Millimeter-wave (mmWave) communication and network densification hold great promise for achieving high-rate communication in next-generation wireless networks. Cloud radio access network (CRAN), in which low-complexity remote radio heads (RRHs) coordinated by a central unit (CU) are deployed to serve users in a distributed manner, is a cost-effective solution to achieve network densification. However, when operating over a large bandwidth in the mmWave frequencies, the digital fronthaul links in a CRAN would be easily saturated by the large amount of sampled and quantized signals to be transferred between the RRHs and the CU. To tackle this challenge, we propose in this paper a new architecture for the mmWave-based CRAN with advanced lens antenna arrays at the RRHs. Due to the energy focusing property, the lens antenna arrays are effective in exploiting the angular sparsity of mmWave channels, and thus help in substantially reducing the fronthaul rate and simplifying the signal processing at the multi-antenna RRHs and the CU, even when the channels are frequency-selective. We consider the uplink transmission in a mmWave CRAN with lens antenna arrays and propose a low-complexity quantization bit allocation scheme for multiple antennas at each RRH to meet the given fronthaul rate constraint. Furthermore, we propose a channel estimation technique that exploits the energy focusing property of the lens array and can be implemented at the CU with low complexity. Finally, we compare the proposed mmWave CRAN using lens antenna arrays with a conventional CRAN using uniform planar arrays at the RRHs, and show that the proposed design achieves significant throughput gains, yet with much lower complexity.

Millimeter-wave (mmWave) communication has emerged as a promising candidate for next-generation wireless systems due to the availability of large bandwidth, while cloud radio access network (CRAN), in which low-complexity remote radio heads (RRHs) are deployed to serve users in a distributed manner, is a cost-effective solution to achieve network densification. However, for a CRAN operating over a large bandwidth in the mmWave frequencies, the fronthaul links would be easily saturated by the large amount of sampled and quantized signals to be transferred between RRHs and the central processor. To tackle this problem, we propose in this paper a new architecture for mmWave CRAN with advanced lens antenna arrays at the RRHs. With the energy focusing property, lens antenna arrays are effective in exploiting the angular sparsity of the mmWave channel to simplify the signal processing required to achieve high data rates with multiple antennas, even when the channel is frequency selective. We consider the uplink transmission in a mmWave CRAN with lens antenna arrays and propose a low-complexity quantization bit allocation scheme for multiple antennas at each RRH to meet the stringent fronthaul rate constraint. We compare the proposed mmWave CRAN using lens antenna arrays, with a conventional CRAN using uniform planar arrays at the RRHs, and show that the proposed design achieves significant throughput gains with much lower complexity.

Ultra-dense (UD) wireless networks and cloud radio access networks (CRAN) are two promising network architectures for the emerging fifth-generation wireless communication systems. By jointly employing them, a new appealing network solution is proposed in this paper, termed UD-CRAN. In a UD-CRAN, millimeter-wave (mmWave) wireless fronthaul is preferred for information exchange between the central processor and the distributed remote radio heads (RRHs), due to its lower cost and higher flexibility in deployment, compared with fixed optical links. This motivates our study in this paper on the downlink transmission in a mmWave fronthaul enabled, orthogonal frequency division multiple access (OFDMA)-based UD-CRAN. In particular, the fronthaul is shared among the RRHs via time division multiple access (TDMA), while the RRHs jointly transmit to the users on orthogonal frequency sub-channels using OFDMA. The joint resource allocation over the TDMA-based mmWave fronthaul and OFDMA-based wireless transmission is investigated to maximize the weighted sum rate of all users. Although the problem is non-convex, we propose a Lagrange duality-based solution, which can be efficiently computed with good accuracy. To further reduce the complexity, we also propose a greedy search-based heuristic, which achieves close to optimal performance under practical setups. Finally, we show the significant throughput gains of the proposed joint resource allocation approach compared with other benchmark schemes by simulations.

Cloud radio access network (CRAN) is a promising candidate for the next-generation wireless communication systems. In CRAN, remote radio heads (RRHs) are deployed to serve users in a target area, which are connected to a central processor (CP) via limited-capacity links termed the fronthaul. In practice, the large amount of information transferred between each RRH and the CP for centralized processing can easily exceed its fronthaul capacity. This motivates our study in this paper on a new hybrid decoding scheme here the RRHs can either locally decode-and-forward the user messages to save the fronthaul capacity, or quantize and forward their signals to the CP for joint decoding as in the conventional CRAN (i.e., forward-and-decode). We consider the uplink transmission in an orthogonal frequency division multiplexing (OFDM)-based CRAN with multiple RRHs to serve a single user, where the proposed hybrid decoding is performed on each OFDM sub-channel (SC). We consider a joint optimization of the processing mode selections (decode-and-forward or forward-and-decode) along with the user's power allocation over all SCs to maximize the sum-rate for the user subject to the RRHs' individual fronthaul capacity constraints and the user's total power constraint. Although the problem is non-convex, we propose a Lagrange duality based solution, which can be efficiently computed with good accuracy. We also compare the performance of the proposed hybrid decoding with existing schemes that perform either decode-and-forward or forward-and-decode processing at all SCs, which shows promising throughput gains.

