Adaptive Pilot Patterns for CA-OFDM Systems in Nonstationary Wireless Channels

Abstract

Abstract-In this paper, we investigate the performance gains of adapting pilot spacing and power for Carrier Aggregation (CA)-OFDM systems in nonstationary wireless channels. In current multi-band CA-OFDM wireless networks, all component carriers use the same pilot density, which is designed for poor channel environments. This leads to unnecessary pilot overhead in good channel conditions and performance degradation in the worst channel conditions. We propose adaptation of pilot spacing and power using a codebook-based approach, where the transmitter and receiver exchange information about the fading characteristics of the channel over a short period of time, which are stored as entries in a channel profile codebook. We present a heuristic algorithm that maximizes the achievable rate by finding the optimal pilot spacing and power, from a set of candidate pilot configurations. We also analyze the computational complexity of our proposed algorithm and the feedback overhead. We describe methods to minimize the computation and feedback requirements for our algorithm in multi-band CA scenarios and present simulation results in typical terrestrial and air-toground/air-to-air nonstationary channels. Our results show that significant performance gains can be achieved when adopting adaptive pilot spacing and power allocation in nonstationary channels. We also discuss important practical considerations and provide guidelines to implement adaptive pilot spacing in CA-OFDM systems.

Probability of Pilot Interference in Pulsed Radar-Cellular Coexistence: Fundamental Insights on Demodulation and Limited CSI Feedback

“…It is well known that corrupted I-CSI is detrimental to coherent demodulation [10]. Therefore, 2) Absence of Radar-Cellular Cooperation: In fading channels with slowly varying channel statistics, throughput can be enhanced by adapting the pilot spacing in time and frequency in real-time, as a function of the channel conditions [14]. In addition, we propose minimizing…”

Rate-Maximizing OFDM Pilot Patterns for UAV Communications in Nonstationary A2G Channels

“…where σ 2 d (σ 2 p ) is the average power per data (pilot) symbol, σ 2 w the noise power and σ ZF = 1 for a M ×M -MIMO system [4]. The channel estimation mean squared error (MSE) is given by σ 2 M SE , which can be computed using the expressions derived in [4], [5] 1 . It is important to note that σ 2 M SE is a function of the channel's temporal correlation R t (∆t), spectral correlation R f (∆f ) and the average pilot SNR (σ 2 p /σ 2 w ).…”

“…3: For all allowed values of V = {ρ, ∆pf, ∆pt} ∈ {P, D f , Dt}, compute σ 2 M SE (see [5]). 4: For all allowed values of V, solve equation (5) to obtain the optimal parameters Vo = {ρo, (∆pf )o, (∆pt)o}. 5: Feed back the optimal parameter set Vo.…”

“…The spectrum utilization function S(∆ p f, ∆ p t) is simply the fraction of data REs in the OFDM grid across all layers over the total number of REs, and can be easily computed with the knowledge of V. Additional control channels and signals are ignored here without loss of generality 1. The channel estimation MSE has been derived for 'diamond-shaped' pilot patterns in[4],[5]. Note that this pattern is used in modern cellular standards such as LTE and NR.…”

Adaptive Pilot Pattern for Massive MIMO Systems

“…The second ax is the pilot arrangement which refers to arranging a pilot sequence of a given user in the available frequency, time, and space resources. It consists of designing a pilot pattern to either enhance the spectral efficiency [16] - [17] or improve the system's reliability [18] - [20]. The final ax is the channel estimation which refers to investigating the channel coefficients detection based on the received pilot signal.…”