss and failure probability of the primary user. Due to the constraint, the optimal policy is randomized, and determines how often the secondary user transmits according to the retransmission state of the packet being served by the primary user. The resulting optimal strategy of the secondary user is proven to have a unique structure. In particular, the optimal throughput is achieved by the secondary user by concentrating its transmission, and thus its interference to the primary user, in the first transmissions of a primary user packet. The rather simple framework considered in this paper highlights two fundamental aspects of cognitive networks that have not been covered so far: 1) the networking mechanisms implemented by the primary users (error control by means of retransmissions in the considered model) react to secondary users' activity; 2) if networking mechanisms are considered, then their state must be taken into account when optimizing secondary users' strategy, i.e., a strategy based on a binary active/idle perception of the primary users' state is suboptimal.

In a cognitive radio (CR) system, an achievable throughput of secondary users (SU) primarily depends on the activity level of primary users (PU) in the channels. More the PU are active in the channels, lesser the opportunity of SU transmission. Protecting the PU from a harmful interference introduced by SU is also a challenging task specifically when multiple PU and SU exist in the system. The trade-off between SU sum throughput and PU occupancy level has been thoroughly analysed considering a zero interference tolerance of an interweave CR system. PU activity level in the channel is measured in terms of duty cycle (DC). The interplay between DC of PU and throughput of SU is presented here for Poisson and binomial distribution based SU arrival processes. The results identify the opportunistic channels when SU aims a particular sum throughput and find the optimal numbers of SU in the channels for various DC.

In an opportunistic spectrum sharing (OSS) wireless network there are two types of users: primary users and secondary users. The primary users own the license for the system bandwidth, while the secondary users opportunistically share the spectrum resources with the primary users. When a secondary user detects a call arrival from a primary user in its current channel, the secondary user leaves the channel immediately and switches to an idle channel, if one is available, to continue the call. Otherwise, the secondary user is preempted. Call arrivals from primary users and secondary users in the OSS system are modeled by a Markovian arrival process (MAP) which captures correlation in the aggregate arrival process consisting of the two types of call arrivals. We derive the stationary probability vector using matrix-analytic methods and obtain expressions for a set of key performance measures. We present numerical results for a sample scenario.