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Analog Communication Systems By P Chakrabarti.pdfl !EXCLUSIVE!



Our study helps clear up contradictions between past studies, in that different investigators had been studying the core, shell, and/or both without consistent recognition of the differences between them (Table 3). In the initial discovery of the budgerigar song system, Paton et al. [33] identified the posterior song pathway and called the nuclei by their songbird analogs HVC and RA, which we note in their figures appears to precisely correspond to our NLC core and AAC core respectively (Table 3). Paton et al. [33] also identified a large region that they named MAN similar to the comparable nucleus songbirds, which corresponds to our NAO core and shell. Striedter [32] renamed the posterior nuclei to NLC and AAC due to the belief that the parrot song system had evolved independently of songbirds, and expanded the regions to encompass most but not all of our shells. In the same study, Striedter [32] also identified our MO core (called HVo then) and NAO core, but did not consider them analogs of any songbird song nuclei, and instead considered a region he called NAs adjacent to the NAO core (part of our NAO shell) as the LMAN analog of songbirds (Table 3). Durand et al. [34] also identified the boundaries of NLC and AAC but did not distinguish the core and shell regions. Further, Durand claimed that NAom (a medial part of our NAO shell) adjacent to NAo (our core) is part of the parrot song system. Jarvis and Mello [24], using vocalizing-driven IEG expression, functionally identified the MO and NAO core and shells, calling them core and surround complexes, but did not recognize a core and shell for the NLC and AAC nuclei (Table 3). Since then, different studies have used either the Jarvis and Mello [24] or Durand et al. [34] naming systems without reconciling the two. In 2004, a new nomenclature for the avian brain was published that more closely matched their mammalian homologs [63,64]. The new nomenclature resulted in changes to the names (but not abbreviations, except for MO and MMSt), but it did not resolve the discrepancies. In the current study, authors from both groups reconciled the differences based on the findings of this study (Table 3).




Analog Communication Systems By P Chakrabarti.pdfl



The remarkable relative size differences between core and shell regions among parrot species could reflect functional and/or brain size differences. Parrot species generally considered to be more limited in their vocal imitation abilities, such as the budgerigar, peach-faced lovebirds, and cockatiels, have larger and conspicuous core nuclei relative to the shell nuclei, whereas those thought to have considerably more complex communication abilities such as the blue and gold macaws, peach-fronted conures, African Grey [15,17], yellow lored Amazon, and yellow crowned Amazon have noticeably smaller cores, and correspondingly larger shell regions. Consistent with this notion, recent behavioral studies by Walløe [72] have indicated that the peach-fronted conures show rapid vocal modification abilities compared to other species such as the budgerigars. We noted that except for the kea MO, the shapes of the song nuclei across species were very similar in serial sections, suggesting that shape differences are unlikely to confer differences in the relative sizes of the core and shell regions. Another possibility is that the differences in core and shell ratios are due to allometric scaling, where the shell could become disproportionally larger than the cores as the brain and body evolved to become larger. However, because we find that not all shell regions scale allometrically (i.e. NLC), the shell scaling of other song nuclei is negatively allometric, and the cores from the very same sections do not show significant scaling with brain section size, this suggests that the shell and core systems can be scaled somewhat independently of each other and from brain size. Further, allometric scaling for the shell also does not mean that larger brain regions (shells) are not involved in more complex aspects of vocal communication. The kea, also a larger brain parrot, does not show a robust shell specialization. Very little has been reported on kea vocal communication abilities, but one study has shown that there is geographic variation in their contact calls, and between juveniles and adults, indicating the presence of vocal learning [73]. Behavioral and/or neuroanatomical studies on most parrot species (with the exception of the budgerigar) including the endangered kea are few. Hence, additional experiments on volume measurements (beyond areas) with a larger number of animals from both sexes and from a wide variety of parrot species that differ in communication abilities would be necessary to further test these hypotheses on size differences.


