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This paper investigates the effect of the path loss exponent $a$ on the Ergodic Capacity (EC) of the cellular systems. Previous studies examined this effect with certain values of $a$ with the association of single and multiple Remote Radio Heads (RRHs). In this work, a closed-form expression for the ergodic capacity is derived for the association with two RRHs using arbitrary values of path loss exponent and an ergodic capacity analysis for Nearest_RRH (NR_RRH) is also considered. Results reveal the relation between the channel capacity and $a$ at different levels of transmitted power for the uplink using multiple antennas at each RRH.

Radio spectrum for mobile operators is an expensive resource that should be wisely provisioned to meet users’ demands. In long term evolution-advanced, the number of physical resource blocks (PRBs) are used to gauge the users’ needs. Accordingly, there is a time in the network when it is beneficial to gain the advantages of consuming the unused PRBs in the transport block (TB) context to operate at a significantly lower power cost per PRB. The novelty of the letter is that the $3\text{rd}$ generation partnership project (3GPP) standard tables are used with multiple-input-multiple-output (MIMO) spatial multiplexing mode, to control the transport block size (TBS) index. This can be done by setting more PRBs and hence changing the modulation order that leads to reducing the transmitted power. In addition to that, MIMO layers, the prerequisite throughput, the intercell interference as well as the channel conditions of each user are considered as constraints to improve the downlink power efficiency. To describe the abovestated problem, an optimization problem is formulated as binary integer nonlinear program, which is very difficult to solve. Thus, a reformulation linearization technique based on a branch-and-bound framework is used to simplify the aforementioned BINLP.

The customer-installed femtocell is considered an efficient technique for solving the coverage and data rate problems inside buildings. In this letter, we introduce a component carrier (CC) group selection mechanism that helps the femtocells to find the optimal carrier aggregation (CA) for a long term evolution-advanced network, relies on the principle of distribution architecture. This mechanism is adopted from cellular manufacturing systems for the machine-part formation problem. By optimizing the CA selection, the proposed scheme can avoid the interference among femtocells and eNodeBs, maximize the system spectral efficiency, and enhance the coverage. A self-organization model for minimizing the variance between the CCs within the CA groups is introduced, where a novel interference threshold calculation is derived to bias the selection of the optimal group.

The decode-and-forward (DF) relay based cellular network is considered an efficient technique for solving the coverage and improving system capacity of the current fourth-generation (4G) and the emerging fifth-generation (5G) mobile telecommunication technologies. In this analysis, we control the codeword's transmission parameters by achieving a good trade-off between setting more resources with changing the modulation order, hence reducing the relay's access link transmitted power. We first address the calculations of codeword's size in 3GPP specification, where an unhealthy component carrier selection mechanism is introduced for defining relay's transmission spectrum. Then, we present a novel 3GPP-compliant binary integer optimization model subject to several practical constraints, such as the requisite resources and throughput. Through system level simulations, the proposed approach is compared with the conventional relaying systems. It is shown that our approach has made several improvements to the conventional schemes.

The radio spectrum for mobile operators is a precious resource that should be wisely provisioned to meet the users' demands and enhance the network performance. Currently, the machine- type communication (MTC) technique is becoming an important part of the evolving cellular network. This paper proposes an effective approach for MTC resource allocation, where the number of resources is used to gauge the MTC devices (MTCDs) transmitted power. Conceptually, the MTCDs elaborate and exchange a lightweight data-traffic and demanding fewer resources. Therefore, in this analysis, we control the index of the transport block size by setting more radio resources, hence reducing the modulation order with respect to the MTCDs' positions and the prerequisite data rate. In addition, we introduce a novel 3GPP-compliant binary-integer optimization model for ensuring an efficient trade-off between the index of the transport block size and the required number of resources for the downlink assignment problem. A major design challenge is to deal with the required throughput, the interference from neighboring cells, as well as the current channel conditions of each MTCD as a set of constraints for operating at a significantly lower power cost per resource. A series of comprehensive simulations is conducted. It is shown that the proposed approach has made several improvements to the conventional schemes.

In this paper, we introduce a new approach to weaken the effect of inter-cell codeword interference caused by two adjacent cells, which leads to a reduction in the communication performance at the cell-edge users. The objective is to create a new transmission procedure exchanged by the proximate base stations to ensure an efficient communication in the context of multiple-input-multiple-output (MIMO) transmissions. One of the most important issues is how the cell-edge users detect and decode the outcomes observed from the individual multi-streams of multi-codewords transmitted by two neighboring base stations. To address this problem, this paper presents a novel framework based on the network assisted interference cancellation and suppression (NAICS) technique. Its application to the scenario with two adjacent base stations and cellular-edge users is guided by two schemes. First, two base stations explicitly define and exchange the transmission policy for each codeword regarding the modulation index, the number of streams, and the order of the transmitted codewords. Second, a new taxonomy scheme is proposed, where the Bayesian decision classifier theory is used for the cell-edge users to extract the judgment rules from the exchanged transmission policy and enable symbols estimation and recognition in terms of posterior probability statements, hence offering a decision for each codeword. We introduce an integer optimization model subject to several practical constraints, such as the channel conditions of the users, the number of subcarriers, and the required throughput. The numerical tests demonstrate how to satisfy these requirements while validating the benefits over conventional approaches.