In this paper, we propose a novel coordinated multipoint (CoMP) downlink transmission strategy for the ultra-dense network (UDN) environment. In this proposed CoMP transmission scheme, we exploit the average received link power (ARLP) of the base stations (BSs) with respect to the ARLP of the strongest BS to a typical user, to dynamically adjust the number of cooperating BSs serving that user in the network. This allows us to effectively manage the inter-cell interference in the UDN regime. Our numerical results and simulations show that the proposed scheme can out-perform the existing fixed number of BS based CoMP transmission scheme in the high BS density scenario.

Timing advance (TA) estimation at the base station (BS) and reliable decoding of random access response (RAR) at the users are important steps in the initial random access (RA) procedure. However, due to limited availability of physical resources dedicated for RA, successful completion of RA requests would become difficult in high user density scenarios, due to contention among users requesting RA. In this paper, we propose to use the large antenna array at the massive multiple input multiple output BS to jointly group RA requests from different users using the same RA preamble. We then beamform the common RAR of each detected user group onto the same frequency resource, in such a way that most users in the group can reliably decode the RAR. The proposed RAR beamforming therefore automatically resolves the problem of collision between multiple RA requests on the same preamble, which reduces the RA latency significantly as compared to LTE. Analysis and simulations also reveal that for a fixed desired signal-to-interference-and-noise ratio of the received RAR, both the required per-user preamble transmission power and the total RAR beamforming power can be decreased roughly by 1.5 dB with every doubling in the number of BS antennas.

In this paper, we study the information theoretic performance of the modified time-reversal maximum ratio combining (TR-MRC) receiver (presented in [9]) with the spatially averaged periodogram-based carrier frequency offset (CFO) estimator (proposed in [7]) in multi-user massive MIMO systems. √ Our analysis shows that an ( ) array gain is achieved with this periodogram-based CFO estimator, which is same as the array gain achieved in the ideal/zero CFO scenario (M is the number of base station antennas). Information theoretic performance comparison with the correlation-based CFO estimator for massive MIMO systems (proposed in [6]) reveals that this periodogram-based CFO estimator is more energy efficient in slowly time-varying channels.

In this paper we consider a constant envelope pilot signal based carrier frequency offset (CFO) estimation in massive multiple-input multiple-output (MIMO) systems. The proposed algorithm performs spatial averaging on the periodogram of the received pilots across the base station (BS) antennas. Our study reveals that the proposed algorithm has complexity only linear in (the number of BS antennas). Further our analysis and numerical simulations also reveal that with fixed number of users and a fixed pilot length, the minimum required transmit pilot power decreases as 1/√M with increasing , while maintaining a fixed desired mean squared error (MSE) of CFO estimation.

Low-complexity carrier frequency offset (CFO) estimation and compensation in multi-user massive multiple-input multiple-output (MIMO) systems is a challenging problem. The existing CFO estimation algorithms incur tremendous increase in complexity with increasing number of base station (BS) antennas, M and number of user terminals (UTs) K (i.e. massive MIMO regime). In this paper, we address this problem by proposing a novel low-complexity algorithm for CFO estimation which uses the pilot signal received at the BS during special uplink slots. The total per-channel use complexity of the proposed algorithm increases only linearly with increasing M and is independent of K. Analysis reveals that the CFO estimation accuracy can be considerably improved by increasing M and K (i.e. massive MIMO regime). For example, for a fixed K and a fixed training length, the required per-user radiated power during uplink training decreases as 1/√M with increasing M.

We consider a single user (SU) massive MIMO system with multiple antennas at the transmitter (base station) and a single antenna at the user terminal (UT). Taking transceiver power consumption into consideration, for a given spectral efficiency (SE) we maximize the energy efficiency (EE) as a function of the number of base station (BS) antennasM, resulting in a closed-form expression for the optimal SE-EE trade-off. It is observed that in contrast to the classical SE-EE trade-off (which considers only the radiated power), with transceiver power consumption taken into account, the EE increases with increasing SE when SE is sufficiently small. Further, for a fixed SE we analyze the impact of varying cell size (i.e., equivalently average channel gain Gc) on the optimal EE. We show the interesting result that for sufficiently small Gc, the optimal EE decreases as O(√Gc) with decreasing Gc. Our analysis also reveals that for sufficiently small SE (or large Gc), the EE is insensitive to the power amplifier efficiency.

In this paper, a novel FM-chaos based noncoherent modulation technique is proposed. In this proposed Frequency-Modulated High Efficiency Differential Chaos Shift Keying (FM-HE-DCSK) with selective Orthogonalization scheme, superior qualities of HE-DCSK, like high data rate and bandwidth efficiency are preserved. The proposed scheme is also observed to provide improved BER performance, comparable to QCSK (Quadrature Chaos Shift Keying) and FM-QCSK, at lesser system complexity. A theoretical treatment on FM-chaos is developed to analyze the scheme and corresponding theoretical performance analysis is provided. Finally, it is shown that the proposed scheme outperforms UWB FM-DCSK in indoor UWB environment.