Adaptive Modulation in OSA-based Cognitive Radio Networks

Opportunistic spectrum access is based on channel state information and can lead to important performance improvements for the underlying communication systems. On the other hand adaptive modulation is also based on channel state information and can achieve increased transmission rates in fading channels. In this work we propose the combination of adaptive modulation with opportunistic spectrum access and we study the anticipated effects on the performance of wireless communication systems in terms of achieved spectral efficiency and power consumption.Comment: accepted conferenc

A Survey on MIMO Transmission with Discrete Input Signals: Technical Challenges, Advances, and Future Trends

Multiple antennas have been exploited for spatial multiplexing and diversity transmission in a wide range of communication applications. However, most of the advances in the design of high speed wireless multiple-input multiple output (MIMO) systems are based on information-theoretic principles that demonstrate how to efficiently transmit signals conforming to Gaussian distribution. Although the Gaussian signal is capacity-achieving, signals conforming to discrete constellations are transmitted in practical communication systems. As a result, this paper is motivated to provide a comprehensive overview on MIMO transmission design with discrete input signals. We first summarize the existing fundamental results for MIMO systems with discrete input signals. Then, focusing on the basic point-to-point MIMO systems, we examine transmission schemes based on three most important criteria for communication systems: the mutual information driven designs, the mean square error driven designs, and the diversity driven designs. Particularly, a unified framework which designs low complexity transmission schemes applicable to massive MIMO systems in upcoming 5G wireless networks is provided in the first time. Moreover, adaptive transmission designs which switch among these criteria based on the channel conditions to formulate the best transmission strategy are discussed. Then, we provide a survey of the transmission designs with discrete input signals for multiuser MIMO scenarios, including MIMO uplink transmission, MIMO downlink transmission, MIMO interference channel, and MIMO wiretap channel. Additionally, we discuss the transmission designs with discrete input signals for other systems using MIMO technology. Finally, technical challenges which remain unresolved at the time of writing are summarized and the future trends of transmission designs with discrete input signals are addressed.Comment: 110 pages, 512 references, submit to Proceedings of the IEE

Iterative Spectrum Shaping with Opportunistic Multiuser Detection

This paper studies a new decentralized resource allocation strategy, named iterative spectrum shaping (ISS), for the multi-carrier-based multiuser communication system, where two coexisting users independently and sequentially update transmit power allocations over parallel subcarriers to maximize their individual transmit rates. Unlike the conventional iterative water-filling (IWF) algorithm that applies the single-user detection (SD) at each user's receiver by treating the interference from the other user as additional noise, the proposed ISS algorithm applies multiuser detection techniques to decode both the desired user's and interference user's messages if it is feasible, thus termed as opportunistic multiuser detection (OMD). Two encoding methods are considered for ISS: One is carrier independent encoding where independent codewords are modulated by different subcarriers for which different decoding methods can be applied; the other is carrier joint encoding where a single codeword is modulated by all the subcarriers for which a single decoder is applied. For each encoding method, this paper presents the associated optimal user power and rate allocation strategy at each iteration of transmit adaptation. It is shown that under many circumstances the proposed ISS algorithm employing OMD is able to achieve substantial throughput gains over the conventional IWF algorithm employing SD for decentralized spectrum sharing. Applications of ISS in cognitive radio communication systems are also discussed.Comment: 7 figures, 24 page

Spectral Efficiency of Spectrum Pooling Systems

In this contribution, we investigate the idea of using cognitive radio to reuse locally unused spectrum to increase the total system capacity. We consider a multiband/wideband system in which the primary and cognitive users wish to communicate to different receivers, subject to mutual interference and assume that each user knows only his channel and the unused spectrum through adequate sensing. The basic idea under the proposed scheme is based on the notion of spectrum pooling. The idea is quite simple: a cognitive radio will listen to the channel and, if sensed idle, will transmit during the voids. It turns out that, although its simplicity, the proposed scheme showed very interesting features with respect to the spectral efficiency and the maximum number of possible pairwise cognitive communications. We impose the constraint that users successively transmit over available bands through selfish water filling. For the first time, our study has quantified the asymptotic (with respect to the band) achievable gain of using spectrum pooling in terms of spectral efficiency compared to classical radio systems. We then derive the total spectral efficiency as well as the maximum number of possible pairwise communications of such a spectrum pooling system.Comment: 24 pages, 8 figure

Stability Analysis of Slotted Aloha with Opportunistic RF Energy Harvesting

Energy harvesting (EH) is a promising technology for realizing energy efficient wireless networks. In this paper, we utilize the ambient RF energy, particularly interference from neighboring transmissions, to replenish the batteries of the EH enabled nodes. However, RF energy harvesting imposes new challenges into the analysis of wireless networks. Our objective in this work is to investigate the performance of a slotted Aloha random access wireless network consisting of two types of nodes, namely Type I which has unlimited energy supply and Type II which is solely powered by an RF energy harvesting circuit. The transmissions of a Type I node are recycled by a Type II node to replenish its battery. We characterize an inner bound on the stable throughput region under half-duplex and full-duplex energy harvesting paradigms as well as for the finite capacity battery case. We present numerical results that validate our analytical results, and demonstrate their utility for the analysis of the exact system

