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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. [ABSTRACT FROM AUTHOR]

Massive multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) is a promising technology for next-generation wireless communications. However, when channel state information (CSI) at the transmitter is obtained using channel feedback, the benefits of this system are severely limited by the tradeoff between downlink capacity and feedback overhead. To solve this problem, we propose a time-domain differential feedback scheme for massive multiple-input single-output (MISO) OFDM systems. The proposed scheme exploits channel correlations in time, frequency, and space domains simultaneously by considering differential channel impulse response (CIR). To simplify the codebook design, and to reduce codeword-search complexity, we partition and then quantize the differential CIR using a number of subcodebooks. Because the total feedback bits are shared by the subcodebooks, we further optimize the bit allocation for them to minimize the total quantization error. For this, we analyze the quantization error of the proposed scheme and then use the analysis results for the bit-allocation optimization. In simulations, the proposed scheme efficiently exploits all correlations and achieves significant spectral-efficiency gain compared to conventional feedback schemes. [ABSTRACT FROM PUBLISHER]

This paper proposes a dynamic bound restriction iterative algorithm (DBRIA) framework, which can make iterative algorithms converge quickly. We apply this framework to realize computationally efficient iteration-based suboptimal bit and power allocation in OFDM systems. The implementation methods for three classes of suboptimal allocation algorithms are analyzed; margin-based, water-filling-based, and Lagrange-multiplier-based. We extend the water-filling-based power allocation algorithm to implement the bit allocation as well. Additional techniques for further reducing the computational complexity for all three allocation methods are also discussed. Simulation results show that both the maximum and the average computational complexity of all three allocation algorithms are reduced dramatically by applying the DBRIA and the additional techniques. For the water-filling-based algorithm, the allocation performance is also improved. [ABSTRACT FROM PUBLISHER]

We consider the problem of reliable communication over non-binary insertion/deletion channels where symbols are randomly deleted from or inserted in the received sequence and all symbols are corrupted by additive white Gaussian noise. To this end, we utilize the inherent redundancy achievable in non-binary symbol sets by first expanding the symbol set and then allocating part of the bits associated with each symbol to watermark symbols. The watermark sequence, known at the receiver, is then used by a forward-backward algorithm to provide soft information for an outer code which decodes the transmitted sequence. Through numerical results and discussions, we evaluate the performance of the proposed solution and show that it leads to significant system ability to detect and correct insertions/deletions. We also provide estimates of the maximum achievable information rates of the system, compare them with the available bounds, and construct practical codes capable of approaching these limits. [ABSTRACT FROM AUTHOR]

Coordinated multi-point (CoMP) transmission is a promising technique for improving spectral efficiency of full frequency reuse cellular systems. However, its performance will be severely degraded if the feedback strategies are not carefully designed for providing channel information through limited uplink resources to the coherently cooperated base stations. In this paper, we study per-cell codebook based limited feedback scheme, which is highly desirable for CoMP systems due to its scalability and flexibility. Specifically, we investigate the issues related to phase ambiguity (PA), which comes from the per-cell codebook structure. We first analyze how many bits are required for the PA feedback to ensure an allowed performance loss. We then propose an adaptive bit allocation algorithm to divide a given amount of feedback bits between the per-cell codebooks for single-cell channel direction quantization and a codebook for PA quantization, aiming at minimizing the average global channel direction quantization error. Finally we provide a scaling law of the number of feedback bits per-user when the bit allocation algorithm is used. Simulation results show that the proposed bit allocation algorithm significantly improves the throughput of coherent CoMP systems either with equal number or different number of antennas at each base station. [ABSTRACT FROM PUBLISHER]

By grouping a number of adjacent subcarriers into a chunk, resource allocation can be carried out chunk by chunk in orthogonal frequency division multiple access (OFDMA) systems. Chunk-based resource allocation is an effective approach to reduce the complexity of resource allocation in OFDMA systems. In this paper, a chunk-based resource allocation scheme, i.e. joint chunk, power and bit allocation, is proposed and analyzed by maximizing the throughput under a total transmit power constraint. A scaling factor is introduced to achieve optimal allocation. Considering the digital nature of bits per symbol per subcarrier (bits/symbol/subcarrier), a digitization process is proposed to digitize the theoretically allocated bits/symbol/subcarrier to integer. System parameters, such as the power constraint, number of users, coherence bandwidth, number of subcarriers and number of chunks, are introduced and their impacts on the average throughput are studied. The performance of the dynamic power allocation scheme is compared with the fixed power allocation scheme. The numerical results show that the theoretical throughput of the fixed power allocation scheme is quite close to that of the dynamic power allocation scheme. However, when the digital nature of bits/symbol/subcarrier is considered, the average throughput of the dynamic power allocation outperforms the fixed power allocation scheme. [ABSTRACT FROM AUTHOR]