The popularity of cellular internet of things (IoT) is increasing day by day and billions of IoT devices will be connected to the internet. Many of these devices have limited battery life with constraints on transmit power. High user power consumption in cellular networks restricts the deployment of many IoT devices in 5G. To enable the inclusion of these devices, 5G should be supplemented with strategies and schemes to reduce user power consumption. Therefore, we present a novel joint uplink user association and resource allocation scheme for minimizing user transmit power while meeting the quality of service. We analyze our scheme for two-tier heterogeneous network (HetNet) and show an average transmit power of -2.8 dBm and 8.2 dBm for our algorithms compared to 20 dBm in state-of-the-art Max reference signal received power (RSRP) and channel individual offset (CIO) based association schemes.

In this article, we simulate a converged 5G mm-wave analogue radio-over-fiber (A-RoF) system at 60 GHz, and perform offline signal processing to equalize the dispersive optical link with the three most frequently employed algorithms, i.e., the simple least mean square (LMS) algorithm, the constant modulus algorithm (CMA) and the adaptive median filtering (AMF), which are implemented in Matlab. The performances of the different algorithms are compared for various optical fiber lengths with respect to the EVM values obtained before and after equalization. In the case of QPSK in OFDM subcarriers, it is observed that the CMA algorithm performs better than the LMS and MF algorithms, with 2% and 1.4% EVM improvement respectively, while for 16QAM in OFDM subcarriers it is observed that the LMS algorithm has a very small improvement of 0.2% EVM compared to the MF algorithm, while CMA is not suitable for 16QAM modulation in the proposed converged 5G mm-wave A-RoF system at 60 GHz.

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Despite the best efforts of networking researchers and practitioners, an ideal Internet experience is inaccessible to an overwhelming majority of people the world over, mainly due to the lack of cost-efficient ways of provisioning high-performance, global Internet. In this paper, we argue that instead of an exclusive focus on a utopian goal of universally accessible "ideal networking" (in which we have a high throughput and quality of service as well as low latency and congestion), we should consider providing "approximate networking" through the adoption of context-appropriate trade-offs. In this regard, we propose to leverage the advances in the emerging trend of "approximate computing" that rely on relaxing the bounds of precise/exact computing to provide new opportunities for improving the area, power, and performance efficiency of systems by orders of magnitude by embracing output errors in resilient applications. Furthermore, we propose to extend the dimensions of approximate computing towards various knobs available at network layers. Approximate networking can be used to provision "Global Access to the Internet for All" (GAIA) in a pragmatically tiered fashion, in which different users around the world are provided a different context-appropriate (but still contextually functional) Internet experience.