Tag Spotting at the Interference Range

In wireless networks, the presence of interference among wireless links in- troduces dependencies among flows that do not share a single link or node. As a result, when designing a resource allocation scheme, be it a medium access scheduler or a flow rate controller, one needs to consider the interdependence among nodes within interference range of each other. Specifically, control plane information needs to reach nearby nodes which often lie outside the communi- cation range, but within the interference range of a node of interest. But how can one communicate control plane information well beyond the existing communication range? To address this fundamental need we introduce tag spotting. Tag spotting refers to a communication system which allows re- liable control data transmission at SNR values as low as 0 dB. It does this by employing a number of signal encoding techniques including adding redundancy to multitone modulation, shaping the spectrum to reduce inter-carrier interfer- ence, and the use of algebraic coding. Making use of a detection theory-based model we analyze the performance achievable by our modulation as well as the trade-off between the rate of the information transmitted and the likelihood of error. Using real-world experiments on an OFDM system built with software radios, we show that we can transmit data at the target SNR value of 0 dB with a 6% overhead; that is, 6% of our packet is used for our low-SNR decodable tags (which carry up to a couple of bytes of data in our testbed), while the remain- ing 94% is used for traditional header and payload data. We also demonstrate via simulations how tag spotting can be used in implementing fair and efficient rate control and scheduling schemes.Comment: 30 page

On the Ergodic Rate of OFDMA Cognitive Radios under Imperfect Cross-Link Information

The ergodic rate performance and limits of orthogonal frequency-division multiple access (OFDMA) cognitive radios (CRs) is studied under imperfect cross-link knowledge. We propose a novel stochastic interference management and exploitation technique to mitigate and control the imposed interference by CRs on licensed users in underlay spectrum sharing. The optimum downlink channel-adaptive resource allocation (RA) algorithm is designed to maximize the CRs functionality subject to the satisfying average transmit and probabilistic peak interference power constraints. An expression of the cumulative density function (cdf) of CRs' received signal-to-interference-plus-noise ratio (SINR) is developed to evaluate the resultant ergodic rate. Simulation studies are conducted to examine the proposed RA algorithm and investigate the impact of parameters uncertainty on the overall system performance.Comment: arXiv admin note: text overlap with arXiv:1710.0827

Multiuser Random Beamforming in Millimetre-Waves Channels

This thesis aims to show that in mmWaves channels, schemes based on randomly-directional beamforming allow to harness both the spatial multiplexing and multi-user diversity characterizing the broadcast channel by using only limited feedback and a simple transmitter architecture. The number of necessary users with respect to the number of transmitting antennas for optimal performances is investigated as well as the fairness issue, for which the use of NOMA is shown to be advantageous w.r.t. OMA

Delay Optimal Multichannel Opportunistic Access

The problem of minimizing queueing delay of opportunistic access of multiple continuous time Markov channels is considered. A new access policy based on myopic sensing and adaptive transmission (MS-AT) is proposed. Under the framework of risk sensitive constrained Markov decision process with effective bandwidth as a measure of queueing delay, it is shown that MS-AT achieves simultaneously throughput and delay optimality. It is shown further that both the effective bandwidth and the throughput of MS-AT are two-segment piece-wise linear functions of the collision constraint (maximum allowable conditional collision probability) with the effective bandwidth and throughput coinciding in the regime of tight collision constraints. Analytical and simulations comparisons with the myopic sensing and memoryless transmission (MS-MT) policy which is throughput optimal but delay suboptimal in the regime of tight collision constraints.Comment: 9 pages, 6 figures, submitted to INFOCOM 201

Joint relay scheduling, channel access, and power allocation for green cognitive radio communications

PublishedJournal Article© 1983-2012 IEEE. The capacity of cognitive radio (CR) systems can be enhanced significantly by deploying relay nodes to exploit the spatial diversity. However, the inevitable imperfect sensing in CR has vital effects on the policy of relay selection, channel access, and power allocation that play pivotal roles in the system capacity. The increase in transmission power can improve the system capacity, but results in high energy consumption, which incurs the increase of carbon emission and network operational cost. Most of the existing schemes for CR systems have not jointly considered the imperfect sensing scenario and the tradeoff between the system capacity and energy consumption. To fill in this gap, this paper proposes an energy-aware centralized relay selection scheme that takes into account the relay selection, channel access, and power allocation jointly in CR with imperfect sensing. Specifically, the CR system is formulated as a partially observable Markov decision process (POMDP) to achieve the goal of balancing the system capacity and energy consumption as well as maximizing the system reward. The optimal policy for relay selection, channel access, and power allocation is then derived by virtue of a dynamic programming approach. A dimension reduction strategy is further applied to reduce its high computation complexity. Extensive simulation experiments and results are presented and analysed to demonstrate the significant performance improvement compared to the existing schemes. The performance results show that the received reward increases more than 50% and the network lifetime increases more than 35%, but the system capacity is reduced less than 6% only.This work was supported by the National Natural Science Foundation of China under Grants 61201219, 61171111, 61472150, and 61173045 and in part by the Fundamental Research Funds for the Central Universities under Grant 2013QN122