In most species, the lowest core to shell area ratios (meaning shell is bigger) were in NLC. The NLC in budgerigars is required for production, but not memory of English words and natural vocalizations [74]. NLC lesions also disrupt the amplitude at which the carrier frequency of amplitude-modulated vocalizations are produced [74]; we note here that the lesions in that study included parts of both the NLC core and shell, making it difficult to pinpoint precise functional significance of the core and shell areas. Perhaps the strongest evidence regarding functional diversification of the NLC core and shell comes from a preliminary study by Striedter and Lei [75] where bilateral lesions of dorsal NLC (core in this study) and ventral NLC (shell in this study) were performed and learned contact calls examined. The authors found that NLC core lesions produced dramatic shifts in vocal frequency within a short period of time, with little recovery after lesions. However, NLC shell lesions produced a slow decrease in the size of the contact call repertoire. This suggests that further experiments are likely to discover different functions of the core and shell systems in parrot vocal communication.


NMMBCs work similar to the conventional wireless communication systems, but with the use of intelligent metasurfaces for recycling the energy dissipated in space that was conventionally thought to be useless and further improving SNR. Similar to conventional wireless communication systems, in NMMBC, an intended RF carrier carrying the information to be transferred is required for information transfer, where the signal modulation or demodulation is made by using nonlinear RF mixers. However, as opposed to the conventional wireless system, in NMMBC, the intelligent metasurface is deployed to shape the ambient environment such that the effective number of information channels can be increased (see Fig. 3a, b). In NMMBC, the intelligent metasurface is utilized to extend the aperture of the antenna in the conventional wireless communication systems in a distributed manner. In other words, the intelligent metasurface can be regarded as an extension part of antenna arrays of the conventional wireless communication systems, which is connected with the intended RF source using air rather than transmission lines [177].


Besides, the intelligent metasurface has several ubiquitous properties. First, the intelligent metasurface can be optimized to match any RF source and associated modules, since it improves the communication performance by tailoring the surrounding environment for all nearby devices instead of modifying the transmitting and receiving devices. Second, unlike the transmission lines in the conventional communication systems, the intelligent metasurface does not involve high-speed signals [177], and thus it can be easily incorporated into the ambient environment and remarkably improve SNR and thus the information capacity of the conventional systems. For instance, Tang et al. demonstrated theoretically that the intelligent metasurfaces were helpful in improving the energy efficiency of power allocation of the base station [177]. Hougne et al. demonstrated that the one-bit reconfigurable metasurface can be optimized to improve remarkably the equivalent number of channels of MIMO wireless communication systems [167]. More recently, in the community of wireless communication, the RIS has been numerically demonstrated to be helpful in enhancing the secure transfer [173, 174] (see Fig. 3c), reducing the mobile edge computing [175, 176] (see Fig. 3d), and so on. Overall, there are rapidly growing interests in this topic, and we would like to refer the readers of interest to Refs. [170,171,172] for more comprehensive reviews about recent progress.


Abstract:The traditional multiple input multiple output (MIMO) systems cannot provide very high Spectral Efficiency (SE), Energy Efficiency (EE), and link reliability, which are critical to guaranteeing the desired Quality of Experience (QoE) in 5G and beyond 5G wireless networks. To bridge this gap, ultra-dense cell-free massive MIMO (UD CF-mMIMO) systems are exploited to boost cell-edge performance and provide ultra-low latency in emerging wireless communication systems. This paper attempts to provide critical insights on high EE operation and power control schemes for maximizing the performance of UD CF-mMIMO systems. First, the recent advances in UD CF-mMIMO systems and the associated models are elaborated. The power consumption model, power consumption parts, and energy maximization techniques are discussed extensively. Further, the various power control optimization techniques are discussed comprehensively. Key findings from this study indicate an unprecedented growth in high-rate demands, leading to a significant increase in energy consumption. Additionally, substantial gains in EE require efficient utilization of optimal energy maximization techniques, green design, and dense deployment of massive antenna arrays. Overall, this review provides an elaborate discussion of the research gaps and proposes several research directions, critical challenges, and useful recommendations for future works in wireless communication systems.Keywords: cell-free massive mimo; ultra-dense CF-mMIMO; energy harvesting; energy efficiency; spectral efficiency; power control; sleep mode; resource allocation; sustainable wireless networks


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