This paper considers the problem of receive antenna selection (AS) in a multiple-antenna communication system having a single radio-frequency (RF) chain. The AS decisions are based on noisy channel estimates obtained using known pilot symbols embedded in the data packets. The goal here is to minimize the average packet error rate (PER) by exploiting the known temporal correlation of the channel. As the underlying channels are only partially observed using the pilot symbols, the problem of AS for PER minimization is cast into a partially observable Markov decision process (POMDP) framework. Under mild assumptions, the optimality of a myopic policy is established for the two-state channel case. Moreover, two heuristic AS schemes are proposed based on a weighted combination of the estimated channel states on the different antennas. These schemes utilize the continuous-valued received pilot symbols to make the AS decisions, and are shown to offer performance comparable to the POMDP approach, which requires one to quantize the channel and observations to a finite set of states. The performance improvement offered by the POMDP solution and the proposed heuristic solutions relative to existing AS training-based approaches is illustrated using Monte Carlo simulations.

In this work, the problem of receive antenna selection (AS) is considered, in a multiple antenna communication system having a single radio frequency (RF) chain at the receiver. The AS is performed on a per-packet basis, and AS decisions are based on noisy estimates of the channel gains obtained using pilot symbols embedded in the data packet for coherent demodulation, along with the receiver's knowledge of the time correlation of the channel. The problem is posed as a partially observable Markov decision process (POMDP) with the goal of minimizing the average packet error rate (PER). The performance of a myopic policy is compared with that of the POMDP solution, and it is shown that the former is optimal under certain conditions. As the POMDP approach requires the channel gains to be quantized to a finite set of states, we also propose two heuristic AS schemes that use the continuous-valued received pilot symbols to make AS decisions, and thereby offer comparable or better performance than the POMDP approach. Unlike previous work, the schemes proposed here for AS do not require a lengthy AS training phase to precede each data packet. The performance improvement offered by the POMDP solution and the proposed heuristic solutions relative to existing AS training-based approaches is illustrated using Monte Carlo simulations.

This paper considers antenna selection (AS) for packet reception at a receiver equipped with multiple antenna elements but only a single radio frequency chain. The receiver makes its AS decisions based on noisy channel estimates obtained from the training symbols (pilots). The time-correlation of the wireless channel and the results of the link-layer error checks upon data packet reception provide additional information that can be exploited for AS. This information can also be used to optimally distribute pilots among the antenna elements, so that packet loss due to selection errors is minimized. The task of the receiver, then, is to sequentially select (a) the pilot symbol allocation for channel estimation on each of the receive antennas and (b) the antenna to be used for data packet reception. The goal is to maximize the expected throughput, based on the history of allocation and selection decisions, and the corresponding noisy channel estimates and error check observations. This joint problem of pilot allocation and AS is solved as a partially observed Markov decision problem (POMDP) and the solutions yield the optimal policies that maximize the long-term expected throughput. The performance of the POMDP solution is compared with several other schemes for a 2-state Markov channel model, and it is illustrated that it outperforms the others.

This paper considers antenna selection (AS) at a receiver equipped with multiple antenna elements but only a single radio frequency chain for packet reception. As information about the channel state is acquired using training symbols (pilots), the receiver makes its AS decisions based on noisy channel estimates. Additional information that can be exploited for AS includes the time-correlation of the wireless channel and the results of the link-layer error checks upon receiving the data packets. In this scenario, the task of the receiver is to sequentially select (a) the pilot symbol allocation, i.e., how to distribute the available pilot symbols among the antenna elements, for channel estimation on each of the receive antennas; and (b) the antenna to be used for data packet reception. The goal is to maximize the expected throughput, based on the past history of allocation and selection decisions, and the corresponding noisy channel estimates and error check results. Since the channel state is only partially observed through the noisy pilots and the error checks, the joint problem of pilot allocation and AS is modeled as a partially observed Markov decision process (POMDP). The solution to the POMDP yields the policy that maximizes the long-term expected throughput. Using the Finite State Markov Chain (FSMC) model for the wireless channel, the performance of the POMDP solution is compared with that of other existing schemes, and it is illustrated through numerical evaluation that the POMDP solution significantly outperforms them.