Wireless cellular networks feature two emerging technological trends. The first is the direct Device-to-Device (D2D) communications, which enables direct links between the wireless devices that reutilize the cellular spectrum and radio interface. The second is that of Machine-Type Communications (MTC), where the objective is to attach a large number of low-rate low-power devices, termed Machine-Type Devices (MTDs) to the cellular network. MTDs pose new challenges to the cellular network, one if which is that the low transmission power can lead to outage problems for the cell-edge devices. Another issue imminent to MTC is the that can lead to overload of the radio interface. In this paper we explore the opportunity opened by D2D links for supporting MTDs, since it can be desirable to carry the MTC traffic not through direct links to a Base Station, but through a nearby relay. MTC is modeled as a fixed-rate traffic with an outage requirement. We propose two network-assisted D2D schemes that enable the cooperation between MTDs and standard cellular devices, thereby meeting the MTC outage requirements while maximizing the rate of the broadband services for the other devices. The proposed schemes apply the principles Opportunistic Interference Cancellation and the Cognitive Radio's underlaying. We show through analysis and numerical results the gains of the proposed schemes.
To the best of our knowledge, the MTC setting has not been considered within the context of D2D, underlaying and cognitive radio. The nearest match within cognitive radio can be found on @cite_51 although there OIC was not considered. We note that interference cancellation schemes have recently stirred increased attention both in academia @cite_47 @cite_5 and industry @cite_29 @cite_46 .
@cite_47 @cite_29 @cite_5 @cite_46 @cite_51
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Interference plays a crucial role for performance degradation in communication networks nowadays. An appealing approach to interference avoidance is the Interference Cancellation (IC) methodology. Particularly, the Successive IC (SIC) method represents the most effective IC-based reception technique in terms of Bit-Error-Rate (BER) performance and, thus, yielding to the overall system robustness. Moreover, SIC in conjunction with Orthogonal Frequency Division Multiplexing (OFDM), in the context of SIC-OFDM, is shown to approach the Shannon capacity when single-antenna infrastructures are applied while this capacity limit can be further extended with the aid of multiple antennas. Recently, SIC-based reception has studied for Orthogonal Frequency and Code Division Multiplexing or (spread-OFDM systems), namely OFCDM. Such systems provide extremely high error resilience and robustness, especially in multi-user environments. In this paper, we present a comprehensive survey on the performance of SIC for single- and multiple-antenna OFDM and spread OFDM (OFCDM) systems. Thereby, we focus on all the possible OFDM formats that have been developed so far. We study the performance of SIC by examining closely two major aspects, namely the BER performance and the computational complexity of the reception process, thus striving for the provision and optimization of SIC. Our main objective is to point out the state-of-the-art on research activity for SIC-OF(C)DM systems, applied on a variety of well-known network implementations, such as cellular, ad hoc and infrastructure-based platforms. Furthermore, we introduce a Performance-Complexity Tradeoff (PCT) in order to indicate the contribution of the approaches studied in this paper. Finally, we provide analytical performance comparison tables regarding to the surveyed techniques with respect to the PCT level. Techniques to process a number of “received” symbol streams in a Multiple-Input Multiple-Output (MIMO) system with multipath channels such that improved performance may be achieved when using successive interference cancellation (SIC) processing. In an aspect, metrics indicative of the quality or “goodness” of a “detected” symbol stream are provided. These metrics consider the frequency selective response of the multipath channels used to transmit the symbol stream. For example, the metrics may relate to (1) an overall channel capacity for all transmission channels used for the symbol stream, or (2) an equivalent signal-to-interference noise ratio (SNR) of an Additive White Gaussian Noise (AWGN) channel modeling these transmission channels. In another aspect, techniques are provided to process the received symbol streams, using SIC processing, to recover a number of transmitted symbol streams. The particular order in which the symbol streams are recovered is determined based on the metrics determined for symbol streams detected at each SIC processing stage. In this paper, we address the problem of computing the probability that r out of n interfering wireless signals are "captured," i.e., received with sufficiently large Signal to Interference plus Noise Ratio (SINR) to correctly decode the signals by a receiver with multi-packet reception (MPR) and Successive Interference Cancellation (SIC) capabilities. We start by considering the simpler case of a pure MPR system without SIC, for which we provide an expression for the distribution of the number of captured packets, whose computational complexity scales with n and r. This analysis makes it possible to investigate the system throughput as a function of the MPR capabilities of the receiver. We then generalize the analysis to SIC systems. In addition to the exact expressions for the capture probability and the normalized system throughput, we also derive approximate expressions that are much easier to compute and provide accurate results in some practical scenarios. Finally, we present selected results for some case studies with the purpose of illustrating the potential of the proposed mathematical framework and validating the approximate methods. In an ad hoc peer-to-peer communication network between wireless devices, a low priory first transmitter device adjusts it transmit power based on a received transmission request response from a higher priority second receiver device. The first transmitter device broadcasts a first transmission request to a corresponding first receiver device and may receive a first transmission request response from a different second receiver device. The second transmission request response is sent by the second receiver device in response to a second transmission request from a second transmitter device. The first transmitter device calculates an interference cost to the second receiver device as a function of the received power of the first transmission request response. A transmission power is obtained as a function of the calculated interference cost and the transmission power of the first transmission request, the transmission power to be used for traffic transmissions corresponding to the first transmission request. Spectrum sharing between wireless networks improves the efficiency of spectrum usage, and thereby alleviates spectrum scarcity due to growing demands for wireless broadband access. To improve the usual underutilization of the cellular uplink spectrum, this paper addresses spectrum sharing between a cellular uplink and a mobile ad hoc networks. These networks access either all frequency subchannels or their disjoint subsets, called spectrum underlay and spectrum overlay, respectively. Given these spectrum sharing methods, the capacity trade-off between the coexisting networks is analyzed based on the transmission capacity of a network with Poisson distributed transmitters. This metric is defined as the maximum density of transmitters subject to an outage constraint for a given signal-to-interference ratio (SIR). Using tools from stochastic geometry, the transmission-capacity trade-off between the coexisting networks is analyzed, where both spectrum overlay and underlay as well as successive interference cancellation (SIC) are considered. In particular, for small target outage probability, the transmission capacities of the coexisting networks are proved to satisfy a linear equation, whose coefficients depend on the spectrum sharing method and whether SIC is applied. This linear equation shows that spectrum overlay is more efficient than spectrum underlay. Furthermore, this result also provides insight into the effects of network parameters on transmission capacities, including link diversity gains, transmission distances, and the base station density. In particular, SIC is shown to increase the transmission capacities of both coexisting networks by a linear factor, which depends on the interference-power threshold for qualifying canceled interferers.