Article Preview
TopIntroduction
Most of the applications of modern wireless communication systems are best suited to fulfil the requirements of higher bandwidth, spectrum efficiency and better quality of service (QoS) that drives cellular wireless communication to its new evolution of the fifth-generation (5G) technology. The real existence of 5G technology expected to be available by 2020-21, has already generated tremendous expectations all across the world (Hammoodi et al., 2019). The 5G techniques are projected to exceed the performance of Long Term Evolution-Advanced (LTE-A) systems and to provide network latency up to 1 ms, data transmission rate up to 20 Gb/s, number of serviced devices up to 1 million per square kilometer etc. among a few (Bochechka et al., 2017). With such specific attributes, the 5G networks are expected to facilitate a wide range of applications covering gigabit wireless connectivity, Internet of Things (IoT), machine-type communication (MTC), tactile Internet etc. (Bochechka et al., 2017). For the above, source and channel coding, antenna deployment strategies, modulation schemes etc. hold the key.
In general, source and channel coding operations performed individually in wireless communication systems, play a significant role in data transmission (Wu et al., 2009). In source coding, the compressed representation of the source is accomplished by way of the elimination of superfluous bits to acquire a systematic representation of the data. Likewise, in channel coding, some redundant bits are appended with the real data to combat noise to accomplish the effective QoS. The use of separate source and channel coding (SSCC) techniques leading to the increase in system complexity is reported in (Gao & Tuncel, 2013; Tian et al., 2013). This complexity can be eliminated by using a joint source and channel coding (JSCC) (Dutch et al., 2005; Deka & Sarma, 2018). There is a crucial need to attain a more effective data depiction through the mitigation of superfluity and lowering of the computational intricacies at the same time. This has been achieved by using JSCC. JSCC has received attention because of its ability to enhance reception quality, better link reliability, reducing redundancy and lowering computational complexity. In this backdrop, the use of fountain code has been proven to be a promising technique. The combination of JSCC with fountain coding provides a near-optimal capacity approach with a less bit error rate (BER) (Deka & Sarma, 2017).
Multiple-input multiple-output (MIMO) has been used extensively in 4G and continues to be a favored option in 5G but with variations. Orthogonal Frequency Division Multiplexing (OFDM) is an attractive technology that is commonly used in broadband wireless systems in 4G and is expected to be a key in 5G systems as well (Ma, 2017). OFDM ensures high spectral efficiency, robustness to channel delays, single-tap frequency domain equalization, reducing inter-symbol interference (ISI), inter-carrier interference (ICI) using a cyclic prefix (CP) and efficient utilization of bandwidth though problems arise due to improper synchronization.
Figure 1. Spectrum utilization of an OFDM based LTE system at 20 MHz channel bandwidth
But OFDM lowers spectral efficiency and enhances distortion (shown in Figure 1). Therefore, some other spectrum efficient modulation techniques have been considered to cover the deficiencies of OFDM (Jaradat et al., 2019).
Of late, due to the promising advantages of expanding the available capacity and better QoS, multi-carrier code-division multiple access (MC-CDMA) has been accepted as a reliable approach. It works by combining CDMA and OFDM and is found to solve the frequency selectivity, inter-symbol interference (ISI) problems. In MC-CDMA, parallel sub-channels are attainable through the division of transmission spectrum using multiple carriers, yielding narrow bandwidth slots for data propagation to make them frequency flat. Thus, by insertion of a proper length CP, the ISI can be lowered in MC-CDMA so that a simplified equalization is achieved (Adachi et al., 2008; Deka & Sarma, 2019).