Recently, small cell systems such as femto cell are being considered as a good alternative that can support the increasing demand for mobile data traffic because they can significantly enhance network capacity by increasing spatial reuse. In this paper, we analyze the coverage and capacity of a femto cell when it is deployed in a hotspot to reduce the traffic loads of neighboring macro base stations (BSs). Our analysis results show that the coverage and capacity of femto cell are seriously affected by surrounding signal environment and they can be greatly enhanced by adapting power allocation for channels to the surrounding environment. Thus, we propose an adaptive power partitioning scheme where power allocation for channels can be dynamically adjusted to suit the environment surrounding the femto cell. In addition, we numerically derive the optimal power allocation ratio for channels to optimize the performance of the femto cell in the proposed scheme. It is shown that the proposed scheme with the optimal channel power allocation significantly outperforms the conventional scheme with fixed power allocation for channels.

In this paper, we investigate a user and antenna joint selection problem in multi-user large-scale MIMO downlink networks, where a BS with N transmit antennas serves K users, and N is much larger than K. The BS activates only S(S≤N) antennas for data transmission to reduce hardware cost and computation complexity, and selects the set of users to which data is to be transmitted by maximizing the sum-rate. The optimal user and antenna joint selection scheme based on exhaustive search causes considerable computation complexity. Thus, we propose a new joint selection algorithm with low complexity and analyze the performance of the proposed scheme in terms of sum-rate and complexity. When S=7, N=10, K=5, and SNR=10dB, the sum-rate of the proposed scheme is 5.1% lower than that of the optimal scheme, while the computation complexity of the proposed scheme is reduced by 99.0% compared to that of the optimal scheme.

In this letter, a novel antenna selection (AS) technique is proposed for the downlink of large-scale multi-user multiple input multiple output (MU-MIMO) networks, where a base station (BS) is equipped with large-scale antennas (N) and communicates simultaneously with K(K ≪ N) mobile stations (MSs). In the proposed scheme, the S antennas (S ≤ N) are selected by utilizing the concept of a sliding window. It is shown that the sum-rate of our proposed scheme is comparable to that of the conventional scheme, while the proposed scheme can significantly reduce the complexity of the BS.

In this paper, a practical antenna selection (AS) scheme is investigated for downlink multiuser massive multiple input multiple output (MIMO) networks where a base station (BS) is equipped with many antennas (N) and communicates with K mobile stations (MSs) simultaneously. In the proposed antenna selection technique, S antennas (S≤N) are selected for transmission based on the knowledge of channel coefficients of each MS for reducing the number of RF chains which mainly induce cost increase in terms of size, hardware, and power. In the proposed AS technique, a BS first ranks antenna elements according to the sum of their channel gains to all MSs. Then, the BS computes the downlink sum-rate with S consecutive antenna elements in the ordered set, where the subset consisting of S consecutive antennas is called a window. The BS selects the window resulting in the highest sum-rate. The selected S antenna elements are used for transmitting signals to multiple users, while the remaining (N-S) antenna elements are turned off for the time slot. Therefore, the proposed AS technique requires only (N-S+1) sum-rate computations, while the optimal AS technique involves $inom{N}{S}$ computations. We analyze downlink sum-rate with the proposed AS technique and compare it with that of a reference system with the same number of antenna elements without AS. Our results show that the proposed AS technique significantly outperforms the reference scheme.

We consider a device-to-device (D2D) underlaid cellular network where D2D communications are allowed to share the same radio spectrum with cellular uplink communications for improving spectral efficiency. However, to protect the cellular uplink communications, the interference level received at a base station (BS) from the D2D communications needs to be carefully maintained below a certain threshold, and thus the BS coordinates the transmit power of the D2D links. In this paper, we investigate on-off power control for the D2D links, which is known as a simple but effective technique due to its low signaling overhead. We first investigate the optimal on-off power control algorithm to maximize the sum-rate of the D2D links, while satisfying the interference constraint imposed by the BS. The computational complexity of the optimal algorithm drastically increases with D2D link number. Thus, we also propose an on-off power control algorithm to significantly reduce the computational complexity, compared to the optimal on-off power control algorithm. Extensive simulations validate that the proposed algorithm significantly reduces the computational complexity with a marginal sum-rate offset from the optimal algorithm.