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Bitcoin is a peer-to-peer digital, decentralized cryptocurrency created by an individual under pseudonym Satoshi Nakamoto. In fact, it is the first digital, decentralized currency. Several developers and organizations have explored the importance of digital cryptocurrency and the concept of the blockchain. Bitcoin is assumed to be one of the secure and comfortable payment methods that can be used in the upcoming days. The backbone of Bitcoin mining is the concept of the blockchain, which is assumed to beone of the ingenious invention of this century. The blockchain is the collection of blocks that are linked together in such a way that the hash of the previous block is contained in the present block. Any change of information in any blocks in a blockchain result in an error on the whole blockchain. Bitcoins are generated by a process called mining, where miners solve a complex mathematical puzzle. The miners are competing with each other to mine the Bitcoin as fast as possible and claim the reward. The mining of Bitcoin requires very high computation power. Since miners are solving the complex mathematical puzzle through hardware, they need to be fast in order to be the first solving the block. The miner who successfully solves the block gets rewarded with Bitcoin. Mining can be done by a single person, or it can be done by pool, where a bunch of miners combines in a network to mine a single block. Single mining, also referred to as solo mining is difficult since the difficulty of Bitcoin mining is increasing every day. Pool mining is another option for those who have fewer resources for mining. We propose an efficient way of mining Bitcoin by analyzing several results through self-experiment, online exchange market data, real-time Bitcoin block data, different mining pools' efficiency data and much more. Several factors are needed to be taken into consideration during mining because we may never mine a single Bitcoin even if we invest thousands of dollars on mining Bitcoin.

In long term evolution Advanced (LTE-A), concept of heterogeneous network (HetNet) has been introduced. Since, spectrum has become a rare resource these days; another mean is to be looked after to improve the existing wireless technology. One possible way is to improve the network topology so that frequency spectrum can be reused. In heterogeneous network, lower power nodes like Pico/Femto cell are deployed inside Macro cell to increase the system throughput and network coverage. Traditionally, cell selection for user equipment (UE) in LTE is based upon the received downlink power but these Pico/Femto cell have a low power than the Macro cell, meaning that few users can get access from Femto/Pico cells. UE should be close to the Pico/Femto cell to get connected with it. So its solution is; cell selection based upon the uplink path loss can be applied allowing more UE get connected to Pico/Femto cell. On doing that area of the Pico/Femto cell will increase which is called range extension region. Another problem arises when cell extension is applied, is that Macro cell imposes interference towards the Physical channel and signal of the Pico/Femto cell UE in range extension region as both Macro and Pico/Femto cell operate with same set of frequencies. 3rd Generation Partnership Project (3GPP) LTE-A Enhanced Inter-Cell Interference Coordination (eICIC) scheme has proposed Almost Blank Sub-frame (ABS) as a solution towards the above mentioned interference problem by reducing the activity or muting the Macro sub-frame. So that the corresponding Pico/Femto sub-frame can transmit the user information without interference from Macro cell ABS. For the reason of backward compatibility ABS still transmit certain physical channel and signals like CRSs, PCH, PBCH and PSS/SSS. So, interference still remains in these signals and channels. The main focus of thesis is reducing the impact of collision of cell-specific reference signal (CRS) from Macro and Femto cell as CRS is used for channel estimation. We have developed LTE link level system model for Macro and Femto cell in the Matlab simulator. Effect of difference in power of Macro and Femto CRS on UE under different noise power is investigated. It shows, higher the power of Macro, higher is the interference level. As a result Femto channel estimation quality degrades which in-turn degrades system performance. Combined receiver Interference cancelation (IC) methodology is implemented to reduce the impact of interference between macro and Femto CRS collision, which is based upon the reference signal received power (RSRP). System performance is evaluated with bit error rate (BER) and block error rate (BLER) versus Signal to Noise Ratio (SNR) and compared with the single cell system (without interference) and without IC system. Result confirms that IC method system performance is far better than system without IC and as close to system performance of single cell without interference. Furthermore, use of convolutional encoder, offer approximately 7dB coding gain in terms of SNR.