Outage rates and outage durations of opportunistic relaying systems

Opportunistic relaying is a simple yet efficient cooperation scheme that achieves full diversity and preserves the spectral efficiency among the spatially distributed stations. However, the stations' mobility causes temporal correlation of the system's capacity outage events, which gives rise to its important second-order outage statistical parameters, such as the average outage rate (AOR) and the average outage duration (AOD). This letter presents exact analytical expressions for the AOR and the AOD of an opportunistic relaying system, which employs a mobile source and a mobile destination (without a direct path), and an arbitrary number of (fixed-gain amplify-and-forward or decode-and-forward) mobile relays in Rayleigh fading environment

Power Allocation and Cooperative Diversity in Two-Way Non-Regenerative Cognitive Radio Networks

In this paper, we investigate the performance of a dual-hop block fading cognitive radio network with underlay spectrum sharing over independent but not necessarily identically distributed (i.n.i.d.) Nakagami-$m$ fading channels. The primary network consists of a source and a destination. Depending on whether the secondary network which consists of two source nodes have a single relay for cooperation or multiple relays thereby employs opportunistic relay selection for cooperation and whether the two source nodes suffer from the primary users' (PU) interference, two cases are considered in this paper, which are referred to as Scenario (a) and Scenario (b), respectively. For the considered underlay spectrum sharing, the transmit power constraint of the proposed system is adjusted by interference limit on the primary network and the interference imposed by primary user (PU). The developed new analysis obtains new analytical results for the outage capacity (OC) and average symbol error probability (ASEP). In particular, for Scenario (a), tight lower bounds on the OC and ASEP of the secondary network are derived in closed-form. In addition, a closed from expression for the end-to-end OC of Scenario (a) is achieved. With regards to Scenario (b), a tight lower bound on the OC of the secondary network is derived in closed-form. All analytical results are corroborated using Monte Carlo simulation method

Separation Framework: An Enabler for Cooperative and D2D Communication for Future 5G Networks

Soaring capacity and coverage demands dictate that future cellular networks need to soon migrate towards ultra-dense networks. However, network densification comes with a host of challenges that include compromised energy efficiency, complex interference management, cumbersome mobility management, burdensome signaling overheads and higher backhaul costs. Interestingly, most of the problems, that beleaguer network densification, stem from legacy networks' one common feature i.e., tight coupling between the control and data planes regardless of their degree of heterogeneity and cell density. Consequently, in wake of 5G, control and data planes separation architecture (SARC) has recently been conceived as a promising paradigm that has potential to address most of aforementioned challenges. In this article, we review various proposals that have been presented in literature so far to enable SARC. More specifically, we analyze how and to what degree various SARC proposals address the four main challenges in network densification namely: energy efficiency, system level capacity maximization, interference management and mobility management. We then focus on two salient features of future cellular networks that have not yet been adapted in legacy networks at wide scale and thus remain a hallmark of 5G, i.e., coordinated multipoint (CoMP), and device-to-device (D2D) communications. After providing necessary background on CoMP and D2D, we analyze how SARC can particularly act as a major enabler for CoMP and D2D in context of 5G. This article thus serves as both a tutorial as well as an up to date survey on SARC, CoMP and D2D. Most importantly, the article provides an extensive outlook of challenges and opportunities that lie at the crossroads of these three mutually entangled emerging technologies.Comment: 28 pages, 11 figures, IEEE Communications Surveys & Tutorials 201

Network Coding for Distributed Antenna Systems

The mushroom growth of devices that require connectivity has led to an increase in the demand for spectrum resources as well as high data rates. 5G has introduced numerous solutions to counter both problems, which are inherently interconnected. Distributed antenna systems (DASs) help in expanding the coverage area of the network by reducing the distance between radio access unit (RAU) and the user equipment. DASs that use multiple-input multiple-output (MIMO) technology allow devices to operate using multiple antennas, which lead to spectrum efficiency. Recently, the concept of virtual MIMO (VMIMO) has gained popularity. VMIMO allows single antenna nodes to cooperate and form a cluster resulting in a transmission flow that corresponds to MIMO technology. In this chapter, we discuss MIMO-assisted DAS and its utility in forming a cooperative network between devices in proximity to enhance spectral efficiency. We further amalgamate VMIMO-assisted DAS and network coding (NC) to quantify end-to-end transmission success. NC is deemed to be particularly helpful in energy constrained environments, where the devices are powered by battery. We conclude by highlighting the utility of NC-based DAS for several applications that involve single antenna empowered sensors or devices

Smart Relay Selection Scheme Based on Fuzzy Logic with Optimal Power Allocation and Adaptive Data Rate Assignment

In this paper fuzzy logic-based algorithm with improved process of relay selection is presented which not only allocate optimal power for transmission but also help in choosing adaptive data rate. This algorithm utilizes channel gain, cooperative gain and signal to noise ratio with two cases considered in this paper: In case-I nodes do not have their geographical location information while in case-II nodes are having their geographical location information. From Monte Carlo simulations, it can be observed that both cases improve the selection process along with data rate assignment and power allocation, but case-II is the most reliable with almost zero probability of error at the cost of computational complexity which is 10 times more than